Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review

• Published in: World journal of surgery Vol. 46; no. 12; pp. 3100 - 3110
• Main Authors: Bektaş, Mustafa, Tuynman, Jurriaan B., Costa Pereira, Jaime, Burchell, George L., van der Peet, Donald L.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2022; John Wiley & Sons, Inc
• Subjects: Abdominal Surgery; Algorithms; Calibration; Cardiac Surgery; Chemoradiotherapy; Chemotherapy; Child; Colorectal Surgery; General Surgery; Humans; Learning algorithms; Machine Learning; Medicine; Medicine & Public Health; Patients; Radiation therapy; Radiomics; Retrospective Studies; Scientific Review; Surgery; Surgical outcomes; Systematic review; Thoracic Surgery; Treatment Outcome; Vascular Surgery
• ISSN: 0364-2313; 1432-2323; 1432-2323
• DOI: 10.1007/s00268-022-06728-1

Background Machine learning (ML) has been introduced in various fields of healthcare. In colorectal surgery, the role of ML has yet to be reported. In this systematic review, an overview of machine learning models predicting surgical outcomes after colorectal surgery is provided. Methods Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using machine learning models for patients undergoing colorectal surgery. To be eligible for inclusion, studies needed to apply machine learning models for patients undergoing colorectal surgery. Absence of machine learning or colorectal surgery or studies reporting on reviews, children, study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of machine learning models. Results A total of 1821 studies were analysed, resulting in the inclusion of 31 articles. A vast proportion of ML algorithms have been used to predict the course of disease and response to neoadjuvant chemoradiotherapy. Radiomics have been applied most frequently, along with predictive accuracies up to 91%. However, most studies included a retrospective study design without external validation or calibration. Conclusions Machine learning models have shown promising potential in predicting surgical outcomes after colorectal surgery. However, large-scale data is warranted to bridge the gap between calibration and external validation. Clinical implementation is needed to demonstrate the contribution of ML within daily practice.