---

About

Interfaces

This algorithm implements all of the following input-output combinations:

Challenge Performance

Date	Challenge	Phase	Rank
Jan. 23, 2025	PANORAMA	Development Phase - Tuning	6
Feb. 25, 2025	PANORAMA	Testing Phase	4

Model Facts

Summary

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with poor prognosis, underscoring the critical need for early and accurate diagnosis. We present a novel two-stage pipeline for PDAC detection from contrast-enhanced computed tomography (CECT) scans, leveraging the nnU-Net framework for segmentation and a highresolution multi-task convolutional neural network (CNN) for joint lesion segmentation and classification. The first stage employs a low-resolution nnU-Net to segment the pancreas region, while the second stage refines segmentation and performs lesion classification.

Mechanism

This algorithm produces a likelihood score for the presence of pancreatic ductal adenocarcinoma (PDAC) in an input venous-phase contrast-enhanced computed tomography scan (CECT), together with a detection map with the lesion localization. we introduce a two-stage pipeline for pancreas region of interest (ROI) localization followed by joint cancer segmentation and classification. The localization is based on a low-resolution nnU-Net model and we further extend it to multitask network by adding a classification head. The algorithm was trained with 2238 images from the PANORAMA public development data set.

For more details: https://github.com/bowang-lab/PANORAMA/blob/main/Panorama_report.pdf

Validation and Performance

Uses and Directions

This algorithm was developed for research purposes only.

Warnings

Common Error Messages

Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3