Deep multimodal fusion of patho-radiomic and clinical data for enhanced survival prediction for colorectal cancer patients.
Authors
Affiliations (5)
Affiliations (5)
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
- Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.
- Department of Urology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
- First Clinical Medical College, Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China.
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China. [email protected].
Abstract
This study introduces PRISM-CRC, a novel deep learning framework designed to improve the diagnosis and prognosis of colorectal cancer (CRC) by integrating histopathology, radiology, and clinical data. The model demonstrated high accuracy, achieving a concordance index of 0.82 for predicting 5-year disease-free survival and an AUC of 0.91 for identifying microsatellite instability (MSI) status. A key finding is the synergistic power of this multimodal approach, which significantly outperformed models using only a single data type. The PRISM-CRC risk score proved to be a strong, independent predictor of survival, offering more granular risk stratification than the traditional TNM staging system. This capability has direct clinical implications for personalizing treatment, such as identifying high-risk stage II patients who might benefit from adjuvant chemotherapy. The study acknowledges limitations, including a modest performance decrease on external datasets due to "domain shift" and classification errors in morphologically ambiguous cases, highlighting the need for future prospective trials to validate its clinical utility.