Zhang Yuwei, Abousamra Shahira, Hasan Mahmudul, Torre-Healy Luke, Krichevsky Spencer, Shrestha Sampurna, Bremer Erich, Oldridge Derek A, Rech Andrew J, Furth Emma E, Bocklage Therese J, Levens Justin S, Hands Isaac, Durbin Erich B, Samaras Dimitris, Kurc Tahsin, Saltz Joel H, Gupta Rajarsi
Res Sq. 2025 Apr 1:rs.3.rs-6173056. doi: 10.21203/rs.3.rs-6173056/v1.
We developed a deep learning Pathomics image analysis workflow to generate spatial Tumor-TIL maps to visualize and quantify the abundance and spatial distribution of tumor infiltrating lymphocytes (TILs) in colon cancer. Colon cancer and lymphocyte detection in hematoxylin and eosin (H&E) stained whole slide images (WSIs) has revealed complex immuno-oncologic interactions that form TIL-rich and TIL-poor tumor habitats, which are unique in each patient sample. We compute Tumor%, total lymphocyte%, and TILs% as the proportion of the colon cancer microenvironment occupied by intratumoral lymphocytes for each WSI. Kaplan-Meier survival analyses and multivariate Cox regression were utilized to evaluate the prognostic significance of TILs% as a Pathomics biomarker. High TILs% was associated with improved overall survival (OS) and progression-free interval (PFI) in localized and metastatic colon cancer and other clinicopathologic variables, supporting the routine use of Pathomics Tumor-TIL mapping in biomedical research, clinical trials, laboratory medicine, and precision oncology.
我们开发了一种深度学习病理组学图像分析工作流程,以生成空间肿瘤浸润淋巴细胞(TIL)图谱,用于可视化和量化结肠癌中肿瘤浸润淋巴细胞(TIL)的丰度和空间分布。在苏木精和伊红(H&E)染色的全切片图像(WSI)中进行结肠癌和淋巴细胞检测,揭示了形成富含TIL和缺乏TIL的肿瘤微环境的复杂免疫肿瘤学相互作用,这些微环境在每个患者样本中都是独特的。我们计算每个WSI中肿瘤内淋巴细胞占据的结肠癌微环境比例,即肿瘤占比、总淋巴细胞占比和TIL占比。采用Kaplan-Meier生存分析和多变量Cox回归来评估TIL占比作为病理组学生物标志物的预后意义。高TIL占比与局限性和转移性结肠癌的总生存期(OS)改善以及无进展生存期(PFI)改善以及其他临床病理变量相关,支持在生物医学研究、临床试验、检验医学和精准肿瘤学中常规使用病理组学肿瘤-TIL图谱。