Table 2.

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

Title	Methodology	Performance	Dataset
Deep learning–based detection of solid and cystic pancreatic neoplasms at contrast-enhanced CT [29▪]	Park et al. developed a three-dimensional nnU-Net-based deep learning model for automatically identifying patients with various solid and cystic pancreatic neoplasms in abdominal CT scan	AUC of 0.91 in test set 1 and 0.87 in test set 2; comparable sensitivity to radiologists for solid and cystic lesions	852 patients in the training set, 603 in test set 1, and 589 in test set 2, CT images
Design of optimal deep learning-based pancreatic tumor and nontumor classification model using computed tomography scans [30▪]	Althobaiti et al. developed an optimal deep learning-based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images, which includes adaptive window filtering for noise removal, sailfish optimizer-based Kapur's thresholding for segmentation, Capsule Network for feature extraction, and Political Optimizer with Cascade Forward Neural Network	Achieved sensitivity, specificity, accuracy, and F-score of 98.73%, 97.75%, 98.40%, and 98.82%, respectively	the benchmark BioGPS dataset [41], CT images
Pancreatic cancer detection on CT scans with deep learning: a nationwide population-based study [31▪▪]	Chen et al. developed an end-to-end deep learning tool for pancreatic cancer detection on CT scans, comprising a segmentation CNN for pancreas localization and a classifier ensemble of five CNNs for cancer identification	89.9% sensitivity, 95.9% specificity (internal test set); 89.7% sensitivity, 92.8% specificity (real-world test set); 74.7% sensitivity for malignancies <2 cm	Retrospectively collected contrast-enhanced CT studies from 546 pancreatic cancer patients and 733 control subjects between January 2004 and December 2019, CT images
Intelligent deep-learning-enabled decision-making medical system for pancreatic tumor classification on CT images [32▪]	Vaiyapuri et al. proposed an intelligent deep-learning-enabled decision-making medical system for pancreatic tumor classification (IDLDMS-PTC) using CT images, which includes an emperor penguin optimizer with multilevel thresholding (EPO-MLT) for segmentation, MobileNet for feature extraction, and optimal autoencoder (AE) with multileader optimization (MLO) for classification	The IDLDMS-PTC model achieved an average sensitivity of 0.9935, specificity of 0.9884, accuracy of 0.9935, and F-score of 0.9948	Various sources, a total of 500 images, with 250 images under pancreatic tumor and 250 images under nonpancreatic tumor, CT images
Neural transformers for classification of intraductal papillary mucinous neoplasm (IPMN) using MRI [33▪▪]	The authors proposed an AI-based IPMN classifier using a transformer-based architecture, specifically ViT, which employs the encoder of the original transformer model on a sequence of image patches	Achieved an accuracy of 0.7 ± 0.11, precision of 0.67 ± 0.19, and recall of 0.64 ± 0.12	139 MRI scans from distinct patients
The FELIX project: deep networks to detect pancreatic neoplasms [34▪▪]	The FELIX Project presents a suite of deep learning algorithms designed to recognize pancreatic lesions from CT images without human input. The deep networks were developed to detect pancreatic neoplasms, particularly PDACs.	>95% specificity and >95% sensitivity. The models also showcase the ability to generalize to other institutions and other pancreatic tumor types	Collected ∼2000 CT abdominal images, including healthy, PDAC, PanNET and cyst
Towards a single unified model for effective detection, segmentation, and diagnosis of eight major cancers using a large collection of CT scans [35▪]	Chen et al. Developed a Unified Tumor Transformer (UniT) model for detecting and diagnosing eight major cancers in CT scans using a query-based Mask Transformer with multiorgan and multitumor semantic segmentation	95% sensitivity for pancreatic cancer	10 042 patients with CT images of eight cancer types and noncancer tumors; 631 patients in the test set
Meta-information-aware dual-path transformer for differential diagnosis of multitype pancreatic lesions in multiphase CT [36▪]	The study introduces a meta-information-aware dual-path transformer for pancreatic lesion classification and segmentation	The method outperforms previous baselines and approaches to the accuracy of radiology reports.	3096 patients with multiphase CT scans

CNN, convolutional neural network; PDAC, pancreatic ductal adenocarcinoma.