What question did this study set out to answer?

The study aims to improve prognostic predictions across multiple cancer types by integrating AI-informed immune cell profiles from histological images.

May 29, 2026

Virtual AI-informed immunocyte profiling (VAIP) from H&E as a predictor of multi-cancer prognosis.

Puntos clave

The study aims to improve prognostic predictions across multiple cancer types by integrating AI-informed immune cell profiles from histological images.
Developed an AI method (VAIP) to quantify tumor-infiltrating lymphocytes (TILs) and virtual tumor-associated macrophages (TAMs) from H&E slides.
Computed immune-balance ratios and cell densities in cancer regions and trained cancer-specific Cox models for prognostic stratification.
Evaluated models using disease-free survival for DCIS and ER+ breast cancer and overall survival for HNSCC.
DCIS model achieved C-index of 0.616 with hazard ratio (HR) 1.76 [1.20-2.59], P<0.001.
HGSOC model achieved C-index of 0.553 with HR 1.38 [1.02-1.87], P=0.030.
ER+ BC model achieved C-index of 0.577 with HR 1.51 [1.00-2.28], P=0.004; HNSCC model C-index 0.603, HR 1.92 [1.03-3.36], P=0.012.

Resumen

3118 Background: Improved risk stratification is critically needed across multiple cancers to guide treatment decisions. This includes reducing overtreatment in Ductal Carcinoma in Situ (DCIS), addressing late recurrence risk in estrogen receptor-positive/lymph node-negative (ER+/LN-) breast cancer (BC), and refining prognosis in head HGSOC 7; ER+ 4; HNSCC 4). Prognostic stratification used disease-free survival (DFS) for DCIS (N=273), HGSOC (training N=165, independent test N=220), and ER+ BC (training N=144, test N=622), and overall survival (OS) for HNSCC (N=77), reflecting clinical endpoints. Evaluation used cross-validation for cohorts without an external test set (DCIS, HNSCC) and independent training and testing when independent cohort was available (HGSOC, ER+). Results: Risk stratification performance across cancer types is in Table 1. The DCIS model used 6 features, largely TIL-macrophage contrast ratios (TIL/M2, TIL/M1). HGSOC used 7 features with a similar ratio-heavy immune-balance signature, plus macrophage-normalized density and polarization metrics (e.g., M1/N, M/N). In contrast, ER+ BC and HNSCC achieved best performance with 4 features dominated by macrophage measures, particularly M2 density and the M1/M2 ratio. Across sites, CD163-related macrophage metrics and immune-balance ratios were consistently informative. Conclusions: Integrating H&E-visible TILs with virtual CD68/CD163 TAMs enables fully automated, H&E-only prognostic biomarkers that stratify OS and DFS across multiple cancers, demonstrating consistent prognostic value in DCIS, ER+ BC, HGSOC, and HNSCC. These findings support further study of this histomorphometric risk classifier through broader external and prospective validation. Performance across cancer types. Cancer Type Cohort C-index HR 95% CI p-value DCIS All 0.616 1.76 1.20-2.59 <0.001 DCIS RT/No-treatment 0.624 1.94 1.15-3.26 0.003 HGSOC Test 0.553 1.38 1.02-1.87 0.030 ER+ BC Test 0.577 1.51 1.00-2.28 0.004 HNSCC All 0.603 1.92 1.03-3.36 0.012

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Cite This Study

Jana et al. (Wed,) studied this question.

synapsesocial.com/papers/6a192d2dfab5b468c4415fe9 https://doi.org/https://doi.org/10.1200/jco.2026.44.16_suppl.3118

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