AUTHOR=Jin Xin , Wu Yibin , Feng Yun , Lin Zhenhai , Zhang Ning , Yu Bingran , Mao Anrong , Zhang Ti , Zhu Weiping , Wang Lu 

TITLE=A population-based predictive model identifying optimal candidates for primary and metastasis resection in patients with colorectal cancer with liver metastatic

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.899659

DOI=10.3389/fonc.2022.899659

ISSN=2234-943X

ABSTRACT=Background: The survival benefit of primary and metastasis resection for patients with colorectal cancer 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 stratified patients into sub-population based on their response to surgery. 
Methods: 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 from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 were identified for model construction and another CRLM patients were enrolled for an internal validation.  Another data included CRLM patients from our center were obtained for an external validation. Calibration plots, the area under curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the nomogram compared with tumor-node-metastasis (TNM) classification. Kaplan-Meier analysis was performed to examine whether this model would distinguish patients who could benefit from surgery.
Results: Totally, 1,220  patients were identified, and 881 (72.2%) underwent the resection. cancer specific survival (CSS) for surgery group was better than the no-surgery groups (41 months vs 14 months, P<0.001). Five factors were found associated with CSS and adopted to build the nomograms: Age, T stage, N stage, Neoadjuvant chemotherapy, 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 the actual survival outcomes. Beneficial & surgery group survived longer significantly than non-beneficial & surgery group (HR =0.21, 95% CI, 0.17–0.27, P<0.001), but no difference was observed between non-beneficial & surgery and non-surgery group (HR =0.89, 95% CI, 0.71–1.13, P=0.344). 
Conclusions: An accurate and easy-to-use CRLM nomogram has been developed and can be applied to identify optimal candidates for resection of primary and metastatic lesions among CRLM patients.