A population-based predictive model identifying optimal candidates for primary and metastasis resection in patients with colorectal cancer with liver metastatic.
A population-based predictive model identifying optimal candidates for primary and metastasis resection in patients with colorectal cancer with liver metastatic.
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基于人群的预测模型，确定肝转移结直肠癌患者原发性和转移性切除的最佳候选者
DOI：
10.3389/fonc.2022.899659
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发表时间：
2022
影响因子：
4.7
通讯作者：
Wang, Lu
中科院分区：
文献类型：
--
作者：
Jin, Xin;Wu, Yibin;Feng, Yun;Lin, Zhenhai;Zhang, Ning;Yu, Bingran;Mao, Anrong;Zhang, Ti;Zhu, Weiping;Wang, Lu
关键词：
原发性和转移性切除术对结直肠癌伴肝转移（CRLM）患者的生存益处已被观察到，但用于区分哪些个体将从手术中受益的方法尚未明确。在此，开发了一种预测模型，以根据患者对手术的反应将患者分为亚群。我们通过比较根治性手术与非手术患者，评估了诊断为结直肠癌肝转移的成人患者的生存获益。在2004年至2015年期间纳入监测、流行病学和最终结果（SEER）数据库的CRLM患者被确定用于模型构建。从我们中心获得的其他数据（包括CRLM患者）用于外部验证。校准图，曲线下面积（AUC），决策曲线分析（DCA）被用来评估诺模图的性能相比，肿瘤淋巴结转移（TNM）分类。进行Kaplan-Meier分析以检查该模型是否能够区分可从手术中获益的患者。共确定了1，220例合格患者，其中881例（72.2%）接受了结直肠和肝脏切除术。手术组的癌症特异性生存期（CSS）显著优于非手术组（41个月vs. 14个月，p < 0.001）。发现与CSS相关的五个因素并用于构建诺模图，即，年龄、T分期、N分期、新辅助化疗和原发肿瘤位置。CRLM诺模图的AUC比TNM分类更能识别手术治疗中获益的患者（训练集，0.826 [95% CI，0.786-0.866] vs. 0.649 [95% CI，0.598-0.701];内部验证集，0.820 [95% CI，0.741-0.899] vs. 0.635 [95% CI，0.539-0.731];外部验证集，0.763 [95% CI，0.691-0.836] vs. 0.626 [95% CI，0.542-0.710]）。校准曲线显示预测的和实际的生存结果之间非常一致。DCA显示诺模图比TNM分期系统显示出更多的临床益处。受益和手术组的生存时间显著长于非受益和手术组（HR = 0.21，95% CI，0.17-0.27，p < 0.001），但非受益和手术组与非手术组之间没有观察到差异（HR = 0.89，95% CI，0.71-1.13，p = 0.344）。一个准确的和易于使用的CRLM诺模图已经开发出来，并可以应用于确定CRLM患者的原发性和转移性病变切除的最佳候选人。
The survival benefit of primary and metastatic resection for patients with colorectal cancer with liver metastasis (CRLM) has been observed, but methods for discriminating which individuals would benefit from surgery have been poorly defined. Herein, a predictive model was developed to stratify patients into sub-population based on their response to surgery. We assessed the survival benefits for adults diagnosed with colorectal liver metastasis by comparing patients with curative surgery vs. those without surgery. CRLM patients enrolled in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 were identified for model construction. Other data including CRLM patients from our center were obtained for external validation. Calibration plots, the area under the curve (AUC), and decision curve analysis (DCA) were used to evaluate the performance of the nomogram compared with the tumor–node–metastasis (TNM) classification. The Kaplan–Meier analysis was performed to examine whether this model would distinguish patients who could benefit from surgery. A total of 1,220 eligible patients were identified, and 881 (72.2%) underwent colorectal and liver resection. Cancer-specific survival (CSS) for the surgery group was significantly better than that for the no-surgery group (41 vs. 14 months, p < 0.001). Five factors were found associated with CSS and adopted to build the nomograms, i.e., age, T stage, N stage, neoadjuvant chemotherapy, and primary tumor position. The AUC of the CRLM nomogram showed a better performance in identifying patients who could obtain benefits in the surgical treatment, compared with TNM classification (training set, 0.826 [95% CI, 0.786–0.866] vs. 0.649 [95% CI, 0.598–0.701]; internal validation set, 0.820 [95% CI, 0.741–0.899] vs. 0.635 [95% CI, 0.539–0.731]; external validation set, 0.763 [95% CI, 0.691–0.836] vs. 0.626 [95% CI, 0.542–0.710]). The calibration curves revealed excellent agreement between the predicted and actual survival outcomes. The DCA showed that the nomogram exhibited more clinical benefits than the TNM staging system. The beneficial and surgery group survived longer significantly than the non-beneficial and surgery group (HR = 0.21, 95% CI, 0.17–0.27, p < 0.001), but no difference was observed between the non-beneficial and surgery and non-surgery groups (HR = 0.89, 95% CI, 0.71–1.13, p = 0.344). An accurate and easy-to-use CRLM nomogram has been developed and can be applied to identify optimal candidates for the resection of primary and metastatic lesions among CRLM patients.
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