Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview

Table 2 Summary of studies using deep-learning-based radiomics for gastric cancer

Ref.	Imaging	Study design	Study aim	DL model	Dataset	Outcomes
Cui et al[32], 2022	CT	Retrospective	Prediction of response to NAC in patients with LAGC	DenseNet-121	719 patients	C-index: 0.829
Li et al[33], 2022	CT	Retrospective	Diagnosis and prediction of chemotherapy response to SRCC patients	Modified U-Net	855 patients (598 in training cohort; 257 in testing cohort)	For diagnosis, AUC: 0.786; accuracy: 71.6%; sensitivity: 77.3%; specificity: 69.2% for testing cohort
Tan et al[34], 2020	CT	Retrospective	Prediction of response to chemotherapy in patients with gastric cancer	V-Net	116 patients	Mean AUC: 0.728 (testing cohort); 0.828 (validation cohort) when using semi-segmentation
Hao et al[35], 2021	CT	Retrospective	Prediction of OS and PFS after gastrectomy; evaluation of effects of variables on survival prediction	Attention-guided VAE	1061 patients (743 for training; 318 for testing)	C-index of OS: 0.783; C-index of PFS: 0.770 when only using postoperative variables
Zhang et al[36], 2021	CT	Retrospective	Prediction of OS risks of patients with gastric cancer	MMF-FPN	640 patients (337 in training set; 181 in validation set; 122 in test set)	C-index: 0.76; hazard ratio: 9.46 in test set
Zhang et al[37], 2020	CT	Retrospective	Prediction of early recurrence of patients with AGC	DCNNs	669 patients	AUC: 0.806; accuracy: 0.723; sensitivity: 0.827; specificity: 0.667
Guan et al[38], 2022	CT	Retrospective	Prediction of preoperative status of LNM of gastric cancer patients	ResNet50-RF	347 patients (242 for training; 105 for testing)	AUC: 0.9803; accuracy: 98.10%; sensitivity: 98.39%; specificity: 0.9767% for testing of ResNet50-RF
Dong et al[39], 2020	CT	Retrospective	Prediction of the number of LNM in LAGC	DenseNet-201	730 patients	C-index: 0.822 in validation set
Li et al[40], 2020	CT	Retrospective	Prediction of LNM and prognosis in gastric cancer patients	DCNNs	204 patients (136 in training set, 68 in test set)	AUC: 0.82 in test set; C-index of OS: 0.67; C-index of PFS: 0.64
Jin et al[41], 2021	CT	Retrospective	Prediction of LNM status in LN stations of gastric cancer patients	ResNet-18	1699 patients	Median AUC: 0.876; median Sensitivity: 0.743; median Specificity: 0.936 in validation cohort
Sun et al[42], 2020	CT	Stage I: Retrospective; stage II: Validation	Prediction of serosa invasion of AGC patients	DCNNs	572 patients (252 in training set, 176 in test set I, 144 in test set II)	AUC: 0.87; accuracy: 80%; sensitivity: 0.73; specificity: 0.85 in test set I. AUC: 0.90; accuracy: 85%; sensitivity: 0.75; specificity: 0.93 in test set II
Li et al[43], 2022	CT	Retrospective	Evaluation of lymphovascular invasion of localized gastric cancer patients	SqueezeNet, ResNet50, Inception V3, VGG19, DeepLoc	1062 patients (728 for training, 334 for testing)	AUC: 0.725; sensitivity: 73.2%; specificity: 60.3%; accuracy: 71.0% for radiomics GRISK model (final model) in testing cohort

CT: Computed tomography; NAC: Neoadjuvant chemotherapy; LAGC: Locally advanced gastric cancer; SRCC: Signet ring cell carcinoma; OS: Overall survival; PFS: Progression-free survival; AGC: Advanced gastric cancer; LNM: Lymph node metastasis; LN: Lymph node; DCNNs: Deep convolutional neural networks; MMN-FPN: Multi-focus and multi-level fusion feature pyramid network; UC: Area under the receiver operating characteristic curve; C-index: Harrell’s concordance index; GRISK: Gastric Risk; RF: Random forest; VAE: Variational auto-encoder; DLR: Deep-learning-based radiomics.