Prognostic nomogram for T3-T4 primary colorectal cancer patients with perineural invasion after surgery: a Surveillance, Epidemiology, and End Results program database analysis

术后伴有神经周围侵犯的T3-T4期原发性结直肠癌患者的预后列线图:一项基于监测、流行病学和最终结果(SEER)项目数据库的分析

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Abstract

BACKGROUND: Colorectal cancer (CRC) is a common malignancy, with T3-T4 primary CRC characterized by perineural invasion (PNI), representing an aggressive subtype with poor prognosis. This study aimed to develop and validate prognostic nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) in patients with T3-T4 primary CRC and PNI after surgery. METHODS: Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database, focusing on patients diagnosed with T3-T4 primary CRC and PNI between 2000 and 2019. Eligible patients were randomly divided into training and validation cohorts. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic factors, which were integrated into nomograms for OS and CSS. The nomograms were assessed using the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). RESULTS: A total of 7,808 patients met the inclusion criteria. Significant prognostic factors identified in the multivariate analysis included age, sex, race, marital status, site, Tumor (T) stage of the Tumor, Node, Metastasis (TNM) staging system, radiation, regional node positive, liver and lung metastasis, tumor size, histologic type, median household income, and SEER summary stage. The nomograms exhibited good predictive accuracy, with C-indexes of 0.7422 for OS in the training cohort and 0.7428 in the validation cohort. The nomograms were validated using ROC curves, calibration plots, and DCA, which confirmed the models' reliability and clinical utility. CONCLUSIONS: The developed nomograms are robust tools for predicting 3-, 5-, and 10-year OS and CSS in patients with T3-T4 primary CRC and PNI after surgery. These tools help clinicians create personalized treatment plans and improve patient outcomes.

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