Table 1.

Recent advances in deep learning for pancreatic cancer diagnosis based on imaging. Ground truth diagnoses in the dataset are usually based on pathology data.

Title	Methodology	Performance	Dataset
Retrospective Analysis of the Value of Enhanced CT Radiomics Analysis in the Differential Diagnosis Between Pancreatic Cancer and Chronic Pancreatitis [10]	Ma et al. built a multivariable logistic regression model based on selected radiomics features, combined with clinical features and established a nomogram	An AUC of 0.980, with a sensitivity of 94.7% and specificity of 91.7%.	A retrospective dataset of 151 pancreatic cancer cases and 24 chronic pancreatitis cases, CT images
Predicting PDAC using artificial intelligence analysis of pre-diagnostic computed tomography images [11]	Ahmad et al. developed a naïve Bayes classifier for the automatic classification of CT scans after selecting features potentially predictive of PDAC	Average accuracy of 86%	108 retrospective CT scans from 72 subjects, with 36 scans each from healthy control, pre-diagnostic, and diagnostic groups
Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Pre-diagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis [12]	A total of 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method. Four ML classifiers were evaluated: KNN, SVM, RF and XGBoost	SVM achieved the highest sensitivity (95.5%), specificity (90.3%), F1-score (89.5%), AUC (0.98), and accuracy (92.2%)	155 pre-diagnostic CT scans of PDAC patients and 265 age-matched CT scans of subjects with normal pancreas
Applying a radiomics-based CAD scheme to classify between malignant and benign pancreatic tumors using CT images [13]	Gai et al. developed a radiomics-based CAD scheme for CT images, including preprocessing, segmentation, feature extraction and classification (SVM)	AUC of 0.75	A retrospective dataset of 77 patients with suspicious pancreatic tumors detected on CT images, including 33 malignant tumors
Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs): an MRI-based radiomic model to determine the malignant degeneration potential [14]	Flammia et al. aimed to create an MRI-based radiomic model to identify features linked to a higher risk of malignant degeneration in BD-IPMNs. Features that showed significant differences were included in a LASSO regression method to build a radiomics-based predictive model.	The radiomic-based predictive model identified 16 significant features, including 5 for T1-W, 6 for post-contrast T1-W, 3 for T2-W, and 2 for the apparent diffusion coefficient	50 patients with BD-IPMN, MRI images
Radiomics Analysis for Predicting Malignant Potential of Intraductal Papillary Mucinous Neoplasms of the Pancreas: Comparison of CT and MRI [15]	Intraclass correlation coefficients were calculated to assess interobserver reproducibility. The least absolute shrinkage and selection operator algorithm was used for feature selection. Radiomics models were constructed based on selected features with logistic regression (LR) and SVM.	MRI radiomics models achieved better AUCs (0.879 with LR and 0.940 with SVM) than CT radiomics models (0.811 with LR and 0.864 with SVM)	60 patients with surgically confirmed IPMNs (37 malignant and 23 benign), both CT and MRI