Postoperative Adjuvant Imatinib Therapy-Associated Nomogram to Predict Overall Survival of Gastrointestinal Stromal Tumor

BACKGROUND: Adjuvant imatinib therapy has been shown to improve overall survival (OS) of gastrointestinal stromal tumor (GIST) significantly. Few nomograms combining the use of adjuvant imatinib and clinicopathological characteristics estimate the outcome of patients. We aimed to establish a more comprehensive nomogram for predicting OS in patients with GIST. METHODS: In total, 1310 GIST patients undergoing curative resection at four high-volume medical centers between 2001 and 2015 were enrolled. Independent prognostic factors were identified by multivariate Cox analysis. Eligible patients were randomly assigned in a ratio of 7:3 into a training set (916 cases) and a validation set (394 cases). A nomogram was established by R software and its predictive power compared with that of the modified National Institutes of Health (NIH) classification using time-dependent receiver operating characteristic (ROC) curves and calibration plot. RESULTS: Age, tumor site, tumor size, mitotic index, postoperative imatinib and diagnostic delay were identified as independent prognostic parameters and used to construct a nomogram. Of note, diagnostic delay was for the first time included in a prognostic model for GIST. The calibrated nomogram resulted in predicted survival rates consistent with observed ones. And the decision curve analysis suggested that the nomogram prognostic model was clinically useful. Furthermore, time-dependent ROC curves showed the nomogram exhibited greater discrimination power than the modified NIH classification in 3- and 5-year survival predictions for both training and validation sets (all P < 0.05). CONCLUSIONS: Postoperative adjuvant imatinib therapy improved the survival of GIST patients. We developed and validated a more comprehensive prognostic nomogram for GIST patients, and it could have important clinical utility in improving individualized predictions of survival risks and treatment decision-making.