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The Logistic Regression and Cox Regression Model for Predicting Local Recurrence, Distant Metastases, and Overall Survival of Rectal Cancer Patients

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z zhou

z zhou*, z zhang , W Hu , H Zhong , J Wang , Fudan university shanghai cancer center, Shanghai, China


SU-E-T-739 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose:The purpose was to develop predicting models for the local recurrence, distant metastases, and overall survival for rectal cancer patients treated with chemoradiotherapy. These models were based on different statistical methods for indicating various relationships between clinical features and follow-up results, which may provide support on decision-making in treatment.
Methods:Models were developed based on logistic regression and cox proportional hazards model using patient data (N=277) from Fudan University Shanghai Cancer Center. The patient data set was randomly split into two proportions as the training and validation datasets. The clinical features used as variables include sex, age, tumor location, RT dose, adjuvant chemotherapy, surgery procedure, clinical tumor stage, and pTNM stage. The Bootstrapping was used for constructing confidence intervals; and the model performance was evaluated by the concordance index (c-index).
Results:The logistic regression model is easy to use because the conditional independence assumption is unnecessary. The Cox regression model can dispose data with time factors so follow-up over a certain time period can be predicted. For the logistic regression model, the c-index for validation are 0.73 (LR), 0.77 (DM), and 0.76(OS), while numerical values for the cox regression model are 0.72 (LR), 0.77 (DM), and 0.70(OS). Both models show the probability to predict follow-up events based on c-index. Clinical tumor stage and pathologic stage are crucial features for both models (p<0.05).
Conclusion:The Logistic Regression and Cox Regression Model can be used for Predicting Local Recurrence, Distant Metastases, and Overall Survival of Rectal Cancer Patients. The performance of models can be further improved with additional parameters provided from different facilities.

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