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Table 5 Reports on outcome prediction of SBRT in lung cancer from analysis of radiomic features

From: Radiomics for prediction of radiation-induced lung injury and oncologic outcome after robotic stereotactic body radiotherapy of lung cancer: results from two independent institutions

Author N Modality/features (software applied) # features selected Model type Outcome measures Validation Result/comment
Huynh [24] 113 CT:1605 (in-house software) 12 + clinical Survival analysis, cc-index Recurrence, Distant mets., OS Single institution cross validation Risk for recurrence: no significant features Risk for dist. metastases: 1 sign. Feature OS: 4 significant features, cc = 0.67
Li [28, 29] 92 CT: 219 (Definiens Developer) 8–68 + clinical + semantic ROC-analysis Recurrence, RFS, OS Single institution cross-validation Risk stratification: AUC = 0.69–0.75
Zhang [58] 112 CT: 30 (ProCanVAS) dependent on model 8 models: Random forest GLM, SVM etc Recurrence, Distal failure, OS Single institution cross validation Risk stratification: AUC = 0.60–0.77
Yu [34] 442 CT: 12 (IBEX) 2 Random survival forests Regional recurrence, OS Single institution test set: 67% OS risk stratification: p = 0.017 Recurrence risk stratification: p < 0.05 2 sign. features: kurtosis, homogeneity
Li [30] 110 CT + FDG-PET (learned by model) from model Kernelled support tensor machine Distant failure Single institution test set: 30% Risk stratification: AUC = 0.80
Oikonomou [31] 150 CT + FDG-PET 2 × 21 (ProCanVAS) 6–8, 4 from PCA PCA, logistic regression Local control, Distant control, DSS, OS Single institution cross validation Risk stratification: p = 0.004–0.02 features: heterogeneity and morphology
Starkov [32] 116 CT: 2D-textures from solid core and GGO 2–30 Cox regression lasso PFS, distant failure Single institution cross validation Risk stratification: p = 0.03 dependent on wavelet filtering
Lafata [26] 70 CT: 43 2 Logistic regression regularized Local recurrence none Risk stratification: p = 0.048 features: density
Franceschini [23] 102 CT: 41 (LifeX) 4–6 Cox regression elastic net, back selection Nodal relapse, PFS, DSS Single institution Test set: 32% Nodal Relapse: accuracy = 85% PFS: 53 vs.45 months features: heterogeneity
Lou [59] 944* CT learned by model CNN, Multivariate competing risk Local recurrence Multi institution test set: 10% Risk stratification: p < 0.002
Baek [21] 122 CT + FDG-PET
2 × 55,296
Features from k-medoids pool CNN (U-Net) logistic regression OS Independent institution test set: 21% Risk stratification: AUC = 0.87
  1. ProCanVAS prostate cancer visualization and analysis system, PCA principal component analysis, cc-index: concordance index, RFS Recurrence-free survival, ROC receiver-operator-characteristics; validation by independent test sets shown in bold
  2. *Includes recurrent lung cancers and pulmonary metastases
  3. #Features from PET and CT