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

Table 1 Summary of studies using deep-learning-based radiomics for esophageal cancer

Ref.	Imaging	Study design	Study aim	DL model	Dataset	Outcomes
Takeuchi et al[23], 2021	CT	Retrospective	Detection of esophageal cancer	VGG16	1646 CT images (1500 images for training and validation, 146 for testing)	Accuracy: 84.2%; F value: 74.2%; Sensitivity: 71.1%; Specificity: 90%) in test set
Hu et al[24], 2021	CT	Retrospective	Evaluation of response to NCRT to ESCC	ResNet50	231 patients (161 in training cohort, 70 in testing cohort)	AUC: 0.805; C-index: 0.805; Accuracy: 77.1%; Sensitivity: 83.9%; Specificity: 71.8%) for the testing cohort
Ypsilantis et al[25], 2015	PET	Retrospective	Prediction of response to NAC in patients with esophageal cancer	3S-CNN	107 patients	Sensitivity: 80.7%; Specificity: 81.6%; Accuracy: 73.4%
Amyar et al[26], 2019	PET	Retrospective	Prediction of response to radio-chemotherapy in patients with esophageal cancer	3D RPET-NET	97 patients	Accuracy: 75.0%; Sensitivity: 76.0%; Specificity: 74.0%; AUC: 0.74
Li et al[27], 2021	CT	Retrospective	Prediction of treatment response to CCRT among patients with locally advanced TESCC	ResNet34	306 patients (203 in training cohort, 103 in validation cohort)	AUC: 0.833; PPV: 100%
Wang et al[28], 2022	CT	Retrospective	Prediction of survival rates for patients with esophageal cancer after 3 yr with chemoradiotherapy	DenseNet- 169	154 patients (116 in training cohort, 38 in validation cohort)	AUC: 0.942; C-index: 0.784
Yang et al[29], 2019	PET	Retrospective	Identification of esophageal cancer patients with poor prognosis	3D-CNN based on ResNet18	1107 scans	AUC: 0.738
Gong et al[30], 2022	CECT	Retrospective	Prediction of LRFS in esophageal cancer patients after 1 yr of definitive chemoradiotherapy	3D-Densenet	397 patients	C-index: 0.76
Wu et al[31], 2019	CT	Retrospective	Prediction of LN status of patients with ESCC	CNN-F	411 patients	C-index: 0.840

CT: Computed tomography; PET: Positron emission tomography; CECT: Contrast-enhanced computed tomography; NCRT: Neoadjuvant chemoradiotherapy; NAC: Neoadjuvant chemotherapy; ESCC: Esophageal squamous cell carcinoma; CCRT: Concurrent chemo-radiation therapy; TESCC: Thoracic esophageal squamous cell carcinoma; LRFS: Local recurrence-free survival; LN: Lymph node; CNN-F: Convolutional neural network-fast; AUC: Area under the receiver operating characteristic curve; PPV: Positive predictive value; DLR: Deep-learning-based radiomics.