PURPOSE: To extract and study comprehensive spatial-temporal (18)F-labeled fluorodeoxyglucose ([(18)F]FDG) positron emission tomography (PET) features for the prediction of pathologic tumor response to neoadjuvant chemoradiation therapy (CRT) in esophageal cancer. METHODS AND MATERIALS: Twenty patients with esophageal cancer were treated with trimodal therapy (CRT plus surgery) and underwent [(18)F]FDG-PET/CT scans both before (pre-CRT) and after (post-CRT) CRT. The 2 scans were rigidly registered. A tumor volume was semiautomatically delineated using a threshold standardized uptake value (SUV) of ≥2.5, followed by manual editing. Comprehensive features were extracted to characterize SUV intensity distribution, spatial patterns (texture), tumor geometry, and associated changes resulting from CRT. The usefulness of each feature in predicting pathologic tumor response to CRT was evaluated using the area under the receiver operating characteristic curve (AUC) value. RESULTS: The best traditional response measure was decline in maximum SUV (SUVmax; AUC, 0.76). Two new intensity features, decline in mean SUV (SUVmean) and skewness, and 3 texture features (inertia, correlation, and cluster prominence) were found to be significant predictors with AUC values ≥0.76. According to these features, a tumor was more likely to be a responder when the SUVmean decline was larger, when there were relatively fewer voxels with higher SUV values pre-CRT, or when [(18)F]FDG uptake post-CRT was relatively homogeneous. All of the most accurate predictive features were extracted from the entire tumor rather than from the most active part of the tumor. For SUV intensity features and tumor size features, changes were more predictive than pre- or post-CRT assessment alone. CONCLUSION: Spatial-temporal [(18)F]FDG-PET features were found to be useful predictors of pathologic tumor response to neoadjuvant CRT in esophageal cancer.
PURPOSE: To extract and study comprehensive spatial-temporal (18)F-labeled fluorodeoxyglucose ([(18)F]FDG) positron emission tomography (PET) features for the prediction of pathologic tumor response to neoadjuvant chemoradiation therapy (CRT) in esophageal cancer. METHODS AND MATERIALS: Twenty patients with esophageal cancer were treated with trimodal therapy (CRT plus surgery) and underwent [(18)F]FDG-PET/CT scans both before (pre-CRT) and after (post-CRT) CRT. The 2 scans were rigidly registered. A tumor volume was semiautomatically delineated using a threshold standardized uptake value (SUV) of ≥2.5, followed by manual editing. Comprehensive features were extracted to characterize SUV intensity distribution, spatial patterns (texture), tumor geometry, and associated changes resulting from CRT. The usefulness of each feature in predicting pathologic tumor response to CRT was evaluated using the area under the receiver operating characteristic curve (AUC) value. RESULTS: The best traditional response measure was decline in maximum SUV (SUVmax; AUC, 0.76). Two new intensity features, decline in mean SUV (SUVmean) and skewness, and 3 texture features (inertia, correlation, and cluster prominence) were found to be significant predictors with AUC values ≥0.76. According to these features, a tumor was more likely to be a responder when the SUVmean decline was larger, when there were relatively fewer voxels with higher SUV values pre-CRT, or when [(18)F]FDG uptake post-CRT was relatively homogeneous. All of the most accurate predictive features were extracted from the entire tumor rather than from the most active part of the tumor. For SUV intensity features and tumor size features, changes were more predictive than pre- or post-CRT assessment alone. CONCLUSION: Spatial-temporal [(18)F]FDG-PET features were found to be useful predictors of pathologic tumor response to neoadjuvant CRT in esophageal cancer.
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