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

Table 4 Summary of studies using deep-learning-based radiomics for pancreatic cancer

Ref.	Imaging	Study design	Study aim	DL model	Dataset	Outcomes
Ziegelmayer et al[55], 2020	CT	Retrospective	Identification of PDAC and AIP	VGG19	86 patients (44 AIP patients and 42 PDAC patients)	Sensitivity: 89%; specificity: 83%; AUC: 0.90
Liao et al[56], 2022	CT	Retrospective	Identification of PDAC, non-cancerous pancreatic diseases and normal pancreas	CNN	3120 images (1872 for training, 624 for validation, 624 for testing)	Sensitivity: 89.9%; specificity: 91.3%; AUC: 0.960 when distinguishing between PDAC and control group
Tong et al[57], 2022	US	Retrospective	Identification of PDAC and CP	ResNet-50	558 patients	AUC: 0.967; sensitivity: 87.2%; specificity: 100%
Watson et al[58]	CT	Retrospective	Prediction of pathologic response of PDAC patients to NAC	LeNet	81 patients (65 for training and validation; 16 for testing)	AUC: 0.785; brier score: 0.174; sensitivity: 81.4%; specificity: 60.4% in test set of hybrid deep learning model
Muhammad et al[59], 2018	CT	Retrospective	Evaluation of survival hazard of PDAC patients	AlexNet	159 patients	C-index: 0.76; hazard ratio: 9.46 in test set
Zhang et al[60], 2020	CT	Retrospective	Evaluation of survival probability of PDAC patients	CNN	520 patients	IPA: 11.81%, C-index: 0.651 in testing cohort
Zhang et al[61], 2020	CT	Retrospective	Prediction of OS of PDAC patients; Evaluation of risk scores to distinguish patients with high or low risk	CNN	98 patients (68 in training cohort; 30 in testing cohort)	AUC: 0.81; hazard ratio: 1.86
Zhang et al[62], 2021	CT	Retrospective	Prediction of 2-yr OS of resectable PDAC patients	CNN	98 patients (68 in training cohort; 30 in testing cohort)	AUC: 0.84; specificity: 68%; sensitivity: 91%
Yao et al[63], 2021	CT	Retrospective	Prediction of survival risk and tumor resection margin of resectable PDAC patients	CNN	205 patients	C-index: 0.705 for survival prediction; balanced accuracy: 73.6%, sensitivity: 81.3%, specificity: 65.9% for resection margin prediction
Yao et al[63], 2021	CT	Retrospective	Prediction of survival risk and tumor resection margin of resectable PDAC patients	CNN	1209 patients	C-index: 0.667 for survival prediction; balanced accuracy: 67.1%; sensitivity: 59.8%; specificity: 74.3% for resection margin prediction
An et al[64], 2022	CT	Retrospective	Prediction of LNM status and OS in PDAC patients	ResNet-18	148 patients (88 in training cohort, 25 in validation cohort, 35 in testing cohort)	For combined model, AUC: 0.92; accuracy: 86%; sensitivity: 94%; specificity: 78% in testing cohort
Li et al[65], 2019	CT	Retrospective	Prediction of HMGA2 and C-MYC gene expression status of PDAC patients;Prediction of survival time of patients	CNN	111 patients	Average AUC score: 0.90; accuracy: 95%; sensitivity: 92%; specificity: 98% in C-MYC test with deep features selected by Doctor B; average AUC score: 0.91; accuracy: 88%; sensitivity: 89%; specificity: 88% in HMGA2 test with deep features selected by Doctor B

CT: Computed tomography; US: Ultrasound; PDAC: Pancreatic ductal adenocarcinoma; AIP: Autoimmune pancreatitis; CP: Chronic pancreatitis; NAC: Neoadjuvant chemotherapy; LNM: Lymph node metastasis; OS: Overall survival; AUC: Area under the receiver operating characteristic curve; C-index: Harrell’s concordance index; CNN: Convolutional neural network; IPA: Index of prediction accuracy; DLR: Deep-learning-based radiomics.