Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Cancer Lett. 2024 Jul 1;593:216967. doi: 10.1016/j.canlet.2024.216967. Epub 2024 May 18.
To predict clinical important outcomes for colorectal liver metastases (CRLM) patients receiving colorectal resection with simultaneous liver resection by integrating demographic, clinical, laboratory, and genetic data.
Random forest (RF) models were developed to predict postoperative complications and major complications (binary outcomes), as well as progression-free survival (PFS) and overall survival (OS) (time-to-event outcomes) of the CRLM patients based on data from two hospitals. The models were validated on an external dataset from an independent hospital. The clinical utility of the models was assessed via decision curve analyses (DCA).
There were 1067 patients included in survival prediction analyses and 1070 patients included in postoperative complication prediction analyses. The RF models provided an assessment of the model contributions of features for outcomes and suggested KRAS, BRAF, and MMR status were salient for the PFS or OS predictions. RF model of PFS showed that the Brier scores at 1-, 3-, and 5-year PFS were 0.213, 0.202 and 0.188; and the AUCs of 1-, 3- and 5-year PFS were 0.702, 0.720 and 0.743. RF model of OS revealed that Brier scores of 1-,3-, and 5-year OS were 0.040, 0.183 and 0.211; and the AUCs of 1-, 3- and 5-year OS were 0.737, 0.706 and 0.719. RF model for postoperative complication resulted in an AUC of 0.716 and a Brier score of 0.196. DCA curves clearly demonstrated that the RF models for these outcomes exhibited a superior net benefit across a wide range of threshold probabilities, signifying their favorable clinical utility. The RF models consistently exhibited robust performance in both internal cross-validation and external validation. The individualized risk profile predicted by the models closely aligned with the actual survival outcomes observed for the patients. A web-based tool (https://kanli.shinyapps.io/CRLMRF/) was provided to demonstrate the practical use of the prediction models for new patients in the clinical setting.
The predictive models and a web-based tool for personalized prediction demonstrated a moderate predictive performance and favorable clinical utilities on several key clinical outcomes of CRLM patients receiving simultaneous resection, which could facilitate the clinical decision-making and inform future interventions for CRLM patients.
通过整合人口统计学、临床、实验室和遗传数据，对接受结直肠切除术联合肝切除术的结直肠肝转移（CRLM）患者的临床重要结局进行预测。
基于两家医院的数据，开发随机森林（RF）模型以预测术后并发症和主要并发症（二分类结局），以及 CRLM 患者的无进展生存期（PFS）和总生存期（OS）（时间事件结局）。该模型在来自独立医院的外部数据集上进行了验证。通过决策曲线分析（DCA）评估模型的临床实用性。
生存预测分析中纳入了 1067 例患者，术后并发症预测分析中纳入了 1070 例患者。RF 模型提供了对结局特征模型贡献的评估，并表明 KRAS、BRAF 和 MMR 状态对 PFS 或 OS 预测很重要。PFS 的 RF 模型显示，1 年、3 年和 5 年 PFS 的 Brier 评分分别为 0.213、0.202 和 0.188；1 年、3 年和 5 年 PFS 的 AUC 分别为 0.702、0.720 和 0.743。OS 的 RF 模型表明，1 年、3 年和 5 年 OS 的 Brier 评分分别为 0.040、0.183 和 0.211；1 年、3 年和 5 年 OS 的 AUC 分别为 0.737、0.706 和 0.719。术后并发症的 RF 模型的 AUC 为 0.716，Brier 评分 0.196。DCA 曲线清楚地表明，这些结局的 RF 模型在广泛的阈值概率范围内表现出优越的净收益，表明其具有良好的临床实用性。RF 模型在内部交叉验证和外部验证中均表现出稳健的性能。模型预测的个体化风险特征与患者实际生存结局密切相关。提供了一个基于网络的工具（https://kanli.shinyapps.io/CRLMRF/），以展示预测模型在临床环境中对新患者的实际应用。
该预测模型和基于网络的个性化预测工具在接受联合切除术的 CRLM 患者的几个关键临床结局上表现出中等的预测性能和良好的临床实用性，这有助于临床决策，并为 CRLM 患者的未来干预提供信息。
