Artificial intelligence in rectal cancer

Table 2 Studies of chemoradiotherapy response prediction with artificial intelligence

Ref.	Number of patients	Parameters evaluated	Imaging method	Technique used	Results
Shi et al[71], 2019	51 (90% cases for training and the remaining 10% for testing)	Tumor volume, mean ADC, radiomic	MRI (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], 2020	43	Radiomic	MRI (Pre-CRT, DWI)	Handcrafted traditional computer-aided diagnostic method vs deep learning	Deep learning model with handcrafted model CRT response prediction AUC values: 0.64 vs 0.73 (P < 0.05)
Shayesteh et al[74], 2019	98 (53 training and 45 validation set)	Radiomic	MRI (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], 2019	89 (66 training and 23 testing)	Radiomic and clinical features	MRI (Pre-CRT) (3 Tesla, T2W, 3 mm section thickness)	RFC	Predicting the accuracy of tumor resistance with RFC 91.3%, AUC: 0.83.
Ferrari et al[76], 2019	55 (28 training, 27 validation)	Radiomic	MR (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], 2018	95	Radiomic, clinical variables	CT	DNN, SVM, LR	CRT response prediction accuracy rates; DNN: 80%; SVM: 71.5% LR: 69.5%.
Huang et al[78], 2020	270 (236 training, 34 validation)	Clinical variables	-	ANN, KNN, SVM, NBC, MLR	pCR 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.

ADC: Apparent diffusion coefficient; ANN: Artificial neural network; AUC: Area under the curve; BN: Bayesian network; CNN: Convolutional neural network; CRT: Chemoradiotherapy; CT: Computed tomography; DCE: Dynamic contrast-enhanced; DNN: Deep neural network; DWI: Diffusion-weighted imaging; KNN: K-nearest neighbors; LR: Linear regression; MLR: Multiple logistic regression; MRI: Magnetic resonance imaging; NBC: Naïve bayes classifier; NN: Neural network; pCR: Pathological complete response; RFC: Random forest classifier; SVM: Support vector machine.