4.7 Article

Clinical parameters model for predicting pathologic complete response following preoperative chemoradiation in patients with esophageal cancer

期刊

ANNALS OF ONCOLOGY
卷 23, 期 10, 页码 2638-2642

出版社

OXFORD UNIV PRESS
DOI: 10.1093/annonc/mds210

关键词

chemoradiation; esophageal cancer; esophageal preservation; nomogram; prediction of response

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资金

  1. Dallas family fund
  2. Park family fund
  3. Smith family fund
  4. Cantu family fund
  5. Kevin Fund
  6. Sultan Fund
  7. River Creek Foundation
  8. Aaron and Martha Schecter Private Foundation
  9. Multidisciplinary Research Program at MD Anderson Cancer Center, Houston, USA
  10. National Institutes of Health through MD Anderson's Cancer Center (National Cancer Institute, Bethesda, USA) [CA016672]

向作者/读者索取更多资源

Approximately 25% of patients with esophageal cancer (EC) who undergo preoperative chemoradiation, achieve a pathologic complete response (pathCR). We hypothesized that a model based on clinical parameters could predict pathCR with a high (>= 60%) probability. We analyzed 322 patients with EC who underwent preoperative chemoradiation. All the patients had baseline and postchemoradiation positron emission tomography (PET) and pre- and postchemoradiation endoscopic biopsy. Logistic regression models were used for analysis, and cross-validation via the bootstrap method was carried out to test the model. The 70 (21.7%) patients who achieved a pathCR lived longer (median overall survival [OS], 79.76 months) than the 252 patients who did not achieve a pathCR (median OS, 39.73 months; OS, P = 0.004; disease-free survival, P = 0.003). In a logistic regression analysis, the following parameters contributed to the prediction model: postchemoradiation PET, postchemoradiation biopsy, sex, histologic tumor grade, and baseline T-EUS stage. The area under the receiver-operating characteristic curve was 0.72 (95% confidence interval [CI] 0.662-0.787); after the bootstrap validation with 200 repetitions, the bias-corrected AU-ROC was 0.70 (95% CI 0.643-0.728). Our data suggest that the logistic regression model can predict pathCR with a high probability. This clinical model could complement others (biomarkers) to predict pathCR.

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