Gastrointestinal Cancer Center, Ordensklinikum Linz, Seilerstaette 4, 4010, Linz, Austria.
Department of Internal Medicine I, Wilhelminenspital, Vienna, Austria.
BMC Cancer. 2020 Nov 25;20(1):1149. doi: 10.1186/s12885-020-07656-w.
Metastatic colorectal cancer (mCRC) remains a lethal disease. Survival, however, is increasing due to a growing number of treatment options. Yet due to the number of prognostic factors and their interactions, prediction of mortality is difficult. The aim of this study is to provide a clinical model supporting prognostication of mCRC mortality in daily practice.
Data from 1104 patients with mCRC in three prospective cancer datasets were used to construct and validate Cox models. Input factors for stepwise backward method variable selection were sex, RAS/BRAF-status, microsatellite status, treatment type (no treatment, systemic treatment with or without resection of metastasis), tumor load, location of primary tumor, metastatic patterns and synchronous or metachronous disease. The final prognostic model for prediction of survival at two and 3 years was validated via bootstrapping to obtain calibration and discrimination C-indices and dynamic time dependent AUC.
Age, sidedness, number of organs with metastases, lung as only site of metastasis, BRAF mutation status and treatment type were selected for the model. Treatment type had the most prominent influence on survival (resection of metastasis HR 0.26, CI 0.21-0.32; any treatment vs no treatment HR 0.31, CI 0.21-0.32), followed by BRAF mutational status (HR 2.58, CI 1.19-1.59). Validation showed high accuracy with C-indices of 72.2 and 71.4%, and dynamic time dependent AUC's of 76.7 ± 1.53% (both at 2 or 3 years), respectively.
The mCRC mortality prediction model is well calibrated and internally valid. It has the potential to support both, clinical prognostication for treatment decisions and patient communication.
转移性结直肠癌（mCRC）仍然是一种致命疾病。然而，由于治疗选择的增多，生存时间正在延长。但由于预后因素的数量及其相互作用，死亡预测仍然很困难。本研究旨在提供一种临床模型，以支持日常实践中 mCRC 死亡率的预后。
使用来自三个前瞻性癌症数据集的 1104 例 mCRC 患者的数据构建和验证 Cox 模型。逐步向后法变量选择的输入因素为性别、RAS/BRAF 状态、微卫星状态、治疗类型（无治疗、有或无转移灶切除的全身治疗）、肿瘤负荷、原发肿瘤位置、转移模式以及同步或异时性疾病。通过自举法对用于预测 2 年和 3 年生存的最终预后模型进行验证，以获得校准和区分 C 指数和动态时间依赖 AUC。
年龄、肿瘤侧别、转移器官数量、肺为唯一转移部位、BRAF 突变状态和治疗类型被选为模型的选择因素。治疗类型对生存的影响最大（转移灶切除术 HR 0.26，CI 0.21-0.32；任何治疗与无治疗 HR 0.31，CI 0.21-0.32），其次是 BRAF 突变状态（HR 2.58，CI 1.19-1.59）。验证显示具有高准确性，C 指数分别为 72.2%和 71.4%，动态时间依赖 AUC 分别为 76.7±1.53%（均在 2 年或 3 年）。
mCRC 死亡率预测模型具有良好的校准度和内部有效性。它有可能支持临床预后判断治疗决策和患者沟通。
