Novel Artificial Intelligence Combining Convolutional Neural Network and Support Vector Machine to Predict Colorectal Cancer Prognosis and Mutational Signatures From Hematoxylin and Eosin Images

Abstract

Reducing recurrence following radical resection of colon cancer without overtreatment or undertreatment remains a challenge. Postoperative adjuvant chemotherapy (Adj) is currently administered based solely on pathologic TNM stage. However, prognosis can vary significantly among patients with the same disease stage. Therefore, novel classification systems in addition to the TNM are necessary to inform decision-making regarding postoperative treatment strategies, especially stage II and III disease, and minimize overtreatment and undertreatment with Adj. We developed a prognostic prediction system for colorectal cancer using a combined convolutional neural network and support vector machine approach to extract features from hematoxylin and eosin staining images. We combined the TNM and our artificial intelligence (AI)-based classification system into a modified TNM-AI classification system with high discriminative power for recurrence-free survival. Furthermore, the cancer cell population recognized by this system as low risk of recurrence exhibited the mutational signature SBS87 as a genetic phenotype. The novel AI-based classification system developed here is expected to play an important role in prognostic prediction and personalized treatment selection in oncology.

Keywords: TNM-artificial intelligence classification system; adjuvant chemotherapy; colon cancer; convolutional neural network; hematoxylin and eosin images; mutational signature; support vector machine.