Lou Shenghan, Huang Yanming, Du Fenqi, Xue Jingmin, Mo Genshen, Li Hao, Yu Zhanjiang, Li Yuanchun, Wang Hang, Huang Yuze, Xie Haonan, Song Wenjie, Zhang Xinyue, Li Huiying, Lou Chun, Han Peng
Department of Oncology Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China.
Department of General Surgery, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, Heilongjiang, China.
Front Immunol. 2025 Jul 8;16:1602909. doi: 10.3389/fimmu.2025.1602909. eCollection 2025.
The conventional tumor-node-metastasis (TNM) classification system remains limited in accurately forecasting prognosis and guiding adjuvant chemotherapy decisions for patients with colorectal cancer (CRC). To address this gap, we introduced and validated a novel pathomics signature (PS) derived from hematoxylin and eosin-stained whole slide images, leveraging a deep learning framework.
This retrospective study analyzed 883 slides from two independent cohorts. An interpretable multi-instance learning model was developed to construct PS, with SHapley Additive exPlanations (SHAP) and gradient-weighted class activation mapping (Grad-CAM) for the improvement of model interpretability and the identification of critical histopathological features, respectively. The transcriptomic data was provided by The Cancer Genome Atlas (TCGA) and integrated to investigate the biological mechanisms underpinning PS.
The results demonstrated that PS was proven to be an independent prognostic indicator for both overall and disease-free survival. It significantly enhanced the prognostic performance alongside TNM staging, as shown by improvements in net reclassification and integrated discrimination indices. Furthermore, patients in stages II and III with low PS levels were more likely to benefit from chemotherapy. Morphologically, PS reflected features such as tumor infiltration, adipocyte presence, fibrotic stroma, and immune cell engagement. Transcriptome analysis further revealed links between PS and pathways involved in tumor progression and immune evasion.
Our findings suggested that the application of deep learning to histopathological images could be an efficient method to improve the prognostic accuracy and evaluate the treatment responses in CRC. The PS offers a promising aid for clinical decision-making by shedding light on key pathogenic processes. Nevertheless, further validation through prospective studies remains essential.
传统的肿瘤-淋巴结-转移（TNM）分类系统在准确预测结直肠癌（CRC）患者的预后和指导辅助化疗决策方面仍然存在局限性。为了弥补这一差距，我们引入并验证了一种基于苏木精和伊红染色的全切片图像，利用深度学习框架得出的新型病理组学特征（PS）。
这项回顾性研究分析了来自两个独立队列的883张切片。开发了一种可解释的多实例学习模型来构建PS，分别使用SHapley加性解释（SHAP）和梯度加权类激活映射（Grad-CAM）来提高模型的可解释性和识别关键组织病理学特征。转录组数据由癌症基因组图谱（TCGA）提供，并进行整合以研究PS背后的生物学机制。
结果表明，PS被证明是总生存期和无病生存期的独立预后指标。正如净重新分类和综合判别指数的改善所示，它与TNM分期一起显著提高了预后性能。此外，II期和III期且PS水平低的患者更有可能从化疗中获益。在形态学上，PS反映了肿瘤浸润、脂肪细胞存在、纤维化基质和免疫细胞参与等特征。转录组分析进一步揭示了PS与肿瘤进展和免疫逃逸相关途径之间的联系。
我们的研究结果表明，将深度学习应用于组织病理学图像可能是提高CRC预后准确性和评估治疗反应的有效方法。PS通过揭示关键致病过程为临床决策提供了有前景的帮助。然而，通过前瞻性研究进行进一步验证仍然至关重要。
