Prediction of survival in patients with colorectal liver metastases- development and validation of a prognostic score model

European Journal of Surgical Oncology(2022)

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摘要
BACKGROUND:Metastatic spread of colorectal cancer to the liver impacts prognosis. Advances in chemotherapy have resulted in increased resectability rates and thereby improved survival in patients with colorectal liver metastases (CRLM). However, criteria are needed to ensure that patients selected for hepatic resection benefit from the invasive therapy. The study aimed to construct a predictive model for overall survival (OS) in patients with CRLM, based on preoperatively available information. METHODS:The retrospective cohort study reviewed all patients with CRLM discussed at multidisciplinary team conference at Karolinska University Hospital, Stockholm, Sweden, 2013-2018. Independent prognostic factors for OS were identified, based on which a score model was generated. The model was validated on patients treated for CRLM at Hôpital Universitaire Paul Brousse, Villejuif, France, 2007-2018. Calibration and discrimination methods were used for internal and external validation. RESULTS:The Swedish development cohort included 1013 patients, the French validation cohort 391 patients. Poor OS was significantly associated with age>60years (hazard ratio (HR) 3.57 (95%CI 2.18-9.94)), number of CRLM (HR 4.59 (2.83-12.20)), diameter of largest CRLM>5 cm (HR 2.59 (1.74-5.03)), right-sided primary tumour (HR 2.98 (2.00-5.80)), extrahepatic disease (HR 4.14 (2.38-15.87)) and non-resectability (HR 0.77 (0.66-0.90)). The C-statistic for prediction of OS was .74, in the development cohort and 0.69 in the validation cohort. CONCLUSION:The presented predictive score model can adequately predict OS for patients at the initial diagnosis of CRLM. The prognostic model could be of clinical value in the management of all patients with CRLM, by predicting individualized survival and thereby facilitating treatment recommendations.
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关键词
Colorectal liver metastases,Overall survival,Predictive model
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