Objective: The primary objective of this study is to investigate various applications of artificial intelligence (AI) and statistical methodologies for analyzing and managing peritoneal metastases (PM) caused by gastrointestinal cancers.
Methods: Relevant keywords and search criteria were comprehensively researched on PubMed and Google Scholar to identify articles and reviews related to the topic. The AI approaches considered were conventional machine learning (ML) and deep learning (DL) models, and the relevant statistical approaches included biostatistics and logistic models.
Importance: Serial circulating tumor DNA (ctDNA) has emerged as a routine surveillance strategy for patients with resected colorectal cancer, but how serial ctDNA monitoring is associated with potential curative outcomes has not been formally assessed.
Objective: To examine whether there is a benefit of adding serial ctDNA assays to standard-of-care imaging surveillance for potential curative outcomes in patients with resected colorectal cancer.
Design, Setting, And Participants: In this single-center (City of Hope Comprehensive Cancer Center, Duarte, California), retrospective, case cohort study, patients with stage II to IV colorectal cancer underwent curative resection and were monitored with serial ctDNA assay and National Cancer Center Network (NCCN)-guided imaging surveillance from September 20, 2019, to April 3, 2024.