Skip to main content
Help us understand how you use our websites. Take part in our 30 minute study now.

Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas


Recent advancements in computational power, machine learning, and artificial intelligence technology have enabled automated evaluation of medical images to generate quantitative diagnostic and prognostic biomarkers. Such objective biomarkers are readily available and have the potential to improve personalized treatment, precision medicine, and patient selection for clinical trials. In this article, we explore the merits of the most recent addition to the “-omics” concept for the broader field of head and neck cancer – “Radiomics”. This review discusses radiomics studies focused on (molecular) characterization, classification, prognostication and treatment guidance for head and neck squamous cell carcinomas (HNSCC). We review the underlying hypothesis, general concept and typical workflow of radiomic analysis, and elaborate on current and future challenges to be addressed before routine clinical application.


Structural and functional imaging provide data that are integral for diagnosis, treatment response evaluation, and surveillance in patients with head and neck squamous cell carcinoma (HNSCC). The large amount of volumetric bioimaging information amassed in institutional archives constitutes an extensive database amenable to high-throughput, quantitative image analysis. Radiomics refers to automated extraction of high-dimensional sets of quantitative descriptors (“radiomic features”) from medical images (e.g. CT, MRI, PET etc.) for development of novel diagnostic and prognostic biomarkers. Machine learning (ML) algorithms and artificial intelligence (AI) are best suited for analysis of radiomics high-dimensional data. Radiomics provides fast, low-cost and non-invasive, yet comprehensive tissue and organ characterization, as features are extracted directly from (pre-processed) standard-of-care medical images. The generated features offer information complementary to traditional clinical predictors in numerous applications, which may help advance cancer care towards personalized precision medicine. Numerous recent radiomics studies have focused on classification, characterization, prognostication, and treatment guidance of HNSCC.

Paired with key clinical predictors, radiomic analysis can capture a large variety of HNSCC properties [1], enabling the predictive models to more accurately reflect the spatial, metabolic, and morphological heterogeneity of primary tumor lesions and metastatic lymph nodes. This review aims to provide an overview of recently published HNSCC radiomics studies focusing on (molecular) characterization, classification, prognostication and treatment guidance. The general principle of radio(geno)mic analysis and the typical radiomics workflow are introduced. We also discuss the applications of advanced machine learning for radiomics-based modelling. Finally, we summarize future challenges, barriers and limitations of individual radiomic applications, as well as the field of head and neck radiomics in general.


Over the last decade, advancements in high-throughput computing and machine learning algorithms have led to emergence of the “-omics” concept – referring to the collective characterization and quantification of pools of biologic information, such as genomics, proteomics or metabolomics. Radiomics refers to automated extraction of mathematically defined, numerical descriptors (“radiomics features”) from 2-dimensional – or more commonly – 3-dimensional medical images and subsequent application of data mining and analysis techniques. Over the past few years, there has been an increasing interest in application of radiomics in patients with HNSCC for prediction of molecular biomarkers, prognostication, and treatment response.

Radiomics features commonly describe shape, intensity (histogram) and texture characteristics. These features can be extracted from different imaging modalities, such as CT, MRI, or metabolic imaging like 18- fludeoxyglucose positron emission tomography (FDG-PET). The notion that certain characteristics of medical images – which are not reliably assessed by human visual inspection – can provide medically meaningful information for diagnostic and prognostic purposes as well as treatment guidance is the underlying hypothesis in the emerging field of radiomics [2]. Prior studies showed that radiomics features represent biological characteristics of the tissue such as cellularity, heterogeneity, and necrosis [3]; and frequently exhibit correlation with diagnostic and outcome variables [2]. Furthermore, certain features can be reflective of molecular and genetic characteristics of malignant tissue. The subfield of Radiogenomics focuses on the identification and scientific exploitation of relationships between quantitative bioimaging features and genomic characteristics of the tumor [4]. It is worth noting that radiomics analysis captures information from the whole volume of interest (VOI), and therefore may act as a quantitative descriptor of tumor spatial heterogeneity, whereas the diagnostic validity of localized tools like tissue sampling may be degraded in heterogeneous tumors [3, 5].

Radiomics workflow

Despite not being part of the radiomics workflow in a narrower sense, image acquisition is often considered the first step in radiomics analysis. Radiomics feature robustness and reproducibility against variation in scan acquisition protocols have been extensively investigated across imaging modalities and in various settings [6], including test-retest assessments [7,8,9], studies designed to evaluate the impact of scanner types/manufacturers using phantoms [10, 11], reconstruction algorithms /slice thickness [12, 13], and motion artifacts [14]. Traverso et al. [6] conducted a systematic review of 41 studies investigating the reproducibility and stability of radiomics features in phantoms and different cancers – including lung, HNSCC, and esophageal cancer – and found that only three studies investigated radiomics reproducibility in HNSCC. Bagher-Ebadian et al. [15] investigated the impact of smoothing and noise on CT and cone beam CT textural features and reported general feature robustness against low-power Gaussian noise and low pass filtering, whereas a high-pass filter significantly impacted textural features. Bogowicz et al. [16] focused on feature stability regarding CT perfusion calculation factors. Finally, Lu et al. [17] studied the effect of seven different segmentation methods and 5 forms of fixed-bin SUV-discretization on PET radiomic features, reporting 50 and 23% of 88 tested features were robust to FDG-PET segmentation and discretization, respectively (with robustness ascertained by an intraclass correlation coefficient ≥ 0.8). While there is as yet no consensus regarding stable radiomic feature sets, it is crucial to assess stability of radiomic features in each study – especially for generalization of findings and future comparison.

The next step in the radiomics workflow involves the delineation (“segmentation”) of the target area/volume in medical images, resulting in image sub-sections referred to as regions of interest (ROI) and volumes of interest (VOI) in 2- and 3-dimensional images, respectively. Manual and (semi-) automated segmentation have both been applied in recent radiomics studies, each with its inherent advantages and drawbacks. Manual segmentation is affected by observer variability; several studies investigated the inter- and intra-rater reproducibility of CT and PET radiomic features extracted from repeated manual segmentations of lung cancer lesions [8, 18, 19]. Lu et al. [17] assessed feature stability across manual and various automated segmentation techniques applied to oropharyngeal cancer lesions on PET scans and showed that 50% of features extracted from 18-FDG-PET achieved an intraclass correlation coefficient ≥ 0.8, which was considered sufficiently reproducible. Across all studies, individual radiomic features were found to exhibit varying degrees of robustness against observer variability, suggesting stability measures may be appropriated for feature dimensionality reduction. A multitude of (semi) automated segmentation methods have been proposed or adapted for Radiomics purposes [20]. An in-depth discussion of the various algorithmic approaches is beyond the scope of this review; however, reproducibility and observer variability are certainly a minor concern with (semi-) automated approaches. On the other hand, fully automated segmentation can only be “as good as” the expert-generated ground truth data used for development and may be impaired by artifacts, presence of multiple pathologic findings and other abnormalities not considered in the development process. The resulting imprecisions in segmentations will undoubtedly affect the quality and usefulness of extracted radiomic features, warranting thorough human validation.

Image pre-processing is usually applied as the next step following segmentation: Resampling voxels to uniform sizes is often necessary due to the heterogeneity of the available imaging data, originating from different scanners and reconstruction protocols. Additionally, resampling to isotropic voxels (i.e. voxel with identical edge lengths) should be considered as it guarantees rotational invariance of texture features [21]. While CT imaging uses a “real-valued” grey scale (the Hounsfield unit scale is an absolute representation of physical density), other imaging modalities require gray scale homogenization to facilitate inter-patient comparability of radiomic features; for example, PET scanners measure radioactivity concentrations [MBq/mL] which directly depend on the amount of injected radiotracer and patient weight [22]. To compensate for variability, the standardized uptake value (SUV) is calculated for each voxel as a relative measure of radiotracer uptake in clinical practice as well as radiomics studies [17, 19, 23,24,25]. MRI grey scales are expressed in arbitrary units unique to the hardware and reconstruction method used. Presence of heterogeneous image acquisition variables in an MRI dataset always necessitates image normalization before radiomic feature extraction [26,27,28]. Notably, in addition to the original image, radiomic features are often extracted from transformed or filtered images. A multitude of studies applied wavelet-decompositions to extract texture features from different frequency bands of the original image [7, 18, 24, 25, 29, 30]. Smoothing filters (e.g. Gaussian filters) or combined filters (e.g. Gaussian smoothing followed by Laplacian for edge enhancement) have also been implemented by some studies [20, 30,31,32].

Radiomic feature extraction represents the last step of common radiomics pipelines. Zwanenburg et al. published “The image biomarker standardisation initiative” (IBSI), which is the most recent attempt to standardize image pre-processing and radiomics feature sets across the field [21]. In brief, IBSI defines 11 feature families, assessing geometric aspects of the ROI/VOI shape, quantifying the grey scale intensity (distribution), and lesion texture. Feature extraction is usually performed by dedicated software in a fully automated fashion. Recent studies extracted their feature sets from original images and several derivatives thereof – generated by filtering, resampling and transformation. This approach commonly yields feature vectors in the magnitude of hundreds to several thousand data points per segmented ROI/VOI.

Both open source- and in house-developed feature libraries and radiomics extraction software have been utilized in recent radiomics studies. Two commonly used open-source solutions for radiomics feature extraction are the “Imaging Biomarker Explorer (IBEX)” [33], and “PyRadiomics” [34]. They represent adaptable, configurable platforms for image preprocessing and feature extraction and were applied in recent HNSCC radiomics studies (for example IBEX in refs [31, 35], .PyRadiomics in refs [36, 37].). The considerable methodological variability in HNSCC-related radiomics studies heralds the need for devising evidence-based consensus radiomics pipelines to improve reproducibility and generalizability. Fig. 1 summarizes the essential steps in common radiomics pipelines.

Fig. 1

Typical radiomics workflow pipeline

Machine learning analysis of radiomics features

Radiomics pipelines extract high-dimensional, quantitative feature sets from medical images [2]. This bioimage-based information is most helpful when combined with clinical variables, serum markers, and other conventional prognostic biomarkers, creating the need for efficient analysis and development of predictive models based on high-dimensional data. Machine learning (ML) methods have proven to be statistically powerful tools for taking on such challenges [2, 38].

ML refers to a series of statistical algorithms driving their functionality from labelled or unlabeled training data, rather than applying predefined sets of rules and functions [38]. This property is ideal in the setting of radiomics research, where extensive numbers of bioimaging features are extracted, to predict molecular biomarkers, histopathological characteristics, clinical outcome, or treatment response [2].

To limit overfitting and augment generalizability, ML studies are ideally based on training, validation, and independent/external testing in separate datasets [38]. The training and validation datasets are used to iteratively fit the ML model (training data), assess its performance (validation data) and optimize model parameters (“tuning of hyperparameters”) [38]. Alternatively, cross validation may be applied to fit/assess/tune the model based on random subdivision and iterative rounds of training and validation [20]. The independent/external test cohort will be kept fully isolated from the model development process and is used to test the final ML model and confirm its performance and generalizability [20, 38].

Typically, in radiomics studies, a data dimensionality reduction strategy is combined with a ML classification or regression algorithm [20]. Dimensionality reduction usually aims to exclude redundant and unstable features and rank-orders the remaining features according to their predictive association with the target outcome. Then, ML algorithms combine the most predictive features into a meaningful, predictive model [20]. The model is next applied to the validation set, where its performance is assessed [38]. The process is iteratively repeated and hyperparameters are adjusted throughout [38].

In a study exploring the importance of feature selection in radiomics analysis, Parmar et al. [29] compared different combinations of 13 feature selection methods and 11 ML classifiers to predict overall survival based on a set of 440 radiomic features extracted from 231 HNSCC primary tumor lesions in contrast-enhanced CT images. Using multifactor analysis of variance (ANOVA) on the receiver operating characteristics (ROC) area under the curve (AUC), they assessed the effect of three ML framework variables (feature selection methods, classification methods, number of selected features). They found that while ML classification methods accounted for 29.02% of the total variance in classification accuracy, the feature selection methods explained 14.02%, and the interaction of classifier and feature selection explained 16.59%. These findings highlight the importance of selecting the appropriate combination of feature selection and ML models, for example by testing various combinations of algorithms with high performance in prior studies.

In the field of head and neck cancer radiomics, classification and (survival) regression models are frequently applied for prediction of molecular markers, identification of genomic signatures, diagnostic differentiation of suspected tissue, survival prognostication, and prediction of treatment response. Increasing numbers of publicly available mega-data and open-source machine-learning algorithms have paved the road for development of novel multivariate diagnostic and prognostic biomarkers integrating quantitative radiomics features and clinical variables for risk stratification, outcome prediction, and precision treatment planning in HNSCC.

Radiomics signatures of HNSCC molecular markers

Multiple recent radiomics studies reported the associations of bioimaging features with various molecular HNSCC traits, such as human papillomavirus (HPV) status, somatic mutations, methylation and gene expression subtypes and PD-L1 expression levels. Among all investigated HNSCC molecular traits, HPV has been evaluated the most:

Human papillomavirus status

The incidence of HPV-associated oropharyngeal SCC (OPSCC) has been rising in recent decades [39, 40]. The prevalence of HPV-associated forms among OPSCC in North America has increased from 50.7% before 2000, to 69.7% in the period from 2005 through 2010 [41]. HPV-positivity is a strong, independent prognostic factor for favorable outcome and overall survival (OS) in patients with OPSCC [42, 43]. HPV association in HNSCC is associated with distinct tumor morphology (smaller primary tumors, marked cervical adenopathy at presentation), younger patients’ age at presentation, and favorable response to radiation therapy [43]. Consequently, the latest release of the American Joint Committee on Cancer (AJCC), and Union for International Cancer Control (UICC) staging manuals have classified HPV-mediated OPSCC as a distinct tumor entity with different staging rules from the OPSCC-negative form [44, 45]. In addition, recent studies suggest that HPV association may analogously impact OS in non-oropharyngeal forms of the HNSSC [46, 47].

Since 2015, multiple studies have demonstrated the association of radiomic features with HPV status in HNSCC: While Buch et al. [48] and Fujita et al. [49] examined the association of individual texture features with HPV status, other groups have designed machine learning classification models for HPV prediction in HNSCC. Table 1 summarizes prior work in this field.

Table 1 Prediction of HPV status based on radiomics features of HNSCC tumors

Of note, some studies did not report the details of the HPV test used for ground truth labeling [48, 49, 53, 54, 57], and some used p16 immunohistochemical surrogate testing to consequently predict p16 status [59]. Many studies evaluated their models’ generalizability in independent confirmation cohorts and confirmed similar performance as compared to the training datasets [50,51,52, 54, 56]. While the majority of studies to date have applied CT-based radiomics for HPV classification, Vallieres et al. [60] reported their preliminary results based on radiomics features from FDG-PET scans in 67 patients with HNSCC. In addition, quantitative diffusion MRI studies have shown the difference in apparent diffusion coefficient values between HPV-positive and HPV-negative OPSCC [61,62,63]; however, there has yet been no report of MR-based radiomics signatures for prediction of HPV status.

A potential application of radiomics-based biomarkers for HPV status would be to aid pathologists if standard p16 immunohistochemical staining is equivocal or to supplement the immunohistochemical tests in subjects requiring second-line testing. For routine clinical HPV-testing, the 2018 Guideline from the College of American Pathologists recommends p16 immunohistochemistry as a surrogate marker for HPV-association on samples from the primary tumor or cervical level II or III nodal metastases. However, they recommend using HPV-specific testing – such as in situ hybridization for HPV DNA – in certain p16-positive cervical nodes or multisite primary tumors [59]. In such cases, radiomics-based biomarkers may be an inexpensive substitute confirmatory test for HPV status.

In addition, radiomics signatures for HPV classification may serves as a prognostic biomarker in patients with OPSCC. Leijenaar et al. [52] used contrast-enhanced CT radiomic features from OPSCC primary tumors (628 subjects for training and 150 for validation) to devise a radiomic biomarker for HPV status. Using Kaplan-Maier survival analysis, they showed that both p16 (as a surrogate for HPV), and the radiomics-based classifier could differentiate low- versus high-risk patients in survival curve analysis. Future studies will likely explore the role of other imaging modalities such as MRI or FDG-PET as well as state-of-the-art ML classifiers to enhance classification performance. There is also a potential role for application of radiomics to detect HPV-association of metastatic nodes in carcinoma of unknow primary that may direct search for the tumor origin to the oropharynx.

Radiomics biomarkers of HNSCC molecular subtypes beyond HPV status

Several recent studies have proposed novel radiomics biomarkers for prediction of HNSCC molecular features and subtypes, aside from HPV status.

Zwirner et al. [64] hypothesized that frequently mutated HNSCC driver genes may correlate with radiomics features known to quantify intra-tumor heterogeneity. The analysis was thus focused on three radiomics features initially described by Aerts et al. [18]. A total of 20 patients with locally advanced SCC of the oral cavity, oropharynx or hypopharynx were recruited for a prospective study by [64]; next-generation tumor sequencing and radiomics analysis of corresponding non-contrast radiotherapy planning CTs was performed. The presence of mutations in known driver genes (TP53, FAT1 and KMT2D) were correlated with each of the three selected radiomics features; and showed significant association of all three tested radiomics features with FAT1 [64]. The authors suggested that these findings are likely related to lower heterogeneity in FAT1-mutated HNSCC tumors.

Huang et al. [51] studied a series of molecular HNSCC “phenotypes”: five DNA methylation subtypes, four previously identified HNSCC gene expression subtypes (transcriptomics-based [65]) and five common somatic gene mutations. DNA methylation aberrations were explored using the MethylMix algorithm [66], followed by consensus clustering for subtyping. Contrary to Zwirner et al. [64], Huang et al. used a large radiomics feature set comprised of 540 individual features extracted from pre-treatment contrast-enhanced CT scans of 113 patients [51]. Feature selection and LASSO-penalized logistic regression were applied in nested cross validation. Multi-class classification was facilitated using a “one-vs-all” approach (i.e. binary classifiers were trained to predict any given class against all others). The machine learning classifiers yielded moderate to good predictive performance in identification of the HNSCC molecular phenotypes, even exceeding models based on clinical variables only.

In a cohort of 126 HNSCC patients, Zhu et al. [57] examined the correlation of radiomic features extracted from contrast-enhanced CT-images with whole-genome multiomics data (microRNA expression, somatic mutations, transcriptional activity of pathways, copy number variations and promoter region DNA methylation changes of pathways). They identified over 5000 significant associations, suggesting widespread association of genomic markers and radiomic features from various feature families. Additionally, Zhu et al. trained random forest classifiers in 5-fold cross validation to predict HPV status (Table 1) and disruptive TP53 mutation status, with the most predictive model yielding an AUC of 0.641 (averaged across 30 cross validation repetitions).

In 2016, nivolumab and pembrolizumab were FDA-approved for treatment of recurrent or metastatic squamous cell carcinoma of the head and neck with disease progression on or after a platinum-based therapy [67]. Expression of programmed cell death protein 1 ligand (PD-L1) is the single factor that is most strongly correlated with response to PD-1 blockers like nivolumab or pembrolizumab [68]. Since overall response rates to these agents are low, ranging from 13 to 18% [69, 70], quantification of PD-L1 expression by immunohistochemical staining has been applied to identify patients who are more likely to respond [71, 72]. Extracting textural features from the PET-portions of staging FDG-PET/CT scans, Chen et al. [73] reported significant association of several radiomics features with PD-L1 expression in 53 patients with oropharyngeal and hypopharyngeal SCC. Multivariate logistic regression analysis revealed one FDG-PET radiomics feature as an independent predictor for PD-L1 expression (PD-L1 staining cutoff of 5%) [73].

Thus far, exploratory studies show associations of CT- and FDG-PET-derived radiomic imaging features with genomic, transcriptomic and proteomic characteristics of HNSCC, suggesting that future “multiomic” investigations of HNSCC should incorporate radiomics-based biomarkers. Additional imaging modalities as well as molecular targets are the focus of future investigations.

Prediction of recurrence, treatment response, and survival in HNSCC

Despite major efforts in treatment and drug development, prognosis of HNSCC is generally poor, with five-year survival rates in Europe ranging from 25% in hypopharyngeal cancer to 59% for cancers of the larynx [74,75,76]. Additionally, the majority of patients with HNSCC presents with advanced-stage disease [76, 77].

More accurate risk stratification, treatment response prediction and prognostication may help clinicians to selectively plan treatment options, guide treatment intensity and ultimately tailor personalized cancer care for their patients. This notion triggered interest among scientists, making outcome prediction by means of bioimaging-features the most popular field within head and neck radiomics.

Table 2 summarizes recent studies focusing on prediction of survival, locoregional recurrence, distant metastasis, progression or treatment failure as well as several composite outcome endpoints. One study used radiomics for prediction of early response to induction chemotherapy [27]; another predicted response to chemoradiotherapy [28] – both in nasopharyngeal carcinoma. Oropharyngeal SCC, laryngeal SCC, hypopharyngeal SCC, nasopharyngeal cancer and combined HNSCC cohorts were investigated from 2013 through 2019, with a marked range in terms of cohort size: While exploratory studies used as few as 30 cases [92], others gathered expansive datasets. For example: 240 and 204 contrast CTs were used for model training and testing, respectively, by Zhai et al. study [93], reporting significantly better prognostic performance of a combined model (radiomics + clinical predictors) as compared to a clinical-variables-only model for disease-free survival in HNSCC. Using 542 oropharyngeal SCC cases from Canada, Leijenaar et al. [87] externally validated a radiomics signature previously devised by Aerts et al. [18] on 422 non-small cell lung cancer contrast-enhanced CTs, which showed significant prognostic differentiation in Kaplan-Meier overall survival analysis in all sub-cohorts. A similarly large dataset of pre-treatment contrast-enhanced CT scans (465 oropharyngeal SCC cases) was analyzed by the Head and Neck Quantitative Imaging Working Group of M.D. Anderson Cancer Center [31]; whose proposed 2-feature-signature could robustly discriminate between the high- versus low-recurrence probability groups. Individual radiomics features, radiomic signatures/scores (e.g. (linear) combinations of several features [18, 27, 31, 94]) as well as ML-generated models [29, 30, 95] showed significant predictive value in a multitude of HNSCC settings, including various HNSCC sub-entities, and outcomes (Table 2).

Table 2 Prediction of locoregional recurrence, treatment response, and survival

The complimentary value of radiomics analysis in addition to conventional “clinical” predictors has been emphasized by several groups [18, 93, 96]. However, using multi-institutional and multi-national dataset of 726 pre-treatment contrast CT scans and 686 FDG-PET scans, Ger et al. [35] were unable to improve HNSCC overall survival prediction using multivariate Cox proportional hazard models incorporating only two and one radiomic features in separate CT-based and FDG-PET-based analysis, respectively. These findings suggest more complex analysis strategies may help improve predictive performance. Leger et al. [30] applied 11 ML algorithms combined with 12 feature selection methods in a proof-of-technology study and identified several promising combinations which may be applied in future time-to-event modelling. Combining large HNSCC cohorts with advanced ML analysis may eventually enable radiomics to more consistently improve prognostic models.

Contrast-enhanced and non-contrast CT, (contrast-enhanced) T1 and T2 MRI sequences and FDG-PET imaging were all applied for radiomics based outcome prediction (Table 2) as well as some less common imaging techniques including diffusion-weighted MRI [28], 18F-fluorothymidine-PET [92], and perfusion CT [80]. Studies listed in Table 2 applied different analytical strategies, such as using single feature, feature combinations (“signatures”, “scores”) or more complex combined models; such analytical heterogeneity limits direct comparison of studies [97], and cannot be fully reflected in Table 2. The majority of studies, however, applied multivariate Cox proportional hazard models, the results of which are summarized in the table. The performance of radiomics, clinical or combined models with regards to the respective outcome(s) prediction is expressed in the Cox-model hazard ratio, and the concordance index typically reflects the overall accuracy of models in survival prediction.

Detection of extra-nodal extension of metastasis

Extra-nodal extension (ENE) of metastasis in cervical lymph nodes is a poor prognostic factor and is associated with higher risk of developing recurrent disease [98,99,100,101]. Thus, the presence of ENE warrants addition of chemotherapy to adjuvant irradiation [98,99,100,101], requiring tri-modality treatment with increased toxicity and patient morbidity [102, 103]. Reliable detection of ENE prior to the therapy, could help guide treatment choices, reduce morbidity, and avoid surgery in patients likely requiring adjuvant chemoradiation. In clinical practice, ENE is ascertained by pathology review after neck dissection, whereas radiographical identification remains challenging [104, 105]. Kann et al. developed [106] and validated [107] quantitative imaging tools for pre-operative detection of ENE: the group segmented 653 nodes in total (380 negative, 153 without ENE and 120 nodes with ENE) on contrast-enhanced CT scans and extracted 99 radiomic features [106]. Random forest ML classifiers were trained and yielded an AUC (95% confidence interval) of 0.88 (0.81–0.95) for the detection of ENE and 0.91 (0.86–0.97) for nodal metastasis detection in an independent test set of 131 lymph nodes; whereas – being the methodological focus of the study – a deep neural network yielded an AUC performance of 0.91 (0.85–0.97) and 0.91 (0.86–0.96) for ENE and metastasis detection, respectively [106]. The deep neural network model generalized well to an external test set, outperforming radiologists in ENE classification [107].

Of note [106, 107], there was no significant difference in performance of deep neural networks (exploratory radiomics) over (preset conventional) radiomic analysis in detection of ENE. They highlight the potential quantitative imaging may possess for augmenting radiologist performance and guiding HNSCC treatment.

Predicting post chemoradiotherapy complications

Radiotherapy combined with chemotherapy (chemoradiotherapy, CRT) is the mainstay treatment regimen for many patients with HNSCC [101]. However, patients not uncommonly suffer from treatment-related side effects such as xerostomia, trismus, hearing loss, mucositis and dermatitis. Identification of those patients who are at risk of developing specific side effects may guide oncologists to plan personalized treatment strategies and adopt preventive remedies to improve therapy tolerance. Several groups have devised radiomics biomarkers to predict the occurrence or severity of treatment-related toxicities based on bioimaging features of at-risk organs.


Radiation-induced xerostomia is a common side effect of radiation therapy for HNSCC and remains a challenge in long-term patient management [108, 109]. The dose-dependent increased risk of xerostomia after irradiation of the salivary glands is well established [109]. Four separate groups designed radiomics-based models to predict post-radiation xerostomia in patients with HNSCC with or without concurrent chemotherapy (Table 3). Imaging features were extracted from salivary glands – either the parotid gland(s) or parotid glands and submandibular glands. A heterogeneous set of xerostomia endpoints was investigated: Sheikh et al. [110] predicted a binary xerostomia-endpoint 3 month post radiotherapy; Liu et al. [111] applied regression analysis for acute xerostomia prediction; and van Dijk et al. [112,113,114] used three different imaging modalities (CT, MRI, FDG-PET) for long-term binary xerostomia outcome classification. Furthermore, the xerostomia assessment methods varied: Liu et al. used objective saliva amount measurements over 5 min [111], whereas other groups used patients-filled questionnaires [112,113,114]. While these results appear promising, their clinical application is limited by the lack of external validation, heterogeneity in image processing, statistical analysis, and treatment outcome measures.

Table 3 Prediction of post-radiation xerostomia based on salivary gland radiomics features


Trismus in HNSCC patients may result from involvement of masticatory muscles in radiotherapy treatment fields, surgery or cancerous invasion into mastication structures or the neural innervation of masticatory muscles [117, 118]. Defining trismus ≥ Grade 1 by CTCAE v4.0 (Common Terminology Criteria for Adverse Events Version 4.0 [115]) criteria 1 year following completion of intensity-modulated radiotherapy (IMRT), Thor et al. [119] compared 24 imaging features from four masticatory muscles on contrast-enhanced post-treatment T1-weighted MRI scans in 10 patients with radiation-induced trismus, versus 10 control subjects. The best discriminative ability among radiomics predictors was observed for the Haralick Correlation GLCM-matrix feature of the medial pterygoid muscle VOI (logistic regression p = 0.12, AUC = 0.78). Their result was not significant, but may be indicative of a potential of radiomics biomarkers for prediction of post-radiation trismus. Studies in larger cohorts may be the focus of future research, to devise radiomics signature predictive of post-radiotherapy trismus.

Hearing loss

Abdollahi et al. [120] explored the potential application of cochlear radiomics for prediction of chemoradiotherapy-induced hearing loss. Using radiomics features extracted from the cochlea on pre-treatment CT scans, they evaluated 47 cancer patients (brain, nasopharynx, parotid, other) treated with 3-dimensional conformal radiation therapy, 23 of whom also received cisplatin-chemotherapy. They showed that combination of radiomic features with clinical and dosimetric variables may predict radiotherapy-induced sensory neural hearing loss.

Future directions, challenges and barriers

The next leap forward in radiomic analysis undoubtedly lies in developing decision support and prognostic tools for day-to-day clinical usage. However, several key barriers and challenges in the field of quantitative imaging should be addressed first:

While exploratory radiomics studies have achieved promising results throughout, independent large-scale validation is lagging [121, 122]. A recent publication by Kim et al. [122] reported on design characteristics of 516 studies applying AI algorithms for diagnostic analysis of medical images. Only 31 studies (6%) have validated their proposed models in external test cohorts – i.e. cohorts from institutions other than the one providing the training data, as well as cohorts obtained from the same institution but a different time period as the training data. On the other hand, usage of homogenous, single-institution or even single-scanner training data may limit the generalizability of radiomics-based models [121]. These limitations highlight the importance of multi-institutional, multi-national medical imaging archives for development of radiomics tools for future clinical usage. Data sharing may help mitigate the shortage of diverse imaging data [2]; hence, platforms like “The Cancer Imaging Archive” (TCIA) were created. TCIA publicly hosts de-identified imaging collections with corresponding clinical data and provides digital infrastructure for data sharing [123]. As of December 2019, nine head and neck cancer collections are available comprising CT, MRI and FDG-PET imaging data [123].

Further challenges lie in the implementation of the radiomics pipeline (including image acquisition) as outlined in this article. Forghani et al. [1] described sources of variation impairing generalizability and reproducibility of radiomics studies, including:

  • Scan acquisition parameters

  • Variability in post-contrast images – such as the degree of enhancement achieved, depending on timing of a contrast agent administration, patients’ circulatory dynamics, anatomical location of the VOI

  • Wear and tear of scanners

  • Differences in manufacturer, model, type of scanner

  • Reconstruction parameters

  • VOI/ROI segmentation

  • Radiomics feature set / feature extraction

Preprocessing steps like resampling and filtering (Fig. 1) may help mitigate some variation. However, standardization of reconstruction and acquisition parameters across providers as well as scanner components among manufacturers should be pursued as the field moves towards clinical application of AI-driven image analysis.

Inter- and intra-observer VOI/ROI delineation variability could be addressed by using semi-automated or automated segmentation tools. In addition, there have been efforts to standardize radiomics features – the most recognized being the “The image biomarker standardisation initiative” (IBSI) [21]. Moreover, open-source feature libraries and radiomics extraction software packages like “PyRadiomics” [34], or the “Imaging Biomarker Explorer” [33] allow for reproducible feature extraction as well as easy reporting of radiomics feature definitions and are increasingly adopted by recent publications.


Precision prognostication and treatment personalization is considered the next major evolution in cancer care, and the “-omics”-concept has been postulated as key enabler thereof. Numerous studies have established radiomics as powerful addition to the “-omics”-toolbox, and ongoing research provides incremental upgrades. Radiomics has indeed revolutionized the landscape of quantitative imaging research: In the future, fast, low-cost and comprehensive tumor and tissue characterization facilitated by radiomic analysis may constitute a compelling augmentation – or even alternative – for traditional clinical testing and prognostication, if adequate performance and stability is attained. Numerous studies in the past 6 years have reported potential applications of radiomics analysis for molecular classification, prognostic characterization, and treatment response prediction in patients with HNSCC. While recent exploratory studies yield promising results in the field of HNSCC radiomics, independent large-scale validation is lagging behind as access to multi-institutional, multi-national imaging data is restricted. Standardization of radiomics pipelines, image acquisition protocols, and outcome targets can pave the road towards engineering of radiomics tools for day to day clinical usage, and ultimately superior outcomes and reduced treatment-related toxicities in the field of head and neck cancer.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.



Artificial intelligence


American Joint Committee on Cancer


Area under the curve


18- fludeoxyglucose positron emission tomography


Head and neck squamous cell carcinoma


Human papillomavirus


Machine learning


Oropharyngeal SCC


Overall survival


Receiver operating characteristics


Squamous cell carcinoma


The Cancer Imaging Archive


Union for International Cancer Control


  1. 1.

    Forghani R, Savadjiev P, Chatterjee A, Muthukrishnan N, Reinhold C, Forghani B. Radiomics and artificial intelligence for biomarker and prediction model development in oncology. Comput Struct Biotechnol J. 2019;17:995–1008.

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, They Are Data. Radiology. 2016;278(2):563–77.

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol. 2016;61(13):R150–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Pinker K, Shitano F, Sala E, Do RK, Young RJ, Wibmer AG, et al. Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging. 2018;47(3):604–20.

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Traverso A, Wee L, Dekker A, Gillies R. Repeatability and reproducibility of Radiomic features: a systematic review. Int J Radiat Oncol Biol Phys. 2018;102(4):1143–58.

    Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Balagurunathan Y, Kumar V, Gu Y, Kim J, Wang H, Liu Y, et al. Test-retest reproducibility analysis of lung CT image features. J Digit Imaging. 2014;27(6):805–23.

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Leijenaar RT, Carvalho S, Velazquez ER, van Elmpt WJ, Parmar C, Hoekstra OS, et al. Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol. 2013;52(7):1391–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Fiset S, Welch ML, Weiss J, Pintilie M, Conway JL, Milosevic M, et al. Repeatability and reproducibility of MRI-based radiomic features in cervical cancer. Radiother Oncol. 2019;135:107–14.

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, et al. Measuring computed tomography scanner variability of Radiomics features. Investig Radiol. 2015;50(11):757–65.

    Article  Google Scholar 

  11. 11.

    Bologna M, Corino V, Mainardi L. Technical note: virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI-radiomics of the brain. Med Phys. 2019;46(11):5116–23.

    Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Zhao B, Tan Y, Tsai WY, Qi J, Xie C, Lu L, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep. 2016;6:23428.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Yan J, Chu-Shern JL, Loi HY, Khor LK, Sinha AK, Quek ST, et al. Impact of Image reconstruction settings on texture features in 18F-FDG PET. J Nucl Med. 2015;56(11):1667–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Oliver JA, Budzevich M, Zhang GG, Dilling TJ, Latifi K, Moros EG. Variability of Image features computed from conventional and respiratory-gated PET/CT images of lung Cancer. Transl Oncol. 2015;8(6):524–34.

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Bagher-Ebadian H, Siddiqui F, Liu C, Movsas B, Chetty IJ. On the impact of smoothing and noise on robustness of CT and CBCT radiomics features for patients with head and neck cancers. Med Phys. 2017;44(5):1755–70.

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Bogowicz M, Riesterer O, Bundschuh RA, Veit-Haibach P, Hullner M, Studer G, et al. Stability of radiomic features in CT perfusion maps. Phys Med Biol. 2016;61(24):8736–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Lu L, Lv W, Jiang J, Ma J, Feng Q, Rahmim A, et al. Robustness of Radiomic features in [(11)C]choline and [(18)F]FDG PET/CT imaging of nasopharyngeal carcinoma: impact of segmentation and discretization. Mol Imaging Biol. 2016;18(6):935–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    van Velden FH, Kramer GM, Frings V, Nissen IA, Mulder ER, de Langen AJ, et al. Repeatability of Radiomic features in non-small-cell lung Cancer [(18)F]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol. 2016;18(5):788–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Larue RT, Defraene G, De Ruysscher D, Lambin P, van Elmpt W. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol. 2017;90(1070):20160665.

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Zwanenburg A, Leger S, Vallières M, Löck S. Image biomarker standardisation initiative. arXiv e-prints. 2016;2016 Available from:

  22. 22.

    Kinahan PE, Fletcher JW. Positron emission tomography-computed tomography standardized uptake values in clinical practice and assessing response to therapy. Semin Ultrasound CT MR. 2010;31(6):496–505.

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Vallieres M, Kay-Rivest E, Perrin LJ, Liem X, Furstoss C, Aerts H, et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep. 2017;7(1):10117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Lv W, Yuan Q, Wang Q, Ma J, Feng Q, Chen W, et al. Radiomics analysis of PET and CT components of PET/CT imaging integrated with clinical parameters: application to prognosis for nasopharyngeal carcinoma. Mol Imaging Biol. 2019;21(5):954–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Bogowicz M, Riesterer O, Stark LS, Studer G, Unkelbach J, Guckenberger M, et al. Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma. Acta Oncol. 2017;56(11):1531–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Shinohara RT, Sweeney EM, Goldsmith J, Shiee N, Mateen FJ, Calabresi PA, et al. Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin. 2014;6:9–19.

    Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Wang G, He L, Yuan C, Huang Y, Liu Z, Liang C. Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma. Eur J Radiol. 2018;98:100–6.

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Liu J, Mao Y, Li Z, Zhang D, Zhang Z, Hao S, et al. Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma. J Magn Reson Imaging. 2016;44(2):445–55.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ. Radiomic machine-learning classifiers for prognostic biomarkers of Head and neck Cancer. Front Oncol. 2015;5:272.

    Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Leger S, Zwanenburg A, Pilz K, Lohaus F, Linge A, Zophel K, et al. A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling. Sci Rep. 2017;7(1):13206.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Group MDACCHaNQIW. Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients. Sci Rep. 2018;8(1):1524.

    Article  CAS  Google Scholar 

  32. 32.

    Zdilar L, Vock DM, Marai GE, Fuller CD, Mohamed ASR, Elhalawani H, et al. Evaluating the effect of right-censored end point transformation for Radiomic feature selection of data from patients with Oropharyngeal Cancer. JCO Clin Cancer Inform. 2018;2:1–19.

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys. 2015;42(3):1341–53.

    Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational Radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–e7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Ger RB, Zhou S, Elgohari B, Elhalawani H, Mackin DM, Meier JG, et al. Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients. PLoS One. 2019;14(9):e0222509.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Liang ZG, Tan HQ, Zhang F, Rui Tan LK, Lin L, Lenkowicz J, et al. Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma. Br J Radiol. 2019;92(1102):20190271.

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Zhang L, Dong D, Li H, Tian J, Ouyang F, Mo X, et al. Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: a retrospective cohort study. EBioMedicine. 2019;40:327–35.

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, et al. Current applications and future impact of machine learning in radiology. Radiology. 2018;288(2):318–28.

    Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Mourad M, Jetmore T, Jategaonkar AA, Moubayed S, Moshier E, Urken ML. Epidemiological trends of Head and neck Cancer in the United States: a SEER population study. J Oral Maxillofac Surg. 2017;75(12):2562–72.

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Gillison ML, Chaturvedi AK, Anderson WF, Fakhry C. Epidemiology of human papillomavirus-positive Head and neck squamous cell carcinoma. J Clin Oncol. 2015;33(29):3235–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Mehanna H, Beech T, Nicholson T, El-Hariry I, McConkey C, Paleri V, et al. Prevalence of human papillomavirus in oropharyngeal and nonoropharyngeal head and neck cancer--systematic review and meta-analysis of trends by time and region. Head Neck. 2013;35(5):747–55.

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Ang KK, Harris J, Wheeler R, Weber R, Rosenthal DI, Nguyen-Tan PF, et al. Human papillomavirus and survival of patients with oropharyngeal cancer. N Engl J Med. 2010;363(1):24–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Benson E, Li R, Eisele D, Fakhry C. The clinical impact of HPV tumor status upon head and neck squamous cell carcinomas. Oral Oncol. 2014;50(6):565–74.

    Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Lydiatt WM, Patel SG, O'Sullivan B, Brandwein MS, Ridge JA, Migliacci JC, et al. Head and neck cancers-major changes in the American joint committee on cancer eighth edition cancer staging manual. CA Cancer J Clin. 2017;67(2):122–37.

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Glastonbury CM, Mukherji SK, O'Sullivan B, Lydiatt WM. Setting the stage for 2018: how the changes in the American joint committee on Cancer/Union for International Cancer Control Cancer staging manual eighth edition impact radiologists. AJNR Am J Neuroradiol. 2017;38(12):2231–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Wookey VB, Appiah AK, Kallam A, Ernani V, Smith LM, Ganti AK. HPV status and survival in non-Oropharyngeal squamous cell carcinoma of the Head and neck. Anticancer Res.2019;39(4):1907–14.

    Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Burr AR, Harari PM, Ko HC, Chen S, Yu M, Baschnagel AM, et al. HPV impacts survival of stage IVC non-oropharyngeal HNSCC cancer patients. Otorhinolaryngol Head Neck Surg.2018;3(1):1–7.

  48. 48.

    Buch K, Fujita A, Li B, Kawashima Y, Qureshi MM, Sakai O. Using texture analysis to determine human papillomavirus status of Oropharyngeal squamous cell carcinomas on CT. AJNR Am J Neuroradiol. 2015;36(7):1343–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Fujita A, Buch K, Li B, Kawashima Y, Qureshi MM, Sakai O. Difference between HPV-positive and HPV-negative non-Oropharyngeal Head and neck Cancer: texture analysis features on CT. J Comput Assist Tomogr. 2016;40(1):43–7.

    Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Bogowicz M, Riesterer O, Ikenberg K, Stieb S, Moch H, Studer G, et al. Computed tomography Radiomics predicts HPV status and local tumor control after definitive Radiochemotherapy in Head and neck squamous cell carcinoma. Int J Radiat Oncol Biol Phys. 2017;99(4):921–8.

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Huang C, Cintra M, Brennan K, Zhou M, Colevas AD, Fischbein N, et al. Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes. EBioMedicine. 2019;45:70–80.

    Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Leijenaar RT, Bogowicz M, Jochems A, Hoebers FJ, Wesseling FW, Huang SH, et al. Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study. Br J Radiol. 2018;91(1086):20170498.

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Mungai F, Verrone GB, Pietragalla M, Berti V, Addeo G, Desideri I, et al. CT assessment of tumor heterogeneity and the potential for the prediction of human papillomavirus status in oropharyngeal squamous cell carcinoma. Radiol Med. 2019;124(9):804–11.

    Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Parmar C, Leijenaar RT, Grossmann P, Rios Velazquez E, Bussink J, Rietveld D, et al. Radiomic feature clusters and prognostic signatures specific for lung and Head & Neck cancer. Sci Rep. 2015;5:11044.

    Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Ranjbar S, Ning S, Zwart CM, Wood CP, Weindling SM, Wu T, et al. Computed tomography-based texture analysis to determine human papillomavirus status of Oropharyngeal squamous cell carcinoma. J Comput Assist Tomogr. 2018;42(2):299–305.

    Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Yu K, Zhang Y, Yu Y, Huang C, Liu R, Li T, et al. Radiomic analysis in prediction of human papilloma virus status. Clin Transl Radiat Oncol. 2017;7:49–54.

    Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Zhu Y, Mohamed ASR, Lai SY, Yang S, Kanwar A, Wei L, et al. Imaging-genomic study of Head and neck squamous cell carcinoma: associations between Radiomic phenotypes and genomic mechanisms via integration of the Cancer genome atlas and the Cancer imaging archive. JCO Clin Cancer Inform. 2019;3:1–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Chen Y, Yao H, Thompson EJ, Tannir NM, Weinstein JN, Su X. VirusSeq: software to identify viruses and their integration sites using next-generation sequencing of human cancer tissue. Bioinformatics. 2013;29(2):266–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Lewis JS Jr, Beadle B, Bishop JA, Chernock RD, Colasacco C, Lacchetti C, et al. Human papillomavirus testing in Head and neck carcinomas: guideline from the College of American Pathologists. Arch Pathol Lab Med. 2018;142(5):559–97.

    Article  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Vallieres M, Kumar A, Sultanem K, El Naqa I. FDG-PET Image-Derived Features Can Determine HPV Status in Head-and-Neck Cancer. Int J Radiation Oncol Biol Phys. 2013;87(2):S467.

    Article  Google Scholar 

  61. 61.

    Payabvash S. Quantitative diffusion magnetic resonance imaging in head and neck tumors. Quant Imaging Med Surg. 2018;8(10):1052–65.

    Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Payabvash S, Brackett A, Forghani R, Malhotra A. Differentiation of lymphomatous, metastatic, and non-malignant lymphadenopathy in the neck with quantitative diffusion-weighted imaging: systematic review and meta-analysis. Neuroradiology. 2019;61(8):897–910.

    Article  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Payabvash S, Chan A, Jabehdar Maralani P, Malhotra A. Quantitative diffusion magnetic resonance imaging for prediction of human papillomavirus status in head and neck squamous-cell carcinoma: a systematic review and meta-analysis. Neuroradiol J. 2019;32(4):232–40.

    Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Zwirner K, Hilke FJ, Demidov G, Socarras Fernandez J, Ossowski S, Gani C, et al. Radiogenomics in head and neck cancer: correlation of radiomic heterogeneity and somatic mutations in TP53, FAT1 and KMT2D. Strahlenther Onkol. 2019;195(9):771–9.

    Article  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Walter V, Yin X, Wilkerson MD, Cabanski CR, Zhao N, Du Y, et al. Molecular subtypes in head and neck cancer exhibit distinct patterns of chromosomal gain and loss of canonical cancer genes. PLoS One. 2013;8(2):e56823.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biol. 2015;16:17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Moskovitz J, Moy J, Ferris RL. Immunotherapy for Head and neck squamous cell carcinoma. Curr Oncol Rep. 2018;20(2):22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Taube JM, Klein A, Brahmer JR, Xu H, Pan X, Kim JH, et al. Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. Clin Cancer Res. 2014;20(19):5064–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Ferris RL, Blumenschein G Jr, Fayette J, Guigay J, Colevas AD, Licitra L, et al. Nivolumab for recurrent squamous-cell carcinoma of the Head and neck. N Engl J Med. 2016;375(19):1856–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Larkins E, Blumenthal GM, Yuan W, He K, Sridhara R, Subramaniam S, et al. FDA approval summary: Pembrolizumab for the treatment of recurrent or metastatic Head and neck squamous cell carcinoma with disease progression on or after platinum-containing chemotherapy. Oncologist. 2017;22(7):873–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Oliva M, Spreafico A, Taberna M, Alemany L, Coburn B, Mesia R, et al. Immune biomarkers of response to immune-checkpoint inhibitors in head and neck squamous cell carcinoma. Ann Oncol. 2019;30(1):57–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Patel SP, Kurzrock R. PD-L1 expression as a predictive biomarker in Cancer immunotherapy. Mol Cancer Ther. 2015;14(4):847–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Chen RY, Lin YC, Shen WC, Hsieh TC, Yen KY, Chen SW, et al. Associations of tumor PD-1 ligands, Immunohistochemical studies, and textural features in (18)F-FDG PET in squamous cell carcinoma of the Head and neck. Sci Rep. 2018;8(1):105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127(12):2893–917.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87–108.

    Article  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Gatta G, Botta L, Sanchez MJ, Anderson LA, Pierannunzio D, Licitra L, et al. Prognoses and improvement for head and neck cancers diagnosed in Europe in early 2000s: the EUROCARE-5 population-based study. Eur J Cancer. 2015;51(15):2130–43.

    Article  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Brouha XD, Tromp DM, De Leeuw JR, Hordijk GJ, Winnubst JA. Increasing incidence of advanced stage head and neck tumours. Clin Otolaryngol Allied Sci. 2003;28(3):231–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Bogowicz M, Leijenaar RTH, Tanadini-Lang S, Riesterer O, Pruschy M, Studer G, et al. Post-radiochemotherapy PET radiomics in head and neck cancer - the influence of radiomics implementation on the reproducibility of local control tumor models. Radiother Oncol. 2017;125(3):385–91.

    Article  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Bogowicz M, Tanadini-Lang S, Guckenberger M, Riesterer O. Combined CT radiomics of primary tumor and metastatic lymph nodes improves prediction of loco-regional control in head and neck cancer. Sci Rep. 2019;9(1):15198.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Bogowicz M, Tanadini-Lang S, Veit-Haibach P, Pruschy M, Bender S, Sharma A, et al. Perfusion CT radiomics as potential prognostic biomarker in head and neck squamous cell carcinoma. Acta Oncol. 2019;58(10):1514–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Cheng NM, Fang YH, Chang JT, Huang CG, Tsan DL, Ng SH, et al. Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma. J Nucl Med. 2013;54(10):1703–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Cheng NM, Fang YH, Lee LY, Chang JT, Tsan DL, Ng SH, et al. Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer. Eur J Nucl Med Mol Imaging. 2015;42(3):419–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Cozzi L, Franzese C, Fogliata A, Franceschini D, Navarria P, Tomatis S, et al. Predicting survival and local control after radiochemotherapy in locally advanced head and neck cancer by means of computed tomography based radiomics. Strahlenther Onkol. 2019;195(9):805–18.

    Article  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Feliciani G, Fioroni F, Grassi E, Bertolini M, Rosca A, Timon G, et al. Radiomic profiling of Head and neck Cancer: (18)F-FDG PET texture analysis as predictor of patient survival. Contrast Media Mol Imaging. 2018;2018:3574310.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Folkert MR, Setton J, Apte AP, Grkovski M, Young RJ, Schoder H, et al. Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics. Phys Med Biol. 2017;62(13):5327–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Kuno H, Qureshi MM, Chapman MN, Li B, Andreu-Arasa VC, Onoue K, et al. CT texture analysis potentially predicts local failure in Head and neck squamous cell carcinoma treated with Chemoradiotherapy. AJNR Am J Neuroradiol. 2017;38(12):2334–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Leijenaar RT, Carvalho S, Hoebers FJ, Aerts HJ, van Elmpt WJ, Huang SH, et al. External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. Acta Oncol. 2015;54(9):1423–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Lv W, Ashrafinia S, Ma J, Lu L, Rahmim A. Multi-level multi-modality fusion radiomics: application to PET and CT imaging for prognostication of head and neck cancer. IEEE J Biomed Health Inform. 2019. [Epub ahead of print].

  89. 89.

    Mo X, Wu X, Dong D, Guo B, Liang C, Luo X, et al. Prognostic value of the radiomics-based model in progression-free survival of hypopharyngeal cancer treated with chemoradiation. Eur Radiol. 2020;30:833–843.

  90. 90.

    Ou D, Blanchard P, Rosellini S, Levy A, Nguyen F, Leijenaar RTH, et al. Predictive and prognostic value of CT based radiomics signature in locally advanced head and neck cancers patients treated with concurrent chemoradiotherapy or bioradiotherapy and its added value to human papillomavirus status. Oral Oncol. 2017;71:150–5.

    Article  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Ouyang FS, Guo BL, Zhang B, Dong YH, Zhang L, Mo XK, et al. Exploration and validation of radiomics signature as an independent prognostic biomarker in stage III-IVb nasopharyngeal carcinoma. Oncotarget. 2017;8(43):74869–79.

    Article  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Ulrich EJ, Menda Y, Boles Ponto LL, Anderson CM, Smith BJ, Sunderland JJ, et al. FLT PET Radiomics for response prediction to Chemoradiation therapy in Head and neck squamous cell Cancer. Tomography. 2019;5(1):161–9.

    Article  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Zhai TT, Langendijk JA, van Dijk LV, Halmos GB, Witjes MJH, Oosting SF, et al. The prognostic value of CT-based image-biomarkers for head and neck cancer patients treated with definitive (chemo-)radiation. Oral Oncol. 2019;95:178–86.

    Article  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Zhang B, Ouyang F, Gu D, Dong Y, Zhang L, Mo X, et al. Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics. Oncotarget. 2017;8(42):72457–65.

    PubMed  PubMed Central  Google Scholar 

  95. 95.

    Zhang B, He X, Ouyang F, Gu D, Dong Y, Zhang L, et al. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett. 2017;403:21–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Zhang B, Tian J, Dong D, Gu D, Dong Y, Zhang L, et al. Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res. 2017;23(15):4259–69.

    Article  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Guha A, Connor S, Anjari M, Naik H, Siddiqui M, Cook G, et al. Radiomic analysis for response assessment in advanced head and neck cancers, a distant dream or an inevitable reality? A systematic review of the current level of evidence. Br J Radiol. 2019;93:20190496.

    Article  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Bernier J, Cooper JS, Pajak TF, van Glabbeke M, Bourhis J, Forastiere A, et al. Defining risk levels in locally advanced head and neck cancers: a comparative analysis of concurrent postoperative radiation plus chemotherapy trials of the EORTC (#22931) and RTOG (# 9501). Head Neck. 2005;27(10):843–50.

    Article  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Cooper JS, Zhang Q, Pajak TF, Forastiere AA, Jacobs J, Saxman SB, et al. Long-term follow-up of the RTOG 9501/intergroup phase III trial: postoperative concurrent radiation therapy and chemotherapy in high-risk squamous cell carcinoma of the head and neck. Int J Radiat Oncol Biol Phys. 2012;84(5):1198–205.

    Article  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Bernier J, Domenge C, Ozsahin M, Matuszewska K, Lefebvre JL, Greiner RH, et al. Postoperative irradiation with or without concomitant chemotherapy for locally advanced head and neck cancer. N Engl J Med. 2004;350(19):1945–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Oosting SF, Haddad RI. Best practice in systemic therapy for Head and neck squamous cell carcinoma. Front Oncol. 2019;9:815.

    Article  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Sethia R, Yumusakhuylu AC, Ozbay I, Diavolitsis V, Brown NV, Zhao S, et al. Quality of life outcomes of transoral robotic surgery with or without adjuvant therapy for oropharyngeal cancer. Laryngoscope. 2018;128(2):403–11.

    Article  PubMed  PubMed Central  Google Scholar 

  103. 103.

    Mermod M, Tolstonog G, Simon C, Monnier Y. Extracapsular spread in head and neck squamous cell carcinoma: a systematic review and meta-analysis. Oral Oncol. 2016;62:60–71.

    Article  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Patel MR, Hudgins PA, Beitler JJ, Magliocca KR, Griffith CC, Liu Y, et al. Radiographic imaging does not reliably predict macroscopic Extranodal extension in human papilloma virus-associated Oropharyngeal Cancer. ORL J Otorhinolaryngol Relat Spec. 2018;80(2):85–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Maxwell JH, Rath TJ, Byrd JK, Albergotti WG, Wang H, Duvvuri U, et al. Accuracy of computed tomography to predict extracapsular spread in p16-positive squamous cell carcinoma. Laryngoscope. 2015;125(7):1613–8.

    Article  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Kann BH, Aneja S, Loganadane GV, Kelly JR, Smith SM, Decker RH, et al. Pretreatment identification of Head and neck Cancer nodal metastasis and Extranodal extension using deep learning neural networks. Sci Rep. 2018;8(1):14036.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Kann BH, Hicks DF, Payabvash S, Mahajan A, Du J, Gupta V, et al. Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma. J Clin Oncol. 2019:JCO1902031. [Epub ahead of print].

  108. 108.

    Dirix P, Nuyts S. Evidence-based organ-sparing radiotherapy in head and neck cancer. Lancet Oncol. 2010;11(1):85–91.

    Article  PubMed  PubMed Central  Google Scholar 

  109. 109.

    Hawkins PG, Lee JY, Mao Y, Li P, Green M, Worden FP, et al. Sparing all salivary glands with IMRT for head and neck cancer: longitudinal study of patient-reported xerostomia and head-and-neck quality of life. Radiother Oncol. 2018;126(1):68–74.

    Article  PubMed  PubMed Central  Google Scholar 

  110. 110.

    Sheikh K, Lee SH, Cheng Z, Lakshminarayanan P, Peng L, Han P, et al. Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands. Radiat Oncol. 2019;14(1):131.

    Article  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Liu Y, Shi H, Huang S, Chen X, Zhou H, Chang H, et al. Early prediction of acute xerostomia during radiation therapy for nasopharyngeal cancer based on delta radiomics from CT images. Quant Imaging Med Surg. 2019;9(7):1288–302.

    Article  PubMed  PubMed Central  Google Scholar 

  112. 112.

    van Dijk LV, Thor M, Steenbakkers R, Apte A, Zhai TT, Borra R, et al. Parotid gland fat related magnetic resonance image biomarkers improve prediction of late radiation-induced xerostomia. Radiother Oncol. 2018;128(3):459–66.

    Article  PubMed  PubMed Central  Google Scholar 

  113. 113.

    van Dijk LV, Brouwer CL, van der Schaaf A, Burgerhof JGM, Beukinga RJ, Langendijk JA, et al. CT image biomarkers to improve patient-specific prediction of radiation-induced xerostomia and sticky saliva. Radiother Oncol. 2017;122(2):185–91.

    Article  PubMed  PubMed Central  Google Scholar 

  114. 114.

    van Dijk LV, Noordzij W, Brouwer CL, Boellaard R, Burgerhof JGM, Langendijk JA, et al. (18)F-FDG PET image biomarkers improve prediction of late radiation-induced xerostomia. Radiother Oncol. 2018;126(1):89–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. 115.

    U.S. Department of Health and Human Services NIH NCI. Common Terminology Criteria for Adverse Events (CTCAE) Version 4.0 2010 [cited 2019 December 1st]. Available from:

    Google Scholar 

  116. 116.

    Bjordal K, Ahlner-Elmqvist M, Tollesson E, Jensen AB, Razavi D, Maher EJ, et al. Development of a European Organization for Research and Treatment of Cancer (EORTC) questionnaire module to be used in quality of life assessments in head and neck cancer patients. EORTC quality of life study group. Acta Oncol. 1994;33(8):879–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. 117.

    Epstein JB, Thariat J, Bensadoun RJ, Barasch A, Murphy BA, Kolnick L, et al. Oral complications of cancer and cancer therapy: from cancer treatment to survivorship. CA Cancer J Clin. 2012;62(6):400–22.

    Article  PubMed  PubMed Central  Google Scholar 

  118. 118.

    Rapidis AD, Dijkstra PU, Roodenburg JL, Rodrigo JP, Rinaldo A, Strojan P, et al. Trismus in patients with head and neck cancer: etiopathogenesis, diagnosis and management. Clin Otolaryngol. 2015;40(6):516–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. 119.

    Thor M, Tyagi N, Hatzoglou V, Apte A, Saleh Z, Riaz N, et al. A magnetic resonance imaging-based approach to quantify radiation-induced normal tissue injuries applied to trismus in head and neck cancer. Phys Imaging Radiat Oncol. 2017;1:34–40.

    Article  PubMed  PubMed Central  Google Scholar 

  120. 120.

    Abdollahi H, Mostafaei S, Cheraghi S, Shiri I, Rabi Mahdavi S, Kazemnejad A. Cochlea CT radiomics predicts chemoradiotherapy induced sensorineural hearing loss in head and neck cancer patients: a machine learning and multi-variable modelling study. Phys Med. 2018;45:192–7.

    Article  PubMed  PubMed Central  Google Scholar 

  121. 121.

    Langlotz CP, Allen B, Erickson BJ, Kalpathy-Cramer J, Bigelow K, Cook TS, et al. A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/the academy workshop. Radiology. 2019;291(3):781–91.

    Article  PubMed  PubMed Central  Google Scholar 

  122. 122.

    Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol. 2019;20(3):405–10.

    Article  PubMed  PubMed Central  Google Scholar 

  123. 123.

    Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26(6):1045–57.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


Not applicable.


There was no funding for this review article.

Author information




SPH and SP searched literature. SPH, BB, WY, and SP wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Seyedmehdi Payabvash.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Haider, S.P., Burtness, B., Yarbrough, W.G. et al. Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas. Cancers Head Neck 5, 6 (2020).

Download citation


  • Radiomics
  • Machine learning
  • Neck
  • Oropharynx
  • Squamous cell carcinoma