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
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, They Are Data. Radiology. 2016;278(2):563–77.
Article
PubMed
Google Scholar
Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol. 2016;61(13):R150–66.
Article
CAS
PubMed
PubMed Central
Google Scholar
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
Google Scholar
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
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
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
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
Google Scholar
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
Google Scholar
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
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
Google Scholar
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
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
Google Scholar
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
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
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
Zwanenburg A, Leger S, Vallières M, Löck S. Image biomarker standardisation initiative. arXiv e-prints. 2016;2016 Available from: https://ui.adsabs.harvard.edu/abs/2016arXiv161207003Z.
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
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
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
Google Scholar
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
Google Scholar
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
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
Google Scholar
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
Google Scholar
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
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
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
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
Google Scholar
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
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
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
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
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
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
Google Scholar
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
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
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
Google Scholar
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
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
Google Scholar
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
Google Scholar
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
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
Google Scholar
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. https://doi.org/10.15761/OHNS.1000160.
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
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
Google Scholar
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
Google Scholar
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
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
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
Google Scholar
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
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
Google Scholar
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
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
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
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
Google Scholar
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
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
Google Scholar
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
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
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
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
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
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
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
Google Scholar
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
Google Scholar
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
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
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
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
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
Google Scholar
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].
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. https://doi.org/10.1007/s00330-019-06452-w.
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
Google Scholar
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
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
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
Google Scholar
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
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
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
Google Scholar
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
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
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].
Dirix P, Nuyts S. Evidence-based organ-sparing radiotherapy in head and neck cancer. Lancet Oncol. 2010;11(1):85–91.
Article
PubMed
Google Scholar
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
Google Scholar
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
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
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
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
Google Scholar
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
Google Scholar
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: https://ctep.cancer.gov/protocolDevelopment/electronic_applications/ctc.htm#ctc_40.
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
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