Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning

Vincent Ochs, Christoph Kuemmerli, Florentin Bieder, Julia Wolleb, Joël L. Lavanchy, Julia Ruppel, Jan Liechti, Stephanie Taha-Mehlitz, Christian A. Nebiker, Beat P. Müller-Stich, Giuseppe Kito Fusai, Jörg-Matthias Pollok, Anas Taha, Philippe C. Cattin, Sebastian Manuel Staubli

Keywords: PDAC, Multi-Modality Model, Adaptive Loss Schedule, Classification.

TL;DR: A multimodal deep learning model that combines CT imaging and clinical data to automatically predict NCCN-based resectability in pancreatic cancer.

Abstract:

Accurate determination of pancreatic ductal adenocarcinoma (PDAC) resectability relies on evaluating how the tumor interacts with major peripancreatic vessels on CT imaging, yet expert assessment often shows substantial variability. We introduce a fully automated multimodal deep learning framework that jointly analyzes 3D contrast enhanced CT and structured clinical information to classify patients into the three National Comprehensive Cancer Network (NCCN) resectability categories (upfront resectable, borderline resectable, locally advanced). The approach uses a Swin-UNETR backbone to obtain anatomy aware image representations through auxiliary segmentation of pancreas, tumor, and vascular structures. These features are fused with a compact clinical embedding derived from 17 routinely collected variables and processed by a lightweight classification head. Model training is guided by a dynamic multitask objective that adapts the balance between segmentation and classification based on current tumor Dice performance, promoting feature representations that remain both anatomically informed and discriminative.

In a cohort of 159 patients (85 upfront resectable, 47 borderline resectable, 27 locally advanced), the proposed method achieved an AUC of 0.86, a macro-F1 of 0.79, and an accuracy of 0.85 using stratified nested 5-fold cross validation, outperforming adapted transformer based and geometric baseline approaches. Because segmentation labels are required only during training, the final system enables mask free inference while preserving vessel aware interpretability. These findings demonstrate that integrating anatomical supervision with clinical context yields a robust and reproducible tool for supporting operability (i.e., NCCN-based resectability) assessment in pancreatic cancer.

Primary Subject Area: Integration of Imaging and Clinical Data

Secondary Subject Area: Detection and Diagnosis

Registration Requirement: Yes

Reproducibility: https://github.com/vincentochs/pancreas_resectability

Visa & Travel: No

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Submission Number: 9