Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.

Background/Aims: We surveyed sickle cell disease (SCD) patients who transitioned from pediatric care at Texas Children’s Hematology Center (TCHC) to adult care to determine the characteristics of patients with an adult SCD provider, continuation rates of pre-transition therapies, and patient perceptions of the transition process. Methods: A cross-sectional study was conducted by telephone survey of 44 young adults with SCD, aged 19–29 years, who transitioned from TCHC to adult care within the last 15 years. Results: Findings of the 23-item questionnaire revealed that transitioned patients with current adult providers (68.2%) were more likely to have seen a provider within 6 months of transition (p = 0.023) and to have been on hydroxyurea and/or monthly blood transfusions pre-transition (p = 0.021) than transitioned patients without a provider; 83% of patients on pre-transition hydroxyurea reported continuing hydroxyurea after transition. Transition challenges included inadequate preparation, difficulty finding knowledgeable adult providers, and lack of healthcare insurance/coverage. Conclusion: Transition to adult providers is predicted by establishing care with an adult SCD provider within 6 months of transition and being on pre-transition disease-modifying therapy. Transition may be improved if pediatric hematology centers assist and verify adult provider contact within 6 months of transition and engage patients of all disease severity during transition.

Repetitive administration of low-dose cisplatin concurrent with whole pelvic radiation is associated with magnesium wasting. However, choice of diuretic with pretreatment hydration had no significant impact on the severity of this adverse effect.

Purpose The radiation oncology workforce in the United States is comparatively less diverse than the U.S. population and U.S. medical school graduates. Workforce diversity correlates with higher quality care and outcomes. The purpose of this study was to determine whether student members of the American Society for Radiation Oncology (ASTRO) are any more diverse than resident members-in-training using the recently established medical student membership category. Methods and Materials Self-reported sex, race and Hispanic ethnicity, medical school, and degree(s) earned for all medical students (n = 268) and members-in-training (n = 713) were collected from the ASTRO membership database. International members were excluded. The χ 2 test was used to assess for differences between subgroups. Results Compared with members-in-training, student members were more likely to be female (40.0% vs 31.5%, P = .032), black or African American (10.7% vs 4.8%, P = .009), candidates for or holders of a DO rather than MD degree (5.2% vs 1.5%, P = .002), and from a U.S. medical school that is not affiliated with a radiation oncology residency program (30.5% vs 20.9%, P = .001). There was no significant difference in self-reported Hispanic ethnicity (7.3% vs 5.4%, P = .356). There were no indigenous members in either category assessed. Conclusions Medical student members of ASTRO are more diverse in terms of black race, female sex, and osteopathic training, though not in terms of Hispanic ethnicity or nonmultiracial indigenous background, than the members-in-training. Longitudinal engagement with these students and assessment of the factors leading to specialty retention versus attrition may increase diversity, equity, and inclusion in radiation oncology.

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