用于生物相关人工智能的多模态数据整合，以指导II期结直肠癌的辅助化疗。

Multimodal data integration for biologically-relevant artificial intelligence to guide adjuvant chemotherapy in stage II colorectal cancer.

作者信息

Xie Chenyi, Ning Ziyu, Guo Ting, Yao Lisha, Chen Xiaobo, Huang Wanghong, Li Suyun, Chen Jiahui, Zhao Ke, Bian Xiuwu, Li Zhenhui, Huang Yanqi, Liang Changhong, Zhang Qingling, Liu Zaiyi

机构信息

Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou, 510080, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.

Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510080, China.

出版信息

EBioMedicine. 2025 Jun 4;117:105789. doi: 10.1016/j.ebiom.2025.105789.

DOI:10.1016/j.ebiom.2025.105789

PMID:40472802

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12171563/

Abstract

BACKGROUND

Adjuvant chemotherapy provides a limited survival benefit (<5%) for patients with stage II colorectal cancer (CRC) and is suggested for high-risk patients. Given the heterogeneity of stage II CRC, we aimed to develop a clinically explainable artificial intelligence (AI)-powered analyser to identify radiological phenotypes that would benefit from chemotherapy.

METHODS

Multimodal data from patients with CRC across six cohorts were collected, including 405 patients from the Guangdong Provincial People's Hospital for model development and 153 patients from the Yunnan Provincial Cancer Centre for validation. RNA sequencing data were used to identify the differentially expressed genes in the two radiological clusters. Histopathological patterns were evaluated to bridge the gap between the imaging and genetic information. Finally, we investigated the discovered morphological patterns of mouse models to observe imaging features.

FINDINGS

The survival benefit of chemotherapy varied significantly among the AI-powered radiological clusters [interaction hazard ratio (iHR) = 5.35, (95% CI: 1.98, 14.41), adjusted P = 0.012]. Distinct biological pathways related to immune and stromal cell abundance were observed between the clusters. The observation only (OO)-preferable cluster exhibited higher necrosis, haemorrhage, and tortuous vessels, whereas the adjuvant chemotherapy (AC)-preferable cluster exhibited vessels with greater pericyte coverage, allowing for a more enriched infiltration of B, CD4-T, and CD8-T cells into the core tumoural areas. Further experiments confirmed that changes in vessel morphology led to alterations in predictive imaging features.

INTERPRETATION

The developed explainable AI-powered analyser effectively identified patients with stage II CRC with improved overall survival after receiving adjuvant chemotherapy, thereby contributing to the advancement of precision oncology.

FUNDING

This work was funded by the National Science Fund of China (81925023, 82302299, and U22A2034), Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (2022B1212010011), and High-level Hospital Construction Project (DFJHBF202105 and YKY-KF202204).

摘要

背景

辅助化疗对II期结直肠癌（CRC）患者的生存获益有限（<5%），建议用于高危患者。鉴于II期CRC的异质性，我们旨在开发一种具有临床可解释性的人工智能（AI）分析器，以识别能从化疗中获益的放射学表型。

方法

收集了来自六个队列的CRC患者的多模态数据，其中包括来自广东省人民医院的405例患者用于模型开发，以及来自云南省癌症中心的153例患者用于验证。RNA测序数据用于识别两个放射学簇中差异表达的基因。评估组织病理学模式以弥合影像学和基因信息之间的差距。最后，我们研究了小鼠模型中发现的形态学模式以观察影像学特征。

结果

在由AI驱动的放射学簇中，化疗的生存获益有显著差异[交互风险比（iHR）=5.35，（95%置信区间：1.98，14.41），校正P=0.012]。在各簇之间观察到与免疫和基质细胞丰度相关的不同生物学途径。仅观察（OO）优先簇表现出更高的坏死、出血和迂曲血管，而辅助化疗（AC）优先簇表现出血管周围周细胞覆盖度更高，使得B细胞、CD4-T细胞和CD8-T细胞更丰富地浸润到肿瘤核心区域。进一步实验证实血管形态的改变导致预测性影像学特征的改变。