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

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

Authors, yearDataset: sample size, cancer typeTime of xerostomia assessment, endpoint/scaleImaging modalityVOIClassifier / regression model(s)Metric: maximum performance a
Sheikh et al. 2019 [110]Train:216, HNSCC
Test:50, HNSCC
3-month post-RT, CTCAE v4.0 b grade ≥ 2 vs. grade 0/1Pre-treatment CT, T1-weighted MRIParotid and submandibular glands (bilateral)Multivariable logistic regressionCV-AUC: 0.75
test-AUC: 0.70
Liu et al.
2019 [111]
Train:35, NPC
Test:4, NPC
day of 10th and 30th RT,
saliva amount (ml) over 5 min (a regression analysis)
CT at start and day of 10th RT fractionParotid glands (bilateral)8 different regression modelsCV-MSE: 0.9042 (10th fraction), 0.0569 (30th fraction)
test-MSE: 0.0233 (30th fraction)
van Dijk et al. 2018 [112]Train:68, HNSCC
Test:25, HNSCC
12 moth post-RT, patient-rated moderate-to-severe xerostomia present vs. not presentPre-treatment T1-weighted MRIParotid glands, (bilateral)Multivariable logistic regressionn/a c
van Dijk et al. 2017 [113]Total: 249, HNSCC12 moth post-RT, EORTC QLQ-H, N35 questionnaire d moderate-to-severe xerostomia vs. not presentPre-treatment contrast CTParotid and submandibular glands (bilateral)Multivariable logistic regressionn/a c
van Dijk et al. 2018 [114]Total: 161, HNSCC12-month post-RT,
EORTC QLQ-H questionnaire d moderate-to-severe xerostomia present vs. not present
Pre-treatment FDG PETContralateral parotid glandMultivariable logistic regressionn/a c
  1. a The reported performance pertains to the maximum observed performance among the purest imaging feature-based models reported (i.e. the best model with fewest or no other predictors is reported)
  2. b Common Terminology Criteria for Adverse Events Version 4.0 [115]
  3. c “Pure” radiomics models were not built. Instead, the contribution of individual radiomics features to baseline models was investigated in terms of performance (gains)
  4. d European Organization for Research and Treatment of Cancer questionnaire module for quality of life assessments in head and neck cancer patients [116]
  5. AUC Area under the receiver operating characteristics curve, CV Cross validation (of total set or training data set), HNSCC Head and neck SCC, MSE Mean squared error, NPC Nasopharyngeal carcinoma, RT Radiotherapy, Test Independent test data set, Total Only one data set used, Train Training data set