ArticleJHEP reports : innovation in hepatology2025
AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection.
Article in JHEP reports : innovation in hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Integrated multi-task learning framework for hepatocellular carcinoma segmentation and histological grading using fused multi-phase MRI.Abdominal radiology (New York) · 2026Article
- HBV reprograms the tumor microenvironment in hepatocellular carcinoma: mechanisms and therapeutic implications.Clinical and experimental medicine · 2026Review
- Dendritic cells in cancer immunotherapy: functional barriers, reprogramming strategies and translational challenges.Frontiers in immunology · 2026Review
- LY-H as a potential screening biomarker for lymphocyte activation in peripheral blood.Frontiers in immunology · 2026Article
- Mechanisms of ferroptosis in primary hepatocellular carcinoma and progress of artificial intelligence-based predictive modeling in hepatocellular carcinoma.World journal of gastroenterology · 2025Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background & Aims: Tumor-infiltrating lymphocytes (TILs), particularly CD8+ TILs, are key prognostic markers in many cancers. However, their prognostic value in hepatocellular carcinoma (HCC) remains controversial, with different evidence. Given the heterogeneous outcomes in patients with HCC undergoing liver resection, this study aims to develop an AI-based system to quantify CD8+ TILs and assess their prognostic value for patients with HCC. Methods: We conducted a retrospective multicenter study on patients undergoing liver resection across three cohorts (N = 514). We trained a deep neural network and a random forest model to segment tumor regions and locate CD8+ TILs in H&E and CD8-stained whole-slide images. We quantified CD8+ TIL density and established an Automated CD8+ Tumor-infiltrating Lymphocyte Scoring (ATLS-8) system to assess its prognostic value. Results: In the discovery cohort, the 5-year overall survival (OS) rates were 34.05% for ATLS-8 low-score and 65.03% for ATLS-8 high-score groups (hazard ratio [HR] 2.40; 95% CI, 1.37-4.19; Conclusions: We developed an automated system using CD8-stained whole-slide images to assess immune infiltration (ATLS-8). In patients with HCC undergoing resection, higher CD8+ TIL density correlates with better OS, as per ATLS-8 assessment. This system is a promising tool for advancing clinical immune microenvironment assessment and outcome prediction. Impact and implications: CD8+ tumor-infiltrating lymphocytes (TILs) have been identified as a prognostic factor associated with many cancers. In this study, CD8+ TILs were identified as an independent prognostic factor for overall survival in patients with hepatocellular carcinoma who undergoing liver resection. Therefore, ATLS-8, a novel digital biomarker based on whole-slide image-level CD8+ TILs, could play an important role in the prognostic assessment of patients with HCC and could be integrated into clinicopathological models to participate in the decision-making and prognostic assessment of patients. The scoring system combined with artificial intelligence is essential for automated, quantitative, whole-slide image-level assessment of TILs, which can be widely applied to quantify the immune profile of multi-cancer disease types with the discussion of subsequent immunotherapy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.