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• W4387033979 abstract "A recurrence-free survival (RFS) prediction model was developed and validated for patients with locally advanced esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy (NCRT) in combination with surgery.We included 282 patients with esophageal squamous cell carcinoma who received neoadjuvant chemoradiotherapy (NCRT) combined with surgery, constructed three models incorporating pathological factors, investigated the discrimination and calibration of each model, and compared the clinical utility of each model using the net reclassification index (NRI) and the integrated discrimination index (IDI).Multivariable analysis showed that pathologic complete response (pCR) and lymph node tumor regression grading (LN-TRG) (p < 0.05) were independent prognostic factors for RFS. LASSO regression screened six correlates of LN-TRG, vascular invasion, nerve invasion, degree of differentiation, platelet grade, and a total diameter of residual cancer in lymph nodes to build model three, which was consistent in terms of efficacy in the training set and validation set. Kaplan-Meier (K-M) curves showed that all three models were able to distinguish well between high- and low-risk groups (p < 0.01). The NRI and IDI showed that the clinical utility of model 2 was slightly better than that of model 1 (p > 0.05), and model 3 was significantly better than that of model 2 (p < 0.05).Clinical prediction models incorporating LN-TRG factors have high predictive efficacy, can help identify patients at high risk of recurrence after neoadjuvant therapy, and can be used as a supplement to the AJCC/TNM staging system while offering a scientific rationale for early postoperative intervention." @default.
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• W4387033979 date "2023-09-26" @default.
• W4387033979 modified "2023-10-06" @default.
• W4387033979 title "Development and Validation of a Recurrence-Free Survival Prediction Model for Locally Advanced Esophageal Squamous Cell Carcinoma with Neoadjuvant Chemoradiotherapy" @default.
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• W4387033979 doi "https://doi.org/10.1245/s10434-023-14308-3" @default.
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• W4387033979 hasPublicationYear "2023" @default.
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