The results indicate that the differences between the symmetry and flatness values obtained from the three dosimeter types are not practically important. The results indicate that the differences between the symmetry and flatness values obtained from the three dosimeter types are not practically important. Recommendations for adjuvant treatment for postoperative, early-stage endometrial cancer varies from observation through vaginal brachytherapy alone to pelvic radiation. While observation alone can lead to recurrence, external radiotherapy has increased morbidity. The aim of this study is to show our results with vaginal brachytherapy alone using a multichannel applicator for treatment of early-stage endometrial cancer. Consecutive patients undergoing vaginal brachytherapy alone following surgery for early-stage endometrial cancer were examined. A Miami multichannel vaginal brachytherapy applicator was used to deliver HDR brachytherapy in 62 patients from May 2013 to June 2018. CT scan-based images guided planning. A dose of 5.5-6.5 Gy × 4 fractions was prescribed 5 mm from the surface of the applicator. At a median follow up of 19 months (6-48 months), 93% of patients treated were alive with no recurrence. Two patients had only local recurrence, and 1 was salvaged with external radiotherapy and chemotherapy. There was only one nodal failure and 2 distant failures. There was no grade 2 or higher vaginal, gastrointestinal or genitourinary toxicity. Vaginal brachytherapy alone using a multichannel applicator can be considered for early-stage endometrial cancers without compromising outcomes. Vaginal brachytherapy alone using a multichannel applicator can be considered for early-stage endometrial cancers without compromising outcomes. The objective of this study was to propose an optimal input image quality for a conditional generative adversarial network (GAN) in T1-weighted and T2-weighted magnetic resonance imaging (MRI) images. A total of 2,024 images scanned from 2017 to 2018 in 104 patients were used. The prediction framework of T1-weighted to T2-weighted MRI images and T2-weighted to T1-weighted MRI images were created with GAN. Two image sizes (512 × 512 and 256 × 256) and two grayscale level conversion method (simple and adaptive) were used for the input images. The images were converted from 16-bit to 8-bit by dividing with 256 levels in a simple conversion method. For the adaptive conversion method, the unused levels were eliminated in 16-bit images, which were converted to 8-bit images by dividing with the value obtained after dividing the maximum pixel value with 256. The relative mean absolute error (rMAE ) was 0.15 for T1-weighted to T2-weighted MRI images and 0.17 for T2-weighted to T1-weighted MRI images with an adaptive conversion method, which was the smallest. Moreover, the adaptive conversion method has a smallest mean square error (rMSE) and root mean square error (rRMSE), and the largest peak signal-to-noise ratio (PSNR) and mutual information (MI). The computation time depended on the image size. Input resolution and image size affect the accuracy of prediction. The proposed model and approach of prediction framework can help improve the versatility and quality of multi-contrast MRI tests without the need for prolonged examinations. Input resolution and image size affect the accuracy of prediction. The proposed model and approach of prediction framework can help improve the versatility and quality of multi-contrast MRI tests without the need for prolonged examinations. The purpose of this study was to characterize pre-treatment non-contrast computed tomography (CT) and F-fluorodeoxyglucose positron emission tomography (PET) based radiomics signatures predictive of pathological response and clinical outcomes in rectal cancer patients treated with neoadjuvant chemoradiotherapy (NACR T). An exploratory analysis was performed using pre-treatment non-contrast CT and PET imaging dataset. The association of tumor regression grade (TRG) and neoadjuvant rectal (NAR) score with pre-treatment CT and PET features was assessed using machine learning algorithms. Three separate predictive models were built for composite features from CT + PET. The patterns of pathological response were TRG 0 (n = 13; 19.7%), 1 (n = 34; 51.5%), 2 (n = 16; 24.2%), and 3 (n = 3; 4.5%). There were 20 (30.3%) patients with low, 22 (33.3%) with intermediate and 24 (36.4%) with high NAR scores. Three separate predictive models were built for composite features from CT + PET and analyzed separately for clinical endpoints. Composite features with α = 0.2 resulted in the best predictive power using logistic regression. For pathological response prediction, the signature resulted in 88.1% accuracy in predicting TRG 0 . TRG 1-3; 91% accuracy in predicting TRG 0-1 . TRG 2-3. For the surrogate of DFS and OS, it resulted in 67.7% accuracy in predicting low . intermediate . high NAR scores. The pre-treatment composite radiomics signatures were highly predictive of pathological response in rectal cancer treated with NACR T. https://www.selleckchem.com/products/ha15.html A larger cohort is warranted for further validation. The pre-treatment composite radiomics signatures were highly predictive of pathological response in rectal cancer treated with NACR T. A larger cohort is warranted for further validation. Radiation therapy (RT), an essential treatment of cancer, involves multiple hospital visits. We hypothesized that radiation departments would adjust their work patterns and RT protocols in response to the SARS-CoV-2 pandemic. An electronic survey was sent during April 2020 to an international sample of radiation oncologists. The survey explored various aspects of departmental preparedness, and changes to their institutional RT protocols. A total of 68 radiation oncologists from 13 countries answered the survey. Healthcare systems were at least moderately affected in 76%. Most institutes appeared well prepared for the outbreak regarding the availability of personal protective equipment, tests, and telemedicine/videoconference facilities. Screening for SARS-CoV-2 was applied in 59% of responders. Modification of RT protocols were minor in 66%, significant in 19% and no changes made in 15%. The extent to which protocols were modified correlated with overall healthcare disruption (p = 0.028). Normal fractionation was recommended to continue in 83% and 85% of head & neck, and cervical cancers .