Jin Cheng-Wu, Lv Sun-Yuan, Yang Can, Tan Mao, Shelat Vishal G, Ambe Peter C, Price Timothy, Song Li, Peng Wei, Jian Shu-Lang, Liu Heng
Department of General Surgery, Chengdu Fifth People's Hospital, Chengdu, China.
Department of General Surgery, Tan Tock Seng Hospital, Singapore, Singapore.
Transl Cancer Res. 2025 Jul 30;14(7):4381-4398. doi: 10.21037/tcr-2025-1084. Epub 2025 Jul 24.
Primary tumor resection (PTR) can improve the prognosis and survival of some patients with metastatic colorectal cancer (mCRC). However, selecting candidates that may benefit from this intervention may be challenging. Therefore, we aim to construct a predictive model to help identify the most eligible candidates for PTR.
Propensity score matching (PSM) was used to balance the baseline characteristics of the patients. Patients in the surgical group were further allocated to either a beneficial or a non-beneficial cohort based on whether their survival time exceeded the median overall survival (mOS) time of the non-surgical group. A multivariate Cox analysis was then conducted to select independent prognostic risk factors the surgical group. Finally, multivariate logistic regression was used to establish a predictive model based on the demographic characteristics, and the calibration curves, area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and a decision curve analysis (DCA) were used to validate and assess the model accuracy and clinical prediction ability.
A total of 11,763 mCRC patients were enrolled in the study, of whom 8,808 (74.88%) underwent PTR. After PSM, the median cancer-specific survival (CSS) was 29 months in the surgical group and 16 months in the non-surgical group (P<0.001). Based on the logistic regression, 10 covariates [age, ethnicity, negative or positive CEA, TNM staging, grade, bone metastasis, liver metastasis, histology, primary tumor site, distant metastasis surgery (or no surgery), and chemotherapy] were identified and used to construct the predictive model, using a training and a validation group. The AUC values of the nomograph were 0.727 in the training group and 0.742 in the validation group. The calibration curves, DCA and Kaplan-Meier (K-M) analysis results suggest that the predictive model was able to accurately predict the likelihood of a patient benefiting from PTR (P<0.001).
This study constructed and validated a predictive model to help clinicians identify patients with mCRC who are most likely to benefit from PTR.
原发性肿瘤切除术（PTR）可改善部分转移性结直肠癌（mCRC）患者的预后和生存率。然而，选择可能从该干预措施中获益的患者可能具有挑战性。因此，我们旨在构建一个预测模型，以帮助识别最适合接受PTR的患者。
采用倾向评分匹配（PSM）来平衡患者的基线特征。手术组患者根据其生存时间是否超过非手术组的中位总生存期（mOS）时间，进一步分为获益组或非获益组。然后对手术组进行多变量Cox分析，以选择独立的预后危险因素。最后，使用多变量逻辑回归基于人口统计学特征建立预测模型，并使用校准曲线、受试者工作特征（ROC）曲线的曲线下面积（AUC）以及决策曲线分析（DCA）来验证和评估模型的准确性及临床预测能力。
本研究共纳入11,763例mCRC患者，其中8,808例（74.88%）接受了PTR。PSM后，手术组的中位癌症特异性生存期（CSS）为29个月，非手术组为16个月（P<0.001）。基于逻辑回归，确定了10个协变量[年龄、种族、癌胚抗原（CEA）阴性或阳性、TNM分期、分级、骨转移、肝转移、组织学类型、原发肿瘤部位、远处转移手术（或未手术）以及化疗]，并使用训练组和验证组构建预测模型。列线图在训练组中的AUC值为0.727，在验证组中为0.742。校准曲线、DCA和Kaplan-Meier（K-M）分析结果表明，该预测模型能够准确预测患者从PTR中获益的可能性（P<0.001）。
本研究构建并验证了一个预测模型，以帮助临床医生识别最有可能从PTR中获益的mCRC患者。
