DTI_PANORAMA

Logo for DTI_PANORAMA

About

Creators:

User Mugshot NataliaAlves 

User Mugshot MountainHill 

Contact email:
han.liu@siemens-healthineers.com
Image Version:
6c176cf6-72a0-427f-a003-7c7b357568d1
Last updated:
Dec. 16, 2024, 4:09 p.m.
Associated publication:
Liu H, Gao R, Grbic S. AI-assisted Early Detection of Pancreatic Ductal Adenocarcinoma on Contrast-enhanced CT. arXiv. Published online March 18, 2025.

Interfaces

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

Inputs Outputs
1
  • Venous phase CT scan (Image)
  • Clinical Information Pancreatic CT (Anything)
  • PDAC Likelihood (Float)
  • PDAC Detection Map (Heat Map)

    • Challenge Performance

    Date Challenge Phase Rank
    Oct. 3, 2024 PANORAMA Development Phase - Sanity Check 1
    Jan. 23, 2025 PANORAMA Development Phase - Tuning 1
    Feb. 25, 2025 PANORAMA Testing Phase 1

    Model Facts

    Summary

    Pancreatic ductal adenocarcinoma (PDAC) is one of the most common and aggressive types of pancreatic cancer. However, due to the lack of early and disease-specific symptoms, most patients with PDAC are diagnosed at an advanced disease stage. Consequently, early PDAC detection is crucial for improving patients’ quality of life and expanding treatment options. In this work, we develop a coarse-to-fine approach to detect PDAC on contrast-enhanced CT scans. First, we localize and crop the region of interest from the low-resolution images, and then segment the PDAC-related structures at a finer scale. Additionally, we develop two strategies to further boost the detection performance, including (1) a data splitting strategy for model ensembling, and (2) a customized post-processing function. We participated in the PANORAMA challenge1 and ranked 1st place for PDAC detection with an AUROC of 0.9263 and an AP of 0.7243. Our code and models are publicly available at https://github.com/han-liu/PDAC_detection.

    Mechanism

    Please refer to our technical report: AI-assisted Early Detection of Pancreatic Ductal Adenocarcinoma on Contrast-enhanced CT (https://arxiv.org/pdf/2503.10068)

    Validation and Performance

    We ranked 1st place in the testing phase of the PANORAMA challenge, with an AUROC of 0.9263 and an AP of 0.7243. More details can be found in our technical report.

    Uses and Directions

    This algorithm was developed for research purposes only.

    Warnings

    Common Error Messages

    If you have any questions, feel free to open an issue at our GitHub page: https://github.com/han-liu/PDAC_Detection

    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