Novel Artificial Intelligence Combining Convolutional Neural Network and Support Vector Machine to Predict Colorectal Cancer Prognosis and Mutational Signatures From Hematoxylin and Eosin Images
{"title":"Novel Artificial Intelligence Combining Convolutional Neural Network and Support Vector Machine to Predict Colorectal Cancer Prognosis and Mutational Signatures From Hematoxylin and Eosin Images","authors":"","doi":"10.1016/j.modpat.2024.100562","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":18706,"journal":{"name":"Modern Pathology","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S089339522400142X/pdfft?md5=011a6be48e3b6400ca253fe1b212ac73&pid=1-s2.0-S089339522400142X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Modern Pathology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S089339522400142X","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PATHOLOGY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Modern Pathology, an international journal under the ownership of The United States & Canadian Academy of Pathology (USCAP), serves as an authoritative platform for publishing top-tier clinical and translational research studies in pathology.
Original manuscripts are the primary focus of Modern Pathology, complemented by impactful editorials, reviews, and practice guidelines covering all facets of precision diagnostics in human pathology. The journal's scope includes advancements in molecular diagnostics and genomic classifications of diseases, breakthroughs in immune-oncology, computational science, applied bioinformatics, and digital pathology.