Multimodal data integration for biologically-relevant artificial intelligence to guide adjuvant chemotherapy in stage II colorectal cancer.
Authors
Affiliations (8)
Affiliations (8)
- 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.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing, 400038, China.
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming 650000, 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. Electronic address: [email protected].
- 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. Electronic address: [email protected].
Abstract
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. 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. 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<sub>interaction</sub> = 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<sup>+</sup>-T, and CD8<sup>+</sup>-T cells into the core tumoural areas. Further experiments confirmed that changes in vessel morphology led to alterations in predictive imaging features. 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. 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).