ABSTRACT
Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients who received whole pelvic radiotherapy (WPRT). This is a retrospective study with methods based on best practice procedures for radiomics research. Sixty-four patients were selected and randomly assigned to training (n = 45) and testing (n = 19) cohorts for radiomics model development with five major steps: pCT image acquisition using a Philips Big Bore CT simulator; multiple manual segmentations of clinical target volume for the prostate (CTVprostate) on the pCT images; feature extraction from the CTVprostate using PyRadiomics; feature selection for overfitting avoidance; and model development with three-fold cross-validation. The radiomics model and signature performances were evaluated based on the area under the receiver operating characteristic curve (AUC) as well as accuracy, sensitivity and specificity. This study's results show that our pCT-based radiomics model was able to predict the six-year progression-free survival of the high-risk localized PCa patients who received the WPRT with highly consistent performances (mean AUC: 0.76 (training) and 0.71 (testing)). These are comparable to findings of other similar studies including those using magnetic resonance imaging (MRI)-based radiomics. The accuracy, sensitivity and specificity of our radiomics signature that consisted of two texture features were 0.778, 0.833 and 0.556 (training) and 0.842, 0.867 and 0.750 (testing), respectively. Since CT is more readily available than MRI and is the standard-of-care modality for PCa WPRT planning, pCT-based radiomics could be used as a routine non-invasive approach to the prognostic prediction of WPRT treatment outcomes in high-risk localized PCa.
ABSTRACT
OBJECTIVES: To analyze the correlation between dose-volume-histograms (DVHs) with three patterns (edema, enhancement, and necrosis) of temporal lobe injury (TLI) in patients receiving intensity modulated radiation therapy (IMRT) for nasopharyngeal carcinoma (NPC) and to determine optimal thresholds to predict the incidence of each TLI pattern, with particular emphasis on the relationship between edema volume and the risk of enhancement and necrosis. MATERIALS AND METHODS: A cohort of 4186 NPC patients treated with IMRT was retrospectively reviewed with TLI presenting in 188 patients. The atlases of complication incidence (ACI) for each pattern were constructed using DVH curves of temporal lobes. Optimal threshold for predicting incidence of each pattern was determined using the point closest to top-left of the plot. The accuracy of using edema volume to predict enhancement and necrosis incidence was evaluated via area under curve (AUC) of receiver operator characteristics (ROC). RESULTS: All DVH parameters, Dmean, Dmax, D0.25cc, D0.5cc, D1cc, D3cc, D6cc, V20Gy, V30Gy, V40Gy, V50Gy, V60Gy, and V70Gy, except Dmin showed statistically significant differences between subgroups of each pattern (pâ¯<â¯0.05). For predicting incidence of each pattern, optimal DVH thresholds over the range of D0.25-D1cc, Dmean and V20-V70 were derived. The optimal thresholds of edema volume for predicting enhancement were 0.96 and 2.2cc and for predicting necrosis were 0.94 and 11.5cc. CONCLUSION: Optimal DVH thresholds were generated for limiting risk of each injury pattern. Edema volume was a strong predictor for risk of enhancement and necrosis, which could potentially be reduced by lowering edema volume below threshold.
