ArticleCancer medicine2025
Prognostic Value of Deep Learning-Extracted Tumor-Infiltrating Lymphocytes in Esophageal Cancer: A Multicenter Retrospective Cohort Study.
Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Development of an artificial intelligence-based computer-aided detection system for routine gastric biopsy diagnosis.Journal of pathology informatics · 2026Article
- Multiple autologous tumor-infiltrating lymphocyte (LM103 infusion) therapy combined with immune checkpoint inhibitor induces repeated tumor regression in a patient with aggressive mucosal melanoma: a case report and literature review.Frontiers in oncology · 2026Article
- Prognostic Value of Deep Learning-Extracted Tumor-Infiltrating Lymphocytes in Esophageal Cancer: A Multicenter Retrospective Cohort Study.Cancer medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundTumor-infiltrating lymphocytes (TILs) have been proven to be important prognostic factors for various tumors. However, their prognostic significance within the context of esophageal squamous cell carcinoma (ESCC) remains inadequately explored. This study aims to assess the prognostic potential of TILs in ESCC using deep learning (DL) methods. MATERIALS AND
methodsWe retrospectively enrolled 626 pathologically confirmed ESCC patients from two research centers. Their digital whole-slide imaging (WSI) and corresponding clinical information were collected. Subsequently, the DL method was employed to identify the tumor margin and TILs within the WSI. Tissue was divided into intratumor, peritumoral, and stromal regions based on their distance from the tumor margin. TILs were counted in each region. Optimal cut-off values of TILs were determined using the X-tile software. To mitigate selection bias and intergroup heterogeneity, a propensity score matching (PSM) analysis was employed. Survival analysis was performed using Kaplan-Meier curves and the log-rank test. The Cox proportional hazards regression model was used to identify independent prognostic factors.
resultsWe classified patients based on the cell counts and cut-off values of intratumor-infiltrating lymphocytes (I-TILs) and peritumoral infiltrating lymphocytes (P-TILs). Patients with high I-TILs and P-TILs were defined as those whose counts of both I-TILs and P-TILs exceeded the determined cutoff value. Patients with high I-TILs and P-TILs showed significantly better overall survival (OS, p = 0.0092) and recurrence-free survival (RFS, p = 0.0088) than patients with low I-TILs and P-TILs after PSM. Multivariable Cox proportional hazards regression further supported this conclusion and recognized I-TILs and P-TILs as independent prognostic factors (p = 0.0136, hazard ratio = 0.63 for OS; p = 0.0098, hazard ratio = 0.63 for RFS).
conclusionIn the present study, we identified the quantitative distribution of TILs in ESCC patients with the help of the DL method. We established that I-TILs and P-TILs serve as independent prognostic factors for these patients. Further studies should focus on the lymphocyte subgroups and make better use of the spatial information to improve the predictive efficacy of TILs.
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.