Department of Surgery, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Yonsei University Medical Library, Seoul, Republic of Korea.
Langenbecks Arch Surg. 2024 Sep 23;409(1):287. doi: 10.1007/s00423-024-03476-9.
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.
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.
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.
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.
我们回顾和分析了机器学习（ML）和深度学习（DL）模型在预测 T1 结直肠癌（CRC）患者淋巴结转移（LNM）中的应用研究。在 T1 CRC 患者中，在根治性手术前预测 LNM 非常重要。然而，目前的手术治疗指南存在局限性。使用 ML 或 DL 进行 LNM 预测可能会提高预测准确性。评估了 ML 和 DL 基于模型对 CRC 患者 LNM 预测的诊断准确性。
我们对 PubMed、Embase 和 Cochrane 数据库（从成立到 2022 年 4 月 30 日）进行了全面检索，以查找专门针对 T1 CRC 患者 LNM 预测的应用 ML 或 DL 的研究，以与组织病理学发现进行比较，而不涉及放射学方面。
纳入了 7 项研究的 33199 例 T1 CRC 患者，这些研究采用回顾性设计。3173 例（9.6%）患者观察到 LNM。总体而言，ML 和 DL 基于模型对 T1 CRC 患者 LNM 的预测具有 0.944 的敏感性和 0.877 的特异性。在本荟萃分析中纳入的研究中使用了六种不同类型的 ML 和 DL 模型。因此，观察到高度异质性。
ML 和 DL 模型对预测 T1 CRC 患者的 LNM 具有较高的敏感性和特异性，且研究之间的异质性显著。这些结果表明 ML 或 DL 作为诊断工具具有潜力。然而，应开发更可靠的算法来预测 T1 CRC 患者手术前的 LNM。
