ArticleThe journal of pathology. Clinical research2025
Automated multi-regional IHC scoring enhances prognostication in colorectal cancer.
Article in The journal of pathology. Clinical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Machine Learning in Biomarker-Driven Precision Oncology: Automated Immunohistochemistry Scoring and Emerging Directions in Genitourinary Cancers.Current oncology (Toronto, Ont.) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
The tumor immune microenvironment plays a critical role in colorectal cancer (CRC) prognosis. However, most studies assess a limited set of immune markers manually in the tumor region, without considering immune heterogeneity across multiple tissue regions. This study aims to enhance CRC prognostic assessment by developing an automated multi-regional immunohistochemistry (IHC) scoring system for 15 immune markers. Two representative tissue cores were extracted from CRC surgical specimens (n = 154) across four regions: tumor center, invasive margin, paracancerous tissues, and normal tissues. IHC staining was performed for 15 immune markers, and digitized slides were analyzed using computational algorithms to classify tissue types (e.g., glands, tumor, and stroma) and identify stained pixels. Immune infiltration was quantified in different tissue types across regions, and a tumor-to-healthy immune ratio (THIR) score was introduced to compare immune marker expression in tumor versus healthy stroma. Associations between IHC scores and overall survival (OS) and relapse-free survival (RFS) were evaluated. Computational models achieved 95.19% accuracy in tissue classification and 97.90% in staining identification. Analysis of 120 IHC scores (15 markers × 8 tissue types) revealed significant immune heterogeneity, with 56 scores correlating with OS and 54 with RFS. Notably, markers such as Granzyme B and CD4 had higher prognostic relevance at the invasive margin than the tumor center, while markers like S100 and CD20 exhibited opposing prognostic effects across regions. Integrating multiple markers significantly improved prognostic accuracy, with the combined marker score in normal stroma providing the most significant risk stratification (log-rank test, p = 1.56e-7, OS). The THIR score also strongly correlated with patient outcomes. This study advances CRC prognostication through automated multi-regional IHC scoring, highlighting the importance of immune heterogeneity across tissue regions. These findings support integrating region-specific immune profiling into clinical workflows for more personalized and precise patient care.
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.