AI and Cancer Research ── Pancreatic ── 2026-10-19

1 Pancreatic papers. Lead: Deep-learning–based automated multimodal fusion network using multiphoton imaging for predicting lymph-node metastasis in pancreatic ductal adenocarcinoma

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Pancreatic cancer: treatment, trials and AI

Deep-learning–based automated multimodal fusion network using multiphoton imaging for predicting lymph-node metastasis in pancreatic ductal adenocarcinoma
Biomedical Signal Processing and Control · 2026-10-17
Abstract
Lymph-node metastasis is a key factor that influences clinical outcomes in patients with pancreatic ductal adenocarcinoma (PDAC), and its accurate and timely identification is essential for optimizing therapeutic strategies and improving patient prognosis. However, the need for extensive screening of pathological scans makes its diagnosis challenging. Multiphoton microscopy (MPM) is a powerful tool for label-free, two- and three-dimensional visualizations of tissue, which exhibits considerable promise in pancreatic-tumor diagnostics. The automation of diagnostic processes has substantially alleviated the workload of pathologists, while maintaining high accuracy. This study aims to develop an efficient deep-learning framework for the automatic prediction of lymph-node metastasis in PDAC patients, using MPM images. We evaluate the performance of four deep-learning models: VGG16, ResNet50, MobileNetv2, and pyramid vision transformer version 2 (PVTv2). Based on the superior and stable performance of PVTv2, we develop a combined model, named automatic lymph-node–metastasis classification network (AutoLNMClassNet), which integrates PVTv2-derived features with collagen and clinical features through a multilayer perceptron. The proposed-model’s performance is evaluated in terms of the accuracy, positive/negative predictive values, sensitivity, and specificity. AutoLNMClassNet demonstrates superior performance, achieving an accuracy of 0.922 and area under the curve of 0.969, outperforming other classification models. These results indicate that AutoLNMClassNet offers high reliability and accuracy in detecting lymph-node metastases, thereby serving as a valuable tool for the diagnosis and treatment of PDAC.
Journal IF-equivalent: 5.1 (OpenAlex 2-year mean citedness, value as of 2026-10-18, retrieved 2026-10-19; not the official Clarivate IF)
Reference: Xi G, Huang Y, He J, Wang G, Zhang J, Chen L, et al. Deep-learning–based automated multimodal fusion network using multiphoton imaging for predicting lymph-node metastasis in pancreatic ductal adenocarcinoma. Biomedical Signal Processing and Control. 2027 Mar;131:111667. doi:10.1016/j.bspc.2026.111667.
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