Copyright ©The Author(s) 2021.
Artif Intell Gastroenterol. Apr 28, 2021; 2(2): 10-26
Published online Apr 28, 2021. doi: 10.35712/aig.v2.i2.10
Table 1 Target volume and organs at risk contouring with artificial intelligence
Number of patients
Imaging method
Artificial intelligence method
Wang et al[54], 201893MR (3 Tesla, T2 -weighted)GTV, CTVCNNBetween deep learning-based autosegmentation and manual contouring DSC (P = 0.42), JSC (P = 0.35), HD (P = 0.079), and ASD (P = 0.16); Before postprocess process only in HD (P = 0.0027).
Trebeschi et al[55], 2017140Multiparametric MRI (1.5 Tesla, T2- weighted)GTVCNNAccording to CNN and both radiologists in independent validation data set DSC: 0.68 and 0.70; For both radiologists AUC: 0.99.
Song et al[56], 2020199CT (3 mm section thickness)CTV and OARCNNs (DeepLabv3+ and ResUNet)CTV segmentation better with DeepLabv3+ than ResUNet (volumetric DSC, 0.88 vs 0.87, P = 0.0005; surface DSC, 0.79 vs 0.78, P = 0.008); DeepLabv3+ model segmentation was better in the small intestine, with the ResUNet model, bladder and femoral heads segmentation results were better. In both models, the OAR manual correction time was 4 min.
Men et al[60], 2017278CT (5 mm section thickness)CTV and OARCNN (DDCNN)DSC values; CTV: 87.7%, bladder: 93.4%, left femoral head: 92.1%, right femoral head: 92.3%, small intestine: 65.3%, colon 61.8%.
Men et al[61], 2018100CT (3 mm section thickness)CTV and OARCNNCTV and bladder contouring were better in the model trained in the same position than the model trained in a different position (P < 0.05). No statistically significant difference between femoral heads (P > 0.05). No statistical difference between accuracy rates in CTV, bladder, and femoral heads segmentation in the model trained in both positions (P > 0.05).
Table 2 Studies of chemoradiotherapy response prediction with artificial intelligence
Number of patients
Parameters evaluated
Imaging method
Technique used
Shi et al[71], 201951 (90% cases for training and the remaining 10% for testing)Tumor volume, mean ADC, radiomicMRI (Pre-CRT and mid-CRT) (T2-DWI, DCE)CNN(1) pCR response prediction: (a) Pre-CRT with MR AUC: 0.80; (b) Mid-CRT with MR AUC: 0.82; and (c) Pre- and mid-CRT MR together AUC: 0.86; and (2) Good response to CRT: predicting yes/no: (a) Pre-CRT with MR AUC: 0.91; (b) Mid-CRT with MR AUC: 0.92; and (c) Pre-- and mid-CRT MR together AUC: 0.93.
Fu et al[73], 202043RadiomicMRI (Pre-CRT, DWI)Handcrafted traditional computer-aided diagnostic method vs deep learningDeep learning model with handcrafted model CRT response prediction AUC values: 0.64 vs 0.73 (P < 0.05)
Shayesteh et al[74], 201998 (53 training and 45 validation set)RadiomicMRI (1 wk before CRT) (3 Tesla, T2W-weighted)Machine learning (SVM, BN, NN, KNN)AUC for the BN algorithm: 74%, accuracy: 79%; When four algorithms were used together, AUC: 97.8% and accuracy rate 92.8%.
Yang et al[75], 201989 (66 training and 23 testing)Radiomic and clinical featuresMRI (Pre-CRT) (3 Tesla, T2W, 3 mm section thickness)RFCPredicting the accuracy of tumor resistance with RFC 91.3%, AUC: 0.83.
Ferrari et al[76], 201955 (28 training, 27 validation)RadiomicMR (Pre, Mid, Post RT) (3 Tesla, T2W, 2 mm section thickness)RFC(1) Prediction of cases with pCR by RFC; AUC: 0.86; and (2) Prediction of unresponsive cases with RFC; AUC 0.83.
Bibault et al[77], 201895Radiomic, clinical variablesCTDNN, SVM, LRCRT response prediction accuracy rates; DNN: 80%; SVM: 71.5% LR: 69.5%.
Huang et al[78], 2020270 (236 training, 34 validation)Clinical variables-ANN, KNN, SVM, NBC, MLRpCR prediction accuracy rates and AUC values; ANN: 88%, 0.84 KNN: 80%, 0.74 SVM: 71%, 0.76 NBC: 80%, 0.63 MLR: 83%, 0.77.