Purpose: Accurate identification of the prostatic urethra and bladder can help determine dosing and evaluate urinary toxicity during intensity-modulated radiation therapy (IMRT) planning in patients with localized prostate cancer. However, it is challenging to locate the prostatic urethra in planning computed tomography (pCT). In the present study, we developed a multiatlas-based auto-segmentation method for prostatic urethra identification using deformable image registration accuracy prediction with machine learning (ML) and assessed its feasibility. Methods: We examined 120 patients with prostate cancer treated with IMRT. All patients underwent temporary urinary catheter placement for identification and contouring of the prostatic urethra in pCT images (ground truth). Our method comprises the following three steps: (a) select four atlas datasets from the atlas datasets using the deformable image registration (DIR) accuracy prediction model, (b) deform them by structure-based DIR, (3) and propagate urethra contour using displacement vector field calculated by the DIR. In (a), for identifying suitable datasets, we used the trained support vector machine regression (SVR) model and five feature descriptors (e.g., prostate volume) to increase DIR accuracy. This method was trained/validated using 100 patients and performance was evaluated within an independent test set of 20 patients. Fivefold cross-validation was used to optimize the hype parameters of the DIR accuracy prediction model. We assessed the accuracy of our method by comparing it with those of two others: Acostas method-based patient selection (previous study method, by Acosta et al.), and the Waterman's method (defines the prostatic urethra based on the center of the prostate, by Waterman et al.). We used the centerlines distance (CLD) between the ground truth and the predicted prostatic urethra as the evaluation index. Results: The CLD in the entire prostatic urethra was 2.09 AE 0.89 mm (our proposed method), 2.77 AE 0.99 mm (Acosta et al., P = 0.022), and 3.47 AE 1.19 mm (Waterman et al., P < 0.001); our proposed method showed the highest accuracy. In segmented CLD, CLD in the top 1/3 segment was highly improved from that of Waterman et.al. and was slightly improved from that of Acosta et.al., with results of 2.49 AE 1.78 mm (our proposed method), 2.95 AE 1.75 mm (Acosta et al., P = 0.42), and 5.76 AE 3.09 mm (Waterman et al., P < 0.001). Conclusions: We developed a DIR accuracy prediction model-based multiatlas-based auto-segmentation method for prostatic urethra identification. Our method identified prostatic urethra with mean error of 2.09 mm, likely due to combined effects of SVR model employment in patient selection, modified atlas dataset characteristics and DIR algorithm. Our method has potential utility in prostate cancer IMRT and can replace use of temporary indwelling urinary catheters.
In external radiotherapy of head and neck (HN) cancers, the reduction of irradiation accuracy due to HN volume reduction often causes a problem. Adaptive radiotherapy (ART) can effectively solve this problem; however, its application to all cases is impractical because of cost and time. Therefore, finding priority cases is essential. This study aimed to predict patients with HN cancers are more likely to need ART based on a quantitative measure of large HN volume reduction and evaluate model accuracy. The study included 172 cases of patients with HN cancer who received external irradiation. The HN volume was calculated using cone-beam computed tomography (CT) for irradiation-guided radiotherapy for all treatment fractions and classified into two groups: cases with a large reduction in the HN volume and cases without a large reduction. Radiomic features were extracted from the primary gross tumor volume (GTV) and nodal GTV of the planning CT. To develop the prediction model, four feature selection methods and two machine-learning algorithms were tested. Predictive performance was evaluated by the area under the curve (AUC), accuracy, sensitivity and specificity. Predictive performance was the highest for the random forest, with an AUC of 0.662. Furthermore, its accuracy, sensitivity and specificity were 0.692, 0.700 and 0.813, respectively. Selected features included radiomic features of the primary GTV, human papillomavirus in oropharyngeal cancer and the implementation of chemotherapy; thus, these features might be related to HN volume change. Our model suggested the potential to predict ART requirements based on HN volume reduction .
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