Integrating radiomics, pathomics, and biopsy-adapted immunoscore for predicting distant metastasis in locally advanced rectal cancer

作者：Rui Zhao, Wenjuan Shen, Wen Zhao, Wenjing Peng, Lijuan Wan, Shanshan Chen, Xiaohang Liu, Suqiong Wang, Shuangmei Zou, Rongxin Zhang, Hongmei Zhang · 发表于：ESMO Open · 年份：2025 · DOI：10.1016/j.esmoop.2024.104102 · 被引用次数：13 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Cancer Immunotherapy and Biomarkers、Colorectal Cancer Surgical Treatments

Background This study aimed to develop and validate a nomogram that utilized macro- and microscopic tumor characteristics at baseline, including radiomics, pathomics, and biopsy-adapted immunoscore (IS B ), to accurately predict distant metastasis (DM) in patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant chemoradiotherapy (nCRT). Materials and methods In total, 201 patients with LARC (91 months of median follow-up) were enrolled. Radiomics features were extracted from apparent diffusion coefficient maps and T2-weighted images. Pathomics features including global pattern (features of the entire image) and local pattern (features of the tumor nuclei) were extracted from whole-slide images of hematoxylin–eosin-stained biopsy specimens. IS B was calculated from the densities of CD3+ and CD8+ T cells in the tumor region using immunohistochemistry on biopsy specimens. The construction of a predictive model was carried out using the least absolute shrinkage and selection operator-Cox analysis, with performance metrics including the area under the curve (AUC) and concordance index (C-index) utilized for evaluation. Results Compared with patients with moderate and high IS B , patients with low IS B exhibited significantly higher risk scores for radiomics and pathomics signatures. The nomogram showed respective C-indexes of 0.902 and 0.848 for 5-year DM-free survival in the training and test sets, along with corresponding AUC values of 0.950 and 0.872. Patie...