Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy Using Spatial-Temporal ¹⁸F-FDG PET Features, Clinical Parameters, and Demographics

Purpose

To construct predictive models using comprehensive tumor features for the evaluation of tumor response to neoadjuvant chemoradiation therapy (CRT) in patients with esophageal cancer.

Methods and Materials

This study included 20 patients who underwent trimodality therapy (CRT + surgery) and underwent ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) both before and after CRT. Four groups of tumor features were examined: (1) conventional PET/CT response measures (eg, standardized uptake value [SUV]_max, tumor diameter); (2) clinical parameters (eg, TNM stage, histology) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, cross-validations being used to avoid model overfitting. Prediction accuracy was assessed by area under the receiver operating characteristic curve (AUC), and precision was evaluated by confidence intervals (CIs) of AUC.

Results

When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). With the use of spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications)—results that were significantly better than when conventional PET/CT measures or clinical parameters and demographics alone were used. For groups with many tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than did the LR model.

Conclusions

The SVM model that used all features including spatial-temporal PET features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer.

Introduction

Esophageal cancer remains one of the most lethal malignancies, with a 5-year relative survival rate of only 17% (1) despite continued advances in therapy. In the United States, it is estimated that 17,460 patients received diagnoses of esophageal cancer and 15,070 died of the disease in 2012 (1). The preferred primary treatment strategy for locally advanced esophageal cancer has been transitioning from surgery (esophagectomy) to trimodality therapy, which consists of concurrent neoadjuvant chemoradiation therapy (CRT) followed by surgery (2). Recently, it was suggested that not all patients benefit from surgery after induction CRT and that definitive CRT (CRT alone) could also become an option (3). Evidence suggests that surgery after CRT can significantly improve local control 4, 5. These improvements in local control, however, have been tempered by the increased mortality (9% to 12%) and morbidity (30%) compared with CRT alone (mortality, 0.8% to 3.5%). Several studies have shown that tumor response to CRT remains an important predictor of both local control and overall survival 3, 4, 5. Complete responders to CRT appear to have superior outcomes, regardless of whether they undergo surgical resection. These data also support that the addition of resection can improve outcomes for patients who are discovered to have residual tumor after the completion of CRT. Given the added mortality and morbidity of surgery after CRT, and the high local failure rate for CRT alone, it is critical to accurately identify patients who respond to CRT so that surgery may be safely deferred. It is equally important to accurately identify patients who do not respond to CRT so that early surgical salvage can be initiated.

Recent studies have emerged suggesting that spatial positron emission tomography/computed tomography (PET/CT) features, including tumor volume (6), tumor shape (7), total glycolytic volume (8), and spatial patterns (texture features) (9), are more informative than the traditional response measure with maximum standardized uptake values (SUV_max) in various tumors. The authors demonstrated that comprehensive spatial-temporal ¹⁸F-fluorodeoxyglucose (FDG) PET features were useful predictors of pathologic tumor response to CRT in esophageal cancer (10). The diversity of the new features suggests that it would be advantageous to combine multiple features in evaluation of tumor response (11) instead of traditional PET response criteria that are based on cutoff values of a single measure 8, 12. The objective of this study was to construct sophisticated tumor response models using comprehensive tumor features to accurately and precisely predict pathologic tumor response to CRT in patients with esophageal cancer.