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

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

Author, year

Dataset: sample size, HNSCC type

Imaging modality

Therapy

Outcome, model, analysis type

(Endpoint:) variables: metric: maximum performance a

Aerts et al.

2014 [18]

Test: 231, HNSCC

(HNSCC cohort used for validation only, training on 422 lung cancer primaries)

Pre-treatment contrast CT

RT + chemotherapy or RT + chemotherapy + surgery or RT alone

OS,

multivariable Cox regression,

regression

Radiom:

test-CI 1:0.69

test-CI 2: 0.69

Radiom+Clin:

test-CI 1: 0.70

test-CI 2: 0.69

Clin:

test-CI 1: 0.69

test-CI 2: 0.66

Bogowicz et al.

2017 [78]

Train: 128, HNSCC

Test: 50, HNSCC

3-month post treatment FDG-PET

RT + cisplatin / cetuximab or RT + cisplatin + cetuximab

LC,

multivariable Cox regression,

regression

Radiom:

CV-CI: 0.74–0.76

test-CI: 0.71–0.73

(study evaluated reproducibility of identical features using two different software - performance range is reported)

Bogowicz et al.

2017 [25]

Train: 121, HNSCC

Test: 51, HNSCC

Pre-treatment FDG-PET, contrast CT

RT + cisplatin / cetuximab or RT + cisplatin + cetuximab

LC,

3 different regression methods,

regression

Radiom:

CV-CI: 0.77

test-CI: 0.73

Bogowicz et al.

2017 [50]

Train: 93, HNSCC

Test: 56, HNSCC

Pre-treatment contrast CT

RT + cisplatin / cetuximab

LC,

multivariable Cox regression,

regression

Radiom:

train-CI: 0.75

test-CI: 0.78

Clin:

train-CI: 0.79

test-CI: 0.73

Radiom + Clin:

train-CI: 0.80

test-CI: 0.76

Bogowicz et al.

2019 [79]

Train: 77, HNSCC

Test: 51, HNSCC

Pre-treatment contrast CT

RT + cisplatin / cetuximab or RT + cisplatin + cetuximab

LC and LRC,

multivariable Cox regression,

regression

LC:

Radiom:

CV-CI: 0.81

test-CI: 0.70

LRC:

Radiom:

CV-CI: 0.75

test-CI: 0.67

(addition of lymph node to primary tumor radiomics features was investigated –the best performance reported)

Bogowicz et al.

2019 [80]

Total: 45, HNSCC

Pre-treatment perfusion CT

IMRT + cisplatin / cetuximab or IMRT + cisplatin + cetuximab

LC,

multivariable Cox regression,

regression

Radiom:

CV-CI: 0.79

Clin:

CV-CI: 0.66

Cheng et al.

2013 [81]

Total: 70, OPSCC

Pre-treatment FDG-PET

RT + platinum-based chemotherapy / cetuximab or RT alone

DSS, OS and PFS,

multivariate Cox regression,

regression

DSS:

Radiom:

HR: 0.28 (p = 0.001)

OS:

Radiom:

HR: 0.46 (p = 0.017)

PFS:

Radiom:

HR: 0.32 (p = 0.001)

(Only 1 radiomics feature was tested in multivariate Cox regression, along with clinicopathological and FDG-PET variables)

Cheng et al.

2015 [82]

Total: 88, OPSCC

Pre-treatment FDG-PET

RT + chemotherapy / biotherapy or RT alone

PFS and DSS,

multivariate Cox regression,

regression

PFS:

Radiom:

HR: 4.38 (p = 0.002)

DSS:

Radiom:

HR: 4.24 (p = 0.005)

(single radiomics features were tested in multivariate Cox regression, along with clinicopathological and FDG-PET variables)

Cozzi et al.

2019 [83]

Train: 70, HNSCC

Test: 40, HNSCC

Pre-treatment contrast CT

RT + chemotherapy or RT + chemotherapy + induction-chemotherapy

OS, PFS and LC,

multivariable Cox regression, regression

OS:

Radiom:

train-CI: 0.88

test-CI: 0.90

PFS:

Radiom:

train-CI: 0.72

test-CI: 0.80

LC:

Radiom:

train-CI: 0.72

test-CI: 0.80

Feliciani et al. 2018 [84]

Total: 90, HNSCC

Pre-treatment FDG-PET

IMRT + platinum-based chemotherapy w/ or w/o adjuvant / neoadjuvant chemotherapy

PFS and OS,

multivariate Cox regression,

regression

PFS:

Radiom + Clin:

CV-CI: 0.76

Clin:

CV-CI: 0.65

OS:

Radiom + Clin:

CV-CI: 0.76

Clin:

CV-CI: 0.73

Folkert et al. 2017 [85]

Train: 174, OPSCC

Test: 65, OPSCC

Pre-treatment FDG-PET

RT + platinum-based chemotherapy / cetuximab / multidrug regimens

ACM, LF and DM,

multivariable logistic regression,

classification

ACM:

Radiom + Clin:

CV-AUC: 0.65

test-AUC: 0.60

LF:

Radiom + Clin:

CV-AUC: 0.73

test-AUC: 0.68

DM:

Radiom + Clin:

CV-AUC: 0.66

test-AUC: 0.65

Ger et al.

2019 [35]

Train 1: 377, HNSCC (CT)

Train 2: 345, HNSCC (PET)

Test 1: 349, HNSCC (CT)

Test 2: 341, HNSCC (PET)

Pre-treatment contrast CT, pre-treatment FDG-PET (separately analyzed)

Not reported (definitive RT as part of treatment was inclusion criterion)

OS,

multivariable Cox regression,

regression

Radiom:

test-AUC 1: 0.72 (CT)

test-AUC 2: 0.59 (PET)

Clin:

test-AUC 1: 0.73 (CT)

(AUC was calculated at 3 years post treatment, with patients with risk prediction > median assigned to the high-risk group)

Kuno et al. 2017 [86]

Total: 62, HNSCC

Pre-treatment contrast CT

IMRT + chemotherapy w/ or w/o induction chemotherapy or IMRT alone

LF,

multivariate Cox regression,

regression

Radiom:

HR: 3.75–8.61

(8 features were significant after adjusting for clinical variables; the HR range is reported above)

Leger et al. 2017 [30]

Train: 213, HNSCC

Test: 80, HNSCC

Pre-treatment non-contrast CT

RT + chemotherapy

LRC and OS,

11 different ML algorithms,

regression

LRC:

Radiom:

test-CI: 0.71

OS:

Radiom:

test-CI: 0.64

Leijenaar et al. 2015 [87]

Test: 542, OPSCC

(validation of radiomics signature by Aerts et al. [18])

Pre-treatment contrast CT

IMRT + chemotherapy or IMRT alone

OS,

multivariable Cox regression,

regression

Radiom:

test-CI: 0.63

Liu et al.

2016 [28]

Train: 42, NPC

Test: 11, NPC

Pre-treatment T2, contrast-enhanced T1 MRI, diffusion weighted MRI

RT + cisplatin

Therapy response (complete/partial response vs. stable / progressive disease),

k-nearest neighbors, neural network,

classification

Radiom:

CV-acc: 0.95

CV-sens: 0.97

CV-spec: 091

test-acc: 0.91

test-sens: 0.88

test-spec: 1

Lv et al.

2019 [88]

Total: 296, HNSCC

(various partitions in train/test were evaluated)

Pre-treatment non-contrast CT, FDG-PET

RT + chemotherapy or RT alone

RFS, MFS and OS,

multivariate Cox regression,

regression

RFS:

Radiom:

mean test-CI: 0.61

Radiom + Clin:

mean test-CI: 0.60

Clin:

mean test-CI: 0.58

MFS:

Radiom:

mean test-CI: 0.70

Radiom + Clin:

mean test-CI: 0.71

Clin:

mean test-CI: 0.61

OS:

Radiom:

mean test-CI: 0.62

Radiom + Clin:

mean test-CI: 0.65

Clin:

mean test-CI: 0.62

(the mean was calculated across all test partitions)

Lv et al.

2019 [24]

Train: 85, NPC

Test: 43, NPC

Pre-treatment CT, FDG-PET

IMRT + cisplatin or IMRT alone

PFS,

multivariate Cox regression,

regression

Radiom:

train-CI: 0.76

test-CI: 0.62

Radiom + Clin:

train-CI: 0.75

test-CI: 0.75

Clin:

train-CI: 0.71

test-CI: 0.75

M.D. Anderson C.C.H.a.N.Q.I.W.G. 2018 [31]

Train: 255, OPSCC

Tune: 165, OPSCC

Test: 45, OPSCC

Pre-treatment contrast CT

One or combinations of: IMRT / chemotherapy / induction chemotherapy / neck dissection

LC,

multivariate Cox regression,

regression

Overall performance evaluation of Cox models not reported

Mo et al.

2019 [89]

Train: 80, HYSCC

Test: 33, HYSCC

Pre-treatment non-contrast CT and contrast-CT

Laryngeal-preservation treatments (RT, chemotherapy, induction-chemotherapy, neck dissection)

PFS,

multivariable Cox regression,

regression

Radiom:

train-CI: 0.79

test-CI: 0.76

Radiom + Clin:

train-CI: 0.80

test-CI: 0.76

Clin:

train-CI: 0.63

test-CI: 0.54

Ou et al.

2017 [90]

Total: 120, HNSCC

Pre-treatment CT

CRT / IMRT + cisplatin / cetuximab

OS and PFS,

multivariable Cox regression,

regression

OS:

Radiom:

HR: 0.3 (p = 0.02)

PFS:

Radiom:

HR: 0.3 (p = 0.01)

Ouyang et al. 2017 [91]

Train: 70, NPC

Test: 30, NPC

Pre-treatment T2, contrast-enhanced T1 MRI

Not reported

PFS,

multivariable Cox regression,

regression

Radiom:

train-HR: 5.14 (p < 0.001)

test-HR: 7.28 (p = 0.015)

Parmar et al. 2015 [29]

Train: 101, HNSCC

Test: 95, HNSCC

Pre-treatment contrast CT

RT + chemotherapy or RT + chemotherapy + surgery or RT alone

OS,

12 different ML classifiers,

classification

Radiom:

test-AUC: 0.79

Parmar et al. 2015 [54]

Train: 136, HNSCC

Test: 95, HNSCC

Pre-treatment contrast CT

RT + chemotherapy or RT + chemotherapy + surgery or RT alone

OS,

multivariable Cox regression,

regression

Radiom:

test-CI: 0.63

Ulrich et al. 2019 [92]

Total: 30, OPSCC and LSCC

Pre-treatment 18F-fluorothymidine PET

RT + platinum-based chemotherapy

PFS,

univariate Cox regression,

regression

Radiom:

HR: 4.10 (p = 0.001)

Vallieres et al. 2017 [23]

Train: 194, HNSCC

Test: 106, HNSCC

Pre-treatment FDG-PET, non-contrast CT

RT + platinum-based chemotherapy / cetuximab or RT alone

LR, DM and OS,

logistic regression, random forests,

classification

(regression analysis was performed for a subset of models; see publication)

LR:

Radiom:

test-AUC: 0.64

Radiom + Clin:

test-AUC: 0.69

DM:

Radiom:

test-AUC: 0.86

Radiom + Clin:

test-AUC: 0.86

OS:

Radiom:

test-AUC: 0.62

Radiom + Clin:

test-AUC: 0.74

Wang et al. 2018 [27]

Total: 120, NPC

Pre-treatment T2, contrast-enhanced T1 MRI

Induction-chemotherapy (cisplatin + 5-fluorouracil + docetaxel)

Early response to induction chemotherapy,

“Rad-score”,

classification

Radiom:

train-AUC: 0.82

internally bootstrap-validated train-AUC: 0.82

Zhai et al. 2019 [93]

Train: 240, HNSCC

Test: 204, HNSCC

Pre-treatment contrast CT

RT + chemotherapy / cetuximab or RT alone

LC, RC, MFS and DFS,

multivariate Cox regression,

regression

LC:

Radiom:

train-CI: 0.62

test-CI: 0.62

Radiom + Clin:

train-CI: 0.66

test-CI: 0.64

Clin:

train-CI: 0.64

test-CI: 0.62

RC:

Radiom:

train-CI: 0.78

test-CI: 0.80

Radiom + Clin:

train-CI: 0.78

test-CI: 0.80

Clin:

train-CI: 0.74

test-CI: 0.76

MFS:

Radiom:

train-CI: 0.73

test-CI: 0.68

Radiom + Clin:

train-CI: 0.72

test-CI: 0.71

Clin:

train-CI: 0.71

test-CI: 0.70

DFS:

Radiom:

train-CI: 0.66

test-CI: 0.65

Radiom + Clin:

train-CI: 0.69

test-CI: 0.70

Clin:

train-CI: 0.66

test-CI: 0.66

Zhang et al. 2017 [94]

Train: 80, NPC

Test: 33, NPC

Pre-treatment T2, contrast-enhanced T1 MRI

Not reported

PFS,

“Rad-score”,

classification

Radiom:

train-AUC: 0.89

test-AUC: 0.82

Zhang et al. 2017 [95]

Train: 70, NPC

Test: 40, NPC

Pre-treatment T2, contrast-enhanced T1 MRI

Not reported

LF and DF,

9 different ML classifiers,

classification

LF and DF:

Radiom:

test-AUC: 0.85

Zhang et al. 2017 [96]

Train: 88, NPC

Test: 30, NPC

Pre-treatment T2, contrast-enhanced T1 MRI

Not reported

PFS,

univariate / multivariable Cox regression,

regression

Radiom:

train-CI: 0.76

test-CI: 0.74

Clin:

train-CI: 0.65

test-CI: 0.63

Radiom + Clin:

train-CI: 0.78

test-CI: 0.72

  1. a The reported performance pertains to the maximum observed performance among all models of each respective category (i.e. we are reporting the highest achieved performance, in case different radiomics features / models / signatures or clinical predictors / models were tested). For radiomics-based models, the performance of the purest imaging feature-based model is reported. (i.e. the model with fewest or no other predictors)
  2. acc Accuracy, ACM All-cause mortality, AUC Area under the receiver operating characteristics curve, CI Concordance index, Clin Non-radiomic predictor(s) or model(s) (“clinical”), CRT Conformal radiotherapy, DF/DM Distant failure/metastasis, DFS Disease-free survival, DSS Disease-specific survival, HNSCC Head and neck SCC, HR Hazard ratio, HYSCC Hypopharyngeal SCC, IMRT Intensity-modulated radiotherapy, LC/LF Local tumor control/failure, LR Locoregional recurrence, LRC Locoregional control, LSCC Laryngeal SCC, MFS Metastasis-free survival, NPC Nasopharyngeal carcinoma, OPSCC Oropharyngeal SCC, OS Overall survival, PFS Progression-free survival, Radiom Radiomics model, radiomic feature(s) or feature combinations (“signature”, “Rad-score”), RC Regional control, RFS Recurrence-free survival, RT Radiotherapy, sens Sensitivity, spec Specificity, test Independent test dataset, total Only one dataset used, train Training dataset, tune Validation set used for hyperparameter tuning
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Cancers of the Head & Neck

ISSN: 2059-7347

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