Computerized estimation of patient setup errors in portal images based on localized pelvic templates for prostate cancer radiotherapy

Computer-assisted framework for machine-learning-based delineation of GTV regions on datasets of planning CT and PET/CT images

We have proposed a computer-assisted framework for machine-learning-based delineation of gross tumor volumes (GTVs) following an optimum contour selection (OCS) method. The key idea of the proposed framework was to feed image features around GTV contours (determined based on the knowledge of radiation oncologists) into a machine-learning classifier during the training step, after which the classifier produces the 'degree of GTV' for each voxel in the testing step. Initial GTV regions were extracted using a support vector machine (SVM) that learned the image features inside and outside each tumor region (determined by radiation oncologists). The leave-one-out-by-patient test was employed for training and testing the steps of the proposed framework. The final GTV regions were determined using the OCS method that can be used to select a global optimum object contour based on multiple active delineations with a LSM around the GTV. The efficacy of the proposed framework was evaluated in 14 lung cancer cases [solid: 6, ground-glass opacity (GGO): 4, mixed GGO: 4] using the 3D Dice similarity coefficient (DSC), which denotes the degree of region similarity between the GTVs contoured by radiation oncologists and those determined using the proposed framework. The proposed framework achieved an average DSC of 0.777 for 14 cases, whereas the OCS-based framework produced an average DSC of 0.507. The average DSCs for GGO and mixed GGO were 0.763 and 0.701, respectively, obtained by the proposed framework. The proposed framework can be employed as a tool to assist radiation oncologists in delineating various GTV regions.

Secondary bladder cancer after anticancer therapy for prostate cancer: reduced comorbidity after androgen-deprivation therapy

Radiotherapy for prostate cancer is associated with an increased incidence of secondary bladder cancer (BC). We investigated the incidence, clinicopathological characteristics, and prognosis of BC after radiotherapy, surgical therapy, and primary androgen-deprivation therapy (ADT) for prostate cancer. This study included 1,334 Japanese patients with prostate cancer treated with radiotherapy (n=631), surgical therapy (n=437), and primary ADT (n=266). During the median follow-up period of 51.2, 44.8, and 45.5 months, secondary BC occurred in 14 (2.2%), 5 (1.1%), and 0 (0%) of patients with prostate cancer treated with radiotherapy, surgical therapy, and primary ADT, respectively. The 10-year BC-free survival rate was 91.3% in the radiotherapy group, 97.4% in the surgical therapy group, and 100% in the primary ADT group. The rates of intravesical recurrence, progression to muscle-invasive BC, and BC-specific death might be higher in secondary BC after radiotherapy compared with after surgical therapy. There was a significant difference in the incidence of secondary BC among different therapeutic modalities for prostate cancer in Japanese men, indicating significantly lower comorbidity rates of secondary BC after primary ADT for prostate cancer compared with radiotherapy.