Abstract

Motivation: Identifying high-risk patients for distant metastasis (DM) before treatment can facilitate the development of personalized neoadjuvant treatment and improve the prognosis of patients with locally advanced rectal cancer (LARC). Goal(s): This study aimed to construct a predictive model that integrates radiological information at the macroscale and pathological information at the microscale to estimate the probability of DM in LARC patients after neoadjuvant chemoradiotherapy, using radiomics, pathomics, and biopsy-adapted immunoscore. Approach: Feature selection and signature construction were performed using the least absolute shrinkage and selection operator (LASSO)-Cox analysis. Results: The results demonstrated the effectiveness of the nomogram in identifying high-risk DM patients. Impact: Incorporating multiscale information, including radiomics, pathomics, and the immune microenvironment, enhances the characterization of tumors and provides a robust model for identifying high-risk DM patients in LARC. This approach aids in the development of personalized neoadjuvant treatment strategies.