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. 2012 Oct;23(10):2638-2642.
doi: 10.1093/annonc/mds210. Epub 2012 Jul 24.

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

J A Ajani  1 , A M Correa  2 , W L Hofstetter  2 , D C Rice  2 , M A Blum  3 , A Suzuki  3 , T Taketa  3 , J Welsh  4 , S H Lin  4 , J H Lee  5 , M S Bhutani  5 , W A Ross  5 , D M Maru  6 , H A Macapinlac  7 , J Erasmus  8 , R Komaki  4 , R J Mehran  2 , A A Vaporciyan  2 , S G Swisher  2
Affiliations

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

J A Ajani et al. Ann Oncol. 2012 Oct.

Abstract

Background: 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.

Patients and methods: 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.

Results: 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 (EUS)T 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).

Conclusion: 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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Figures

Figure 1
Figure 1
Nomogram for predicting pathCR based on clinical variables.
See this image and copyright information in PMC

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