Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA.
Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan.
World J Surg. 2024 Nov;48(11):2760-2771. doi: 10.1002/wjs.12376. Epub 2024 Oct 19.
Machine learning (ML) may provide novel insights into data patterns and improve model prediction accuracy. The current study sought to develop and validate an ML model to predict early extra-hepatic recurrence (EEHR) among patients undergoing resection of colorectal liver metastasis (CRLM).
Patients with CRLM who underwent curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. An eXtreme gradient boosting (XGBoost) model was developed to estimate the risk of EEHR, defined as extrahepatic recurrence within 12 months after hepatectomy, using clinicopathological factors. The relative importance of factors was determined using Shapley additive explanations (SHAP) values.
Among 1410 patients undergoing curative-intent resection, 131 (9.3%) patients experienced EEHR. Median OS among patients with and without EEHR was 35.4 months (interquartile range [IQR] 29.9-46.7) versus 120.5 months (IQR 97.2-134.0), respectively (p < 0.001). The ML predictive model had c-index values of 0.77 (95% CI, 0.72-0.81) and 0.77 (95% CI, 0.73-0.80) in the entire dataset and the validation data set with bootstrapping resamples, respectively. The SHAP algorithm demonstrated that T and N primary tumor categories, as well as tumor burden score were the three most important predictors of EEHR. An easy-to-use risk calculator for EEHR was developed and made available online at: https://junkawashima.shinyapps.io/EEHR/.
An easy-to-use online calculator was developed using ML to help clinicians predict the chance of EEHR after curative-intent resection for CRLM. This tool may help clinicians in decision-making related to treatment strategies for patients with CRLM.
机器学习 (ML) 可能为数据模式提供新的见解，并提高模型预测准确性。本研究旨在开发和验证一种 ML 模型，以预测接受结直肠癌肝转移 (CRLM) 切除术的患者的早期肝外复发 (EEHR)。
从一个国际多机构数据库中确定了 2000 年至 2020 年期间接受根治性切除术的 CRLM 患者。使用 XGBoost 模型，根据临床病理因素，估计 EEHR（肝切除后 12 个月内发生肝外复发）的风险。使用 Shapley 加性解释 (SHAP) 值确定因素的相对重要性。
在 1410 例接受根治性切除术的患者中，131 例（9.3%）患者发生 EEHR。有和无 EEHR 的患者的中位 OS 分别为 35.4 个月（四分位距 [IQR] 29.9-46.7）和 120.5 个月（IQR 97.2-134.0）（p<0.001）。ML 预测模型在整个数据集和 bootstrapping 重采样的验证数据集中的 c-index 值分别为 0.77（95%CI，0.72-0.81）和 0.77（95%CI，0.73-0.80）。SHAP 算法表明，T 和 N 原发性肿瘤类别以及肿瘤负担评分是 EEHR 的三个最重要预测因子。开发了一个易于使用的 EEHR 风险计算器，并可在以下网址在线获取：https://junkawashima.shinyapps.io/EEHR/。
使用 ML 开发了一个易于使用的在线计算器，以帮助临床医生预测 CRLM 根治性切除术后 EEHR 的机会。该工具可能有助于临床医生在与 CRLM 患者的治疗策略相关的决策中使用。
