# PanDx: AI-assisted Early Detection of Pancreatic Ductal Adenocarcinoma on Contrast-enhanced CT

arXiv: https://arxiv.org/abs/2503.10068 · HTML: https://mlsanity.com/paper/2503.10068/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 1 of 1)

- [Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography](https://mlsanity.com/paper/2111.15409/) (2021)

## Built on by (most cited first, 2 of 2)

- [Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification](https://mlsanity.com/paper/2512.12887/) (2025)
- [From Global Radiomics to Parametric Maps: A Unified Workflow Fusing Radiomics and Deep Learning for PDAC Detection](https://mlsanity.com/paper/2602.17986/) (2026)

## Abstract (arXiv)

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive forms of pancreatic cancer and is often diagnosed at an advanced stage due to subtle early imaging signs. To enable earlier detection and improve clinical decision-making, we propose a coarse-to-fine AI-assisted framework named PanDx for identifying PDAC on contrast-enhanced CT scans. Our approach integrates two novel techniques: (1) distribution-aware stratified ensembling to improve generalization across lesion variations, and (2) peak-scaled lesion candidate extraction to enhance lesion localization precision. PanDx is developed and evaluated as part of the PANORAMA challenge, where it ranked 1st place on the official test set with an AUROC of 0.9263 and an AP of 0.7243. Furthermore, we analyzed failure cases with a radiologist to identify the limitations of AI models on this task and discussed potential future directions for model improvement. Our code and models are publicly available at https://github.com/han-liu/PanDx.