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<p>Immunohistochemistry (IHC) remains the gold standard for evaluating protein expression in tumor microenvironment analysis. This approach hinders robust correlation analyses between spatial heterogeneity in the tumor microenvironment and clinical outcomes like disease-free survival (DFS). To address these challenges, we developed an automated pipeline for quantitative IHC feature extraction. Our method integrates deep learning-based tumor segmentation with computational detection of invasive margins at varying distances. Deconvolution algorithms quantify diaminobenzidine (DAB) staining intensity across the tumor body and the invasive margin. Spatial heterogeneous DAB density patterns were subsequently analyzed for DFS correlation. Using 104 patient samples (57 training/47 validation) stained for CD3, CD8, CD31, and HIF-1α, we identified 2 prognostic feature categories (CD3/CD8 aggregated positive areas within the 0.25-mm peripheral zone extending outward from the tumor-invasive front and HIF1-α-positive areas within a 0.75-mm peripheral zone extending outward from the tumor-invasive front). Immune-related features demonstrated C-indices of 0.726 (training) and 0.626 (validation), while hypoxia-associated markers showed C-indices of 0.714 and 0.656, respectively. Integration of these features with pTNM staging enhanced DFS stratification compared to pTNM staging alone, improving C-indices from 0.702 to 0.819 (training) and 0.668 to 0.853 (validation). This automated pipeline addresses critical limitations in traditional IHC analysis by enabling: 1) Objective quantification of spatial DAB heterogeneity. 2) Identification of biologically interpretable prognostic features. 3) Enhanced predictive performance over conventional staging systems. Our findings suggest this methodology could standardize IHC-based prognostic assessments and inform personalized treatment strategies. Further validation in multicenter cohorts is warranted to confirm clinical applicability.</p>

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Automatically quantifying spatial heterogeneity of immune and tumor hypoxia environment and predicting disease-free survival for patients with rectal cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月18日

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  • Shenlun Chen,
  • Zonglin Liu,
  • Jiazhou Wang,
  • Zhiyuan Zhang,
  • Andre Dekker,
  • Leonard Wee,
  • Petros Kalendralis,
  • Yiqun Sun,
  • Tong Tong

摘要

Immunohistochemistry (IHC) remains the gold standard for evaluating protein expression in tumor microenvironment analysis. This approach hinders robust correlation analyses between spatial heterogeneity in the tumor microenvironment and clinical outcomes like disease-free survival (DFS). To address these challenges, we developed an automated pipeline for quantitative IHC feature extraction. Our method integrates deep learning-based tumor segmentation with computational detection of invasive margins at varying distances. Deconvolution algorithms quantify diaminobenzidine (DAB) staining intensity across the tumor body and the invasive margin. Spatial heterogeneous DAB density patterns were subsequently analyzed for DFS correlation. Using 104 patient samples (57 training/47 validation) stained for CD3, CD8, CD31, and HIF-1α, we identified 2 prognostic feature categories (CD3/CD8 aggregated positive areas within the 0.25-mm peripheral zone extending outward from the tumor-invasive front and HIF1-α-positive areas within a 0.75-mm peripheral zone extending outward from the tumor-invasive front). Immune-related features demonstrated C-indices of 0.726 (training) and 0.626 (validation), while hypoxia-associated markers showed C-indices of 0.714 and 0.656, respectively. Integration of these features with pTNM staging enhanced DFS stratification compared to pTNM staging alone, improving C-indices from 0.702 to 0.819 (training) and 0.668 to 0.853 (validation). This automated pipeline addresses critical limitations in traditional IHC analysis by enabling: 1) Objective quantification of spatial DAB heterogeneity. 2) Identification of biologically interpretable prognostic features. 3) Enhanced predictive performance over conventional staging systems. Our findings suggest this methodology could standardize IHC-based prognostic assessments and inform personalized treatment strategies. Further validation in multicenter cohorts is warranted to confirm clinical applicability.