Predicting peritoneal recurrence after radical gastrectomy for gastric cancer: Validation of a prediction model (PERI-Gastric 1 and PERI-Gastric 2) on a Korean database.

European Journal of Surgical Oncology(2024)

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摘要
Background Peritoneal recurrence is a significant cause of treatment failure after radical gastrectomy for gastric cancer. The prediction of metachronous peritoneal recurrence would have a significantly impact risk stratification and tailored treatment planning. This study aimed to externally validate the previously established PERI-Gastric 1 and 2 models to assess their generalizability in an independent population. Methods Retrospective external validation was conducted on a cohort of 8,564 patients who underwent elective gastrectomy for stage Ib-IIIc gastric cancer between 1998 and 2018 at the Yonsei Cancer Center. Discrimination was tested using the area under the receiver operating characteristic curves (AUROC). Accuracy was tested by plotting observations against the predicted risk of peritoneal recurrence and analyzing the resulting calibration plots. Clinical usefulness was tested with a decision curve analysis. Results In the validation cohort, PERI-Gastric 1 and PERI-Gastric 2 exhibited an AUROC of 0.766 (95% C.I. 0.752-0.778) and 0.767 (95% C.I. 0.755-0.780), a calibration-in-the-large of 0.935 and 0.700, a calibration belt with a 95% C.I. over the bisector in the risk range of 24%-33% and 35%-47%. The decision curve analysis revealed a positive net benefit in the risk range of 10%-42% and 15%-45%, respectively. Conclusions This study presents the external validation of the PERI-Gastric 1 and 2 scores in an Eastern population. The models demonstrated fair discrimination and satisfactory calibration for predicting the risk of peritoneal recurrence after radical gastrectomy, even in Eastern patients. PERI-Gastric 1 and 2 scores could also be applied to predict the risk of metachronous peritoneal recurrence in Eastern populations.
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关键词
gastric cancer,prediction models,peritoneal carcinomatosis
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