Chen Qichen, Deng Yiqiao, Wang Kun, Li Yuan, Bi Xinyu, Li Kan, Zhao Hong
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, 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.
JAMA Netw Open. 2025 Aug 1;8(8):e2529093. doi: 10.1001/jamanetworkopen.2025.29093.
Current prognostic models for colorectal liver metastases (CRLM) primarily incorporate clinicopathologic features assessed at a single time point, resulting in a static risk assessment for individuals. Given that tumor progression is a dynamic process, especially for patients with CRLM, and patients' data are continuously collected during the follow-up visits, dynamic prediction is a natural model for risk assessments via reflecting the latest prognosis, whenever new marker measurements are available.
To develop CRLM prognostic models and a clinical web-based tool to facilitate dynamic predictions.
DESIGN, SETTING, AND PARTICIPANTS: In this retrospective prognostic study, patients with CRLM who underwent resection between January 2014 and January 2021, were included in the training and validation cohorts. Clinicopathologic characteristics and preoperative and postoperative laboratory measurements taken within 12 months after surgery across 9 laboratory markers (carcinoembryonic antigen, carbohydrate antigen 19-9, γ-glutamyl transferase, red blood cell distribution width SD and coefficient of variance, aspartate aminotransferase to platelet ratio index, Fibrous-4 index, S-index, and neutrophil-to-lymphocyte ratio) were collected. Three prediction models for progression-free survival (PFS) and overall survival (OS) based on a functional random survival forest framework were constructed and compared: model A incorporated only clinicopathologic characteristics, model B included clinicopathologic characteristics and preoperative laboratory markers, and model C integrated clinicopathologic characteristics along with longitudinal laboratory markers. Data were analyzed from June 2024 to June 2025.
Resection in patients with CRLM.
Performance metrics included area under the receiver operating characteristic curve (AUC) and Brier score (BS).
A total of 976 patients (median [IQR] age, 59 [51-65] years; 612 [62.7%] male) were eligible for this study, with 758 patients in the training cohort (median [IQR] age, 59 [52-66] years; 487 [64.2%] male) and 218 patients in the validation cohort (median [IQR] age, 58 [49-64] years; 125 [57.3%] male).The training cohort included a total of 24 992 longitudinal measurements, and the external validation cohort included 7198 longitudinal measurements. In the external validation cohort, model C demonstrated an improved prognostic capability compared with models A and B, with AUC values of 0.796 (95% CI, 0.740-0.848) for 1-year progression-free survival (PFS), 0.837 (95% CI, 0.768-0.899) for 3-year PFS, and 0.850 (95% CI, 0.780-0.914) for 5-year PFS. The corresponding BSs were 0.246 (95% CI, 0.236-0.261) for 1 year, 0.205 (95% CI, 0.193-0.218) for 3 years, and 0.142 (95% CI, 0.132-0.153) for 5 years. Model C consistently outperformed models A and B for overall survival (OS) prognosis, with AUCs of 0.849 (95% CI, 0.768-0.914) for 1 year, 0.741 (95% CI, 0.667-0.815) for 3 years, and 0.753 (95% CI: 0.656-0.849) for 5 years, alongside BS values of 0.047 (95% CI, 0.045-0.048) for 1 year, 0.178 (95% CI, 0.168-0.195) for 3 years, and 0.144 (95% CI, 0.133-0.165) for 5 years. Additionally, dynamic individualized risk profiles for PFS and OS were developed for patients. A web-based tool was created to facilitate the practical application of these dynamic prediction models for new patients in clinical environments.
In this retrospective prognostic study, the dynamic models, along with the web-based tool for personalized prediction, demonstrated improved performance by incorporating multiple longitudinal makers.
目前用于结直肠癌肝转移（CRLM）的预后模型主要纳入在单一时间点评估的临床病理特征，从而对个体进行静态风险评估。鉴于肿瘤进展是一个动态过程，尤其是对于CRLM患者，并且在随访期间会持续收集患者的数据，动态预测通过反映最新预后（每当有新的标志物测量值时），是一种自然的风险评估模型。
开发CRLM预后模型和基于网络的临床工具以促进动态预测。
设计、设置和参与者：在这项回顾性预后研究中，2014年1月至2021年1月期间接受手术切除的CRLM患者被纳入训练和验证队列。收集了临床病理特征以及术后12个月内的术前和术后实验室测量值，涉及9种实验室标志物（癌胚抗原、糖类抗原19-9、γ-谷氨酰转移酶、红细胞分布宽度标准差和变异系数、天冬氨酸转氨酶与血小板比值指数、Fibrous-4指数、S指数和中性粒细胞与淋巴细胞比值）。构建并比较了基于功能随机生存森林框架的3种无进展生存期（PFS）和总生存期（OS）预测模型：模型A仅纳入临床病理特征，模型B包括临床病理特征和术前实验室标志物，模型C整合临床病理特征以及纵向实验室标志物。数据于2024年6月至2025年6月进行分析。
CRLM患者的手术切除。
性能指标包括受试者操作特征曲线下面积（AUC）和Brier评分（BS）。
共有976例患者（中位[四分位间距]年龄，59[51-65]岁；612例[62.7%]为男性）符合本研究条件，训练队列中有758例患者（中位[四分位间距]年龄，59[52-66]岁；487例[64.2%]为男性），验证队列中有218例患者（中位[四分位间距]年龄，58[49-64]岁；125例[57.3%]为男性）。训练队列共包括24992次纵向测量，外部验证队列包括7198次纵向测量。在外部验证队列中，与模型A和B相比，模型C显示出更好的预后能力，1年无进展生存期（PFS）的AUC值为0.796（95%CI，0.740-0.848），3年PFS为0.837（95%CI，0.768-0.899），5年PFS为0.850（95%CI，0.780-0.914）。相应的1年BS为0.246（95%CI，0.236-0.261），3年为0.205（95%CI，0.193-0.218），5年为0.142（95%CI，0.132-0.153）。在总生存期（OS）预后方面，模型C始终优于模型A和B，1年AUC为0.849（95%CI，0.768-0.914），3年为0.741（95%CI，0.667-0.815），5年为0.753（95%CI：0.656-0.849），同时1年BS值为0.047（95%CI，0.045-0.048），3年为0.178（95%CI，0.168-0.195），5年为0.144（95%CI，0.133-0.165）。此外，还为患者制定了PFS和OS的动态个体化风险概况。创建了一个基于网络的工具，以促进这些动态预测模型在临床环境中对新患者的实际应用。
在这项回顾性预后研究中，动态模型以及用于个性化预测的基于网络的工具通过纳入多个纵向标志物显示出更好的性能。
