Use of artificial intelligence-powered spatial analysis of tumor microenvironment to predict the prognosis in resected gallbladder cancer.
4137 Background: Gallbladder cancer (GBC) is a highly lethal disease with a lack of reliable biomarkers. The tumor microenvironment (TME) is closely associated with prognosis, but its clinical application as a prognostic marker is limited by evaluation challenges. This study assessed the prognostic significance of AI-powered TME analysis in resected GBC patients. Methods: A total of 225 GBC patients with an R0 resection were enrolled, and their hematoxylin & eosin (H&E)-stained GBC sections were analyzed using Lunit SCOPE IO, an artificial intelligence (AI)-powered whole-slide image (WSI) analyze, to evaluate TME-related features, including tumor-infiltrating lymphocyte (TIL) density, fibroblast (FB) density, and tertiary lymphoid structure (TLS) counts. Risk stratification was based on TME-related risk factors (low TIL, high FB, low TLS), and survival outcomes were assessed. External validation was conducted using 146 biliary tract cancer patients. Results: Overall survival (OS) and disease-free survival (DFS) declined as the number of TME-related risk factors increased. Patients with three risk factors had the poorest outcomes (median OS: 17.7 months [reference]; median DFS: 12.7 months [reference]), followed by those with two risk factors (median OS: 115.9 months, HR = 0.40, 95% CI: 0.19–0.85; median DFS: 57.8 months, HR = 0.37, 95% CI: 0.18–0.74) and one risk factor (median OS: 126.5 months, HR = 0.34, 95% CI: 0.16–0.74; median DFS: 117.2 months, HR = 0.30, 95% CI: 0.15–0.62). Patients with no risk factors had the best survival (median OS: not reached, HR = 0.20, 95% CI: 0.06–0.67; median DFS: not reached, HR = 0.13, 95% CI: 0.04–0.41). External validation confirmed consistent trends across all risk groups. Conclusions: AI-powered TME analysis shows promise as a practical tool for identifying TME-related risk factors using H&E-stained WSI, providing valuable prognostic information for resected GBC patients.
Authors
- H. J. Kim (ORCID: https://orcid.org/0000-0001-9787-4684)
- Kwang Hyuck Lee
- Young Hoon Choi (ORCID: https://orcid.org/0000-0002-2633-1401)
- Kee‐Taek Jang (ORCID: https://orcid.org/0000-0001-7987-4437)
- In Woong Han (ORCID: https://orcid.org/0000-0001-7093-2469)
- Chang Ho Ahn (ORCID: https://orcid.org/0000-0002-0702-0608)
- Yeong Hak Bang (ORCID: https://orcid.org/0000-0001-6544-6431)
- Sang Hyun Shin (ORCID: https://orcid.org/0000-0002-2533-4491)
- Yo Han Jeon (ORCID: https://orcid.org/0000-0002-0353-7255)
- Kyu Taek Lee
- Jinny Park (ORCID: https://orcid.org/0009-0005-3186-3591)
- Changhoon Yoo (ORCID: https://orcid.org/0000-0002-1451-8455)
- Jin Seok Heo
- Song Eun Yoon (ORCID: https://orcid.org/0000-0002-3972-7288)
- Soohyun Hwang
- Hongbeom Kim
- Cheolyong Joe
- Sangwon Shin
- Se-Hoon Lee
- Jong Kyun Lee (ORCID: https://orcid.org/0000-0003-1665-0326)
Institutions
- Ulsan College (KR)
- Samsung (South Korea) (KR)
- Asan Medical Center (KR)
- Samsung Medical Center (KR)
- University of Ulsan (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Journal of Clinical Oncology
- Published
- 2025-05-28
- DOI
- https://doi.org/10.1200/jco.2025.43.16_suppl.4137
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00