Prediction model to estimate overall survival benefit of postoperative radiotherapy for resected major salivary gland cancers.

Oral oncology(2022)

引用 3|浏览14
暂无评分
摘要
OBJECTIVES:To develop and validate a prediction model to estimate overall survival (OS) with and without postoperative radiotherapy (PORT) for resected major salivary gland (SG) cancers. MATERIALS AND METHODS:Adults in the National Cancer Database diagnosed with invasive non-metastatic major SG cancer between 2004 and 2015 were identified. Exclusion criteria included prior malignancy, pT1N0 or unknown stage, no or unknown surgery, and neoadjuvant therapy. Cox proportional hazards models evaluated the effect of covariates on OS. A multivariate regression model was utilized to predict 2-, 5-, and 10-year OS. Internal cross-validation was performed using 50-50 hold-out and Harrell's concordance index. RESULTS:18,400 subjects met inclusion criteria, including 9,721 (53%) who received PORT. Distribution of SG involvement was 86% parotid, 13% submandibular, and 1% sublingual. Median follow-up for living subjects was 4.9 years. PORT was significantly associated with improved OS for the following subgroups by log-rank test: pT3 (p < 0.001), pT4 (p < 0.001), high grade (p < 0.001), node-positive (p < 0.001), and positive margin (p < 0.001). The following variables were incorporated into a multivariate model: age, sex, Charlson-Deyo comorbidity score, involved SG, pathologic T-stage, grade, margin status, ratio of nodal positivity, and PORT. The resulting model based on data from 6,138 subjects demonstrated good accuracy in predicting OS, with Harrell's concordance index of 0.73 (log-rank p < 0.001). CONCLUSION:This cross-validated prediction model estimates 2-, 5-, and 10-year differences in OS based on receipt of PORT for resected major SG cancers using readily available clinicopathologic features. Clinicians can utilize this tool to aid personalized adjuvant therapy decisions.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要