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

Table 3 Summary of studies using deep-learning-based radiomics for liver cancer

Ref.	Imaging	Study design	Study aim	DL model	Dataset	Outcomes
Ding et al[44], 2021	CT	Retrospective	Evaluation of HCC differentiation	VGG19	1234 patients (799 in training cohort, 248 in validation cohort; 187 in independent testing cohort)	AUC: 0.8042; accuracy: 72.73%; sensitivity: 70.75%; specificity: 75.31% in testing cohort for the fused DLRs model
Peng et al[45], 2020	CT	Retrospective	Prediction of different treatment responses to TACE in HCC patients	ResNet50	789 patients (562 in training cohort; 89 and 138 in validation cohorts 1 and 2)	Accuracy: 85.1% in validation cohort 1; accuracy: 82.8% in validation cohort 2
Peng et al[46], 2021	CT	Retrospective	Prediction of initial response to TACE in HCC patients	CNN	310 patients (139 in training cohort; 171 in validation cohort)	AUC: 0.994
Wei et al[47], 2021	CT	Retrospective	Prediction of OS of HCC patients treated with SBRT	CNN	167 patients	C-index: 0.650 in cross validation
Liu et al[48], 2020	US	Retrospective	Prediction of PFS of HCC patients treated with RFA or surgical resection	CNN	214 RFA patients (149 for training; 65 for validation), 205 SR patients (144 for training; 61 for validation)	C-index of RFA: 0.726; C-index of surgical resection: 0.726
Wang et al[49], 2019	CT	Retrospective	Prediction of early recurrence of HCC patients	ResNet	167 patients	AUC of best model: 0.825
Wang et al[50], 2020	CT	Retrospective	Prediction of early recurrence of HCC patients	ResNet	167 patients	For the best model with joint loss function, AUC: 0.8331; accuracy: 80.49%
He et al[51], 2021	MRI and pathological data	Retrospective	Evaluation of HCC recurrence risk of liver transplantation recipients	U-net, CapsNet	109 patients (87 for training; 22 for testing)	Total accuracy: 82%; recall: 80%; precision: 89%; AUC: 0.87; F-1 score: 84%
Jiang et al[52], 2021	CT	Retrospective	Prediction of microvascular invasion status of HCC patients	3D-CNN	405 patients (324 in training set, 81 in validation set)	AUC: 0.906; sensitivity: 75.7%; specificity: 93.2%; accuracy: 85.2%; F-1 score: 87.2% in validation set
Wang et al[53], 2022	CT	Retrospective	Prediction of microvascular invasion status of HCC patients	Transformer, CNN	138 patients	For arterial phase images in validation set, AUC: 0.9223; Average accuracy: 86.78%
Fu et al[54], 2021	CT	Retrospective	Prediction of macrovascular invasion status in HCC patients	Modified U-Net	366 patients (281 in training cohort, 85 in validation cohort)	AUC: 0.836 in validation cohort

CT: Computed tomography; MRI: Magnetic resonance imaging; US: Ultrasound; HCC: Hepatocellular carcinoma; TACE: Transarterial chemoembolization; PFS: Progression-free survival; RFA: Radiofrequency ablation; SBRT: Stereotactic body radiation therapy; CNN: Convolutional neural network; AUC: Area under the receiver operating characteristic curve; C-index: Harrell’s concordance index; DLR: Deep-learning-based radiomics.