Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands.
World J Surg. 2022 Dec;46(12):3100-3110. doi: 10.1007/s00268-022-06728-1. Epub 2022 Sep 15.
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.
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.
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.
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.
机器学习（ML）已在医疗保健的各个领域得到应用。在结直肠外科中，ML 的作用尚未被报道。在本系统综述中，提供了用于预测结直肠手术后手术结果的机器学习模型概述。
检索了 PubMed、EMBASE、Cochrane 和 Web of Science 数据库，以查找使用机器学习模型对接受结直肠手术的患者进行研究的文献。纳入研究需要应用机器学习模型对接受结直肠手术的患者进行分析。排除不使用机器学习或结直肠手术或报告综述、儿童、研究摘要的研究。使用 Probast 风险偏倚工具评估机器学习模型的方法学质量。
共分析了 1821 项研究，最终纳入 31 篇文章。大量的 ML 算法被用于预测疾病的病程和对新辅助放化疗的反应。放射组学应用最为广泛，预测准确率高达 91%。然而，大多数研究采用的是回顾性研究设计，没有外部验证或校准。
机器学习模型在预测结直肠手术后的手术结果方面显示出了有前景的潜力。然而，需要大规模的数据来弥合校准和外部验证之间的差距。需要临床应用来证明 ML 在日常实践中的贡献。
