Application of machine learning for predicting lymph node metastasis in T1 colorectal cancer: a systematic review and meta-analysis.

IF 2.1 3区 医学 Q2 SURGERY
Chinock Cheong, Na Won Kim, Hye Sun Lee, Jeonghyun Kang
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引用次数: 0

Abstract

Background: We review and analyze research on the application of machine learning (ML) and deep learning (DL) models to lymph node metastasis (LNM) prediction in patients with T1 colorectal cancer (CRC). Predicting LNM before radical surgery is important in patients with T1 CRC. However, current surgical treatment guidelines are limited. LNM prediction using ML or DL may improve predictive accuracy. The diagnostic accuracy of LNM prediction using ML- and DL-based models for patients with CRC was assessed.

Methods: We performed a comprehensive search of the PubMed, Embase, and Cochrane databases (inception to April 30th of 2022) for studies that applied ML or DL to LNM prediction in T1 CRC patients specifically to compare with histopathological findings and not related to radiological aspects.

Results: 33,199 T1 CRC patients enrolled across seven studies with a retrospective design were included. LNM was observed in 3,173 (9.6%) patients. Overall, the ML- and DL-based model exhibited a sensitivity of 0.944 and specificity of 0.877 for the prediction of LNM in patients with T1 CRC. Six different types of ML and DL models were used across the studies included in this meta-analysis. Therefore, a high degree of heterogeneity was observed.

Conclusions: The ML and DL models provided high sensitivity and specificity for predicting LNM in patients with T1 CRC, and the heterogeneity between studies was significant. These results suggest the potential of ML or DL as diagnostic tools. However, more reliable algorithms should be developed for predicting LNM before surgery in patients with T1 CRC.

应用机器学习预测 T1 结直肠癌淋巴结转移:系统综述和荟萃分析。
背景:我们回顾并分析了机器学习(ML)和深度学习(DL)模型在 T1 结直肠癌(CRC)患者淋巴结转移(LNM)预测中的应用研究。在根治性手术前预测淋巴结转移对 T1 结直肠癌患者非常重要。然而,目前的手术治疗指南却很有限。使用 ML 或 DL 预测 LNM 可以提高预测准确性。我们评估了使用基于 ML 和 DL 的模型对 CRC 患者进行 LNM 预测的诊断准确性:我们对 PubMed、Embase 和 Cochrane 数据库(从开始到 2022 年 4 月 30 日)进行了全面检索,以寻找将 ML 或 DL 应用于 T1 CRC 患者 LNM 预测的研究,这些研究专门与组织病理学结果进行比较,而与放射学方面无关:共纳入了 7 项采用回顾性设计的研究中的 33199 例 T1 CRC 患者。3173例(9.6%)患者观察到LNM。总体而言,基于 ML 和 DL 的模型预测 T1 CRC 患者 LNM 的灵敏度为 0.944,特异度为 0.877。本次荟萃分析所纳入的研究中使用了六种不同类型的 ML 和 DL 模型。因此,观察到了高度的异质性:结论:ML 和 DL 模型在预测 T1 CRC 患者的 LNM 方面具有较高的灵敏度和特异性,而不同研究之间的异质性非常明显。这些结果表明,ML 或 DL 具有作为诊断工具的潜力。不过,应开发更可靠的算法,用于在 T1 CRC 患者手术前预测 LNM。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.30
自引率
8.70%
发文量
342
审稿时长
4-8 weeks
期刊介绍: Langenbeck''s Archives of Surgery aims to publish the best results in the field of clinical surgery and basic surgical research. The main focus is on providing the highest level of clinical research and clinically relevant basic research. The journal, published exclusively in English, will provide an international discussion forum for the controlled results of clinical surgery. The majority of published contributions will be original articles reporting on clinical data from general and visceral surgery, while endocrine surgery will also be covered. Papers on basic surgical principles from the fields of traumatology, vascular and thoracic surgery are also welcome. Evidence-based medicine is an important criterion for the acceptance of papers.
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