Twitter and Academic Urology in the United States and Canada: A Comprehensive Assessment of the Twitter-verse in 2019.

OBJECTIVE: To provide the first comprehensive analysis of the Twitter-verse amongst academic urologists and programs in North America. METHODS: Using national accreditation and individual program websites, all active urology residency programs (USA & Canada) and academic Urology faculty at these programs were identified. Demographic data for each program (AUA section, resident class size) and physician (title, fellowship training, Scopus H-index and citations) were documented. Twitter metrics (Twitter handle, date joined, # tweets, # followers, # following, likes) for programs and physicians were catalogued (data capture: March-April 2019). Descriptive analyses and temporal trends in Twitter utilization amongst programs and physician were assessed. Multivariable (MV) logistic regression was used to identify predictors of Twitter use. RESULTS: 156 academic programs (143 USA, 13 Canada) and 2214 academic faculty (2015 USA, 199 Canada) were identified. Twitter utilization is currently 49.3% and 34.1% amongst programs and physicians, respectively, and continues to increase. On MV analysis, programs with 3-5 residents/year and programs with a higher percentage of faculty Twitter engagement were more likely to have Twitter accounts. From a physician perspective, those with fellowship training, lower academic rank (clinical instructor, assistant professor, associate professor vs. professor) and higher H-indices were more likely to have individual Twitter accounts. CONCLUSION: There is a steady increase in Twitter engagement amongst Urology programs and academic physicians. Faculty Twitter utilization is an important driver of program Twitter engagement. Twitter SoMe activity is strongly associated with academic productivity, and may in fact drive academic metrics. Within Urology, SoMe presence appears to be proportional to academic activity

High-Dose-Rate Brachytherapy for the Treatment of Basal and Squamous Cell Carcinomas on Sensitive Areas of the Face: A Report of Clinical Outcomes and Acute and Subacute Toxicities

Purpose Basal cell and cutaneous squamous cell carcinoma are common malignancies (keratinocyte carcinomas [KCs]). Surgical resection is the standard of care. Radiation using high-dose rate brachytherapy (HDR-BT) may serve as a superior alternative where surgical scars may be of cosmetic concern or in elderly patients with significant comorbidity. We aim to describe the clinical and cosmetic outcomes as well as posttreatment radiation toxicities associated with HDR-BT in patients who were treated for KCs of the face. Methods and Materials Patients with KCs treated with HDR-BT from 2015 to 2018 were included in the study. Patient medical records and clinical photos were reviewed at multiple time points: start of treatment, end of treatment, short-term (2 week) follow-up, 3-month follow-up, and if needed at 6 months. Radiation toxicity was graded using the Radiation Therapy Oncology Grading (RTOG) acute toxicity scale. Median (range) toxicity grades at follow-up intervals were calculated. Clinical outcomes including local recurrence were evaluated for all patients. Results The study included 19 patients and 20 KCs. The median radiation dose was 42 Gy (39-42 Gy) over 6 fractions. The median toxicity at completion of treatment was RTOG grade 2 (85% of patients). At short-term follow-up, 50% of patients (n = 10) improved to RTOG grade 1 (0-2). At 3 months, 70% of patients (n = 14) had RTOG grade 0, and by 6 months, 100% of patients (n = 18) had RTOG grade 0. No RTOG grade 3 or higher skin toxicity was observed. With a median follow-up of 7.2 months (range, 1.3-54.4 months), the local recurrence-free survival was 95%. Conclusions We demonstrate that HDR-BT can be used as definitive treatment of KCs of the face with excellent cosmetic outcomes and local control. Acute and subacute skin toxicities were most commonly RTOG grade 2 or less with resolution of patient’s skin toxicity by 3 months

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ‘‘modality-agnostic training’’ technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing