Radiation Oncology Virtual Education Rotation (ROVER) 2.0 for Residents: Implementation and Outcomes.

The COVID-19 pandemic catalyzed the integration of a virtual education curriculum to support radiation oncologists in training. We report outcomes from Radiation Oncology Virtual Education Rotation (ROVER) 2.0, a supplementary virtual educational curriculum created for radiation oncology residents globally. A prospective cohort of residents completed surveys before and after the live virtual webinar sessions (pre- and post-surveys, respectively). Live sessions were structured as complex gray-zone cases across various core disease sites. Resident demographics and responses were summarized using means, standard deviations, and proportions. Nine ROVER sessions were held from October 2020 to June 2021. A total of 1487 registered residents completed the pre-survey, of which 786 attended the live case discussion and 223 completed post-surveys. A total of 479 unique radiation oncology residents (of which 95, n = 19.8%, were international attendees) from 147 institutions (national, n = 81, 55.1%; international, n = 66, 44.9%) participated in the sessions. There was similar participation across post-graduate year (PGY) 2 through 5 (range n = 86 to n = 105). Of the 122 unique resident post-surveys, nearly all reported learning through the virtual structure as "very easy" or "easy" (97.5%, n = 119). A majority rated the ROVER 2.0 educational sessions to be "valuable or "very valuable" (99.2%, n = 121), and the panelists-attendee interaction as "appropriate" (97.5%, n = 119). Virtual live didactics aimed at radiation oncology residents are feasible. These results suggest that the adoption of the ROVER 2.0 curricula may help improve radiation oncology resident education.

Genetic and population diversity of Toxocara cati (Schrank, 1788) Brumpt, 1927, on the basis of the internal transcribed spacer (ITS) region.

The present investigation was aimed to study the sequence, phylogenetic and haplotype analyses of Toxocara cati based on the ITS region, along with the genetic diversity, demographic history and population-genetic structure. The maximum likelihood tree based on Kimura 2-parameter model was constructed using the complete ITS region of all the nucleotide sequences (n = 57) of Toxocara spp. and other related ascarid worms available in the GenBank™. It placed all the sequences of T. cati into four major clades designated as T. cati genotypes 1-4 (TcG1-G4). A total of 66 signature nucleotides were identified in the ITS region between genotypes. The median-joining haplotype network displayed a total of 24 haplotypes, with China exhibiting the highest number of haplotypes (h = 20) followed by India (h = 4), and Japan and Russia (h = 1). It indicated a clear distinction between all the four genotypes. The pairwise FST values between all the genotypes indicated huge genetic differentiation (> 0.25) between different T. cati genotypes. Moreover, the gene flow (Nm) between T. cati genotypes was very low. Results of AMOVA revealed higher genetic variation between genotypes (92.82%) as compared to the variation within genotypes (7.18%). The neutrality indices and mismatch distributions for the G1-G4 genotypes, Indian isolates and the overall dataset of T. cati indicated either a constant population size or a slight population increase. The geographical distribution of all the genotypes of T. cati is also reported. This is the first report of genotyping of T. cati on the basis of the ITS region.

Real-time analysis and display of quantitative measures to track and improve clinical workflow.

PURPOSE: Radiotherapy treatment planning is a complex process with multiple, dependent steps involving an interdisciplinary patient care team. Effective communication and real-time tracking of resources and care path activities are key for clinical efficiency and patient safety. MATERIALS AND METHODS: We designed and implemented a secure, interactive web-based dashboard for patient care path, clinical workflow, and resource utilization management. The dashboard enables visualization of resource utilization and tracks progress in a patient's care path from the time of acquisition of the planning CT to the time of treatment in real-time. It integrates with the departmental electronic medical records (EMR) system without the creation and maintenance of a separate database or duplication of work by clinical staff. Performance measures of workflow were calculated. RESULTS: The dashboard implements a standardized clinical workflow and dynamically consolidates real-time information queried from multiple tables in the EMR database over the following views: (1) CT Sims summarizes patient appointment information on the CT simulator and patient load; (2) Linac Sims summarizes patient appointment times, setup history, and notes, and patient load; (3) Task Status lists the clinical tasks associated with a treatment plan, their due date, status and ownership, and patient appointment details; (4) Documents provides the status of all documents in the patients' charts; and (5) Diagnoses and Interventions summarizes prescription information, imaging instructions and whether the plan was approved for treatment. Real-time assessment and quantification of progress and delays in a patient's treatment start were achieved. CONCLUSIONS: This study indicates it is feasible to develop and implement a dashboard, tailored to the needs of an interdisciplinary team, which derives and integrates information from the EMR database for real-time analysis and display of resource utilization and clinical workflow in radiation oncology. The framework developed facilitates informed, data-driven decisions on clinical workflow management as we seek to optimize clinical efficiency and patient safety.

Daily Quality Assurance Efficiency Evaluation Using SunCHECK Machine and Machine Performance Check.

Purpose To investigate time efficiency, applicability, and accuracy of using a web-based, independent quality assurance (QA) platform and vendor-dependent based system check for daily linear accelerator (LINAC) QA. Methods  Time needed to perform daily QA on a single (n=1) LINAC was collected for three months. Task Group report 142 (TG-142) compliant daily QA included dosimetry checks (four photon, four electron beams); imaging checks (planar kilovolt (kV) & megavolt (MV), kV cone-beam computed tomography (CBCT)); and mechanical and safety checks using SunCHECK Machine (SCM) (Sun Nuclear Inc., Melbourne, FL, USA). Additionally, Machine Performance Check (MPC) (Varian Medical Systems, Inc., Palo Alto, CA, USA) was performed for all energies. Four trained radiation therapists performed daily QA on both platforms. Data were collected to identify the time required to complete both SCM and MPC. Additionally, the two platforms were evaluated on usability and features. Output results were compared to our monthly standard to assess accuracy. Results On average, SCM took 22 minutes with a standard deviation of six minutes and MPC took 15 minutes with a standard deviation of three minutes. MPC output results were impacted due to the beam output being coupled to the beam profile changes. As a result, the two systems on average disagreed by -1.41% after three months despite being baselined at the same time point and output agreeing well initially (average difference of -0.1% across all energies). While there was overlap in the tests performed, SCM tests were more relevant to TG-142 while MPC tests were beneficial to machine service and, with a clear understanding of the limitations of the system, found suitable as a secondary backup to SCM for daily output verification. Conclusions  This work demonstrates that a comprehensive TG-142 daily QA can be designed using SCM and MPC can be added as a beneficial tool and backup for output verification while still maintaining an efficient daily QA process.

Clinical capability of modern brain tumor segmentation models.

PURPOSE: State-of-the-art automated segmentation methods achieve exceptionally high performance on the Brain Tumor Segmentation (BraTS) challenge, a dataset of uniformly processed and standardized magnetic resonance generated images (MRIs) of gliomas. However, a reasonable concern is that these models may not fare well on clinical MRIs that do not belong to the specially curated BraTS dataset. Research using the previous generation of deep learning models indicates significant performance loss on cross-institutional predictions. Here, we evaluate the cross-institutional applicability and generalzsability of state-of-the-art deep learning models on new clinical data. METHODS: We train a state-of-the-art 3D U-Net model on the conventional BraTS dataset comprising low- and high-grade gliomas. We then evaluate the performance of this model for automatic tumor segmentation of brain tumors on in-house clinical data. This dataset contains MRIs of different tumor types, resolutions, and standardization than those found in the BraTS dataset. Ground truth segmentations to validate the automated segmentation for in-house clinical data were obtained from expert radiation oncologists. RESULTS: We report average Dice scores of 0.764, 0.648, and 0.61 for the whole tumor, tumor core, and enhancing tumor, respectively, in the clinical MRIs. These means are higher than numbers reported previously on same institution and cross-institution datasets of different origin using different methods. There is no statistically significant difference when comparing the dice scores to the inter-annotation variability between two expert clinical radiation oncologists. Although performance on the clinical data is lower than on the BraTS data, these numbers indicate that models trained on the BraTS dataset have impressive segmentation performance on previously unseen images obtained at a separate clinical institution. These images differ in the imaging resolutions, standardization pipelines, and tumor types from the BraTS data. CONCLUSIONS: State-of-the-art deep learning models demonstrate promising performance on cross-institutional predictions. They considerably improve on previous models and can transfer knowledge to new types of brain tumors without additional modeling.

Comparison of Tumor Bed Delineation Using a Novel Radiopaque Filament Marker Versus Surgical Clips for Targeting Breast Cancer Radiotherapy.

BACKGROUND: Accuracy of tumor bed (TB) delineation is essential for targeting boost doses or partial breast irradiation. Multiple studies have shown high interobserver variability with standardly used surgical clip markers (CMs). We hypothesize that a radiopaque filament marker (FM) woven along the TB will improve TB delineation consistency. METHODS: An FDA-approved FM was intraoperatively used to outline the TB of patients undergoing lumpectomy. Between January 2020 and January 2022, consecutive patients with FM placed after either (1) lumpectomy or (2) lumpectomy with oncoplastic reconstruction were identified and compared with those with CM. Six "experts" (radiation oncologists specializing in breast cancer) across 2 institutions independently defined all TBs. Three metrics (volume variance, dice coefficient, and center of mass [COM] deviation). Two-tailed paired samples t tests were performed to compare FM and CM cohorts. RESULTS: Twenty-eight total patients were evaluated (14 FM and 14 CM). In aggregate, differences in volume between expert contours were 29.7% (SD ± 58.8%) with FM and 55.4% (SD ± 105.9%) with CM ( P < 0.001). The average dice coefficient in patients with FM was 0.54 (SD ± 0.15), and with CM was 0.44 (SD ± 0.22) ( P < 0.001). The average COM deviation was 0.63 cm (SD ± 0.53 cm) for FM and 1.05 cm (SD ± 0.93 cm) for CM; ( P < 0.001). In the subset of patients who underwent lumpectomy with oncoplastic reconstruction, the difference in average volume was 21.8% (SD ± 20.4%) with FM and 52.2% (SD ± 64.5%) with CM ( P <0.001). The average dice coefficient was 0.53 (SD ± 0.12) for FM versus 0.39 (SD ± 0.24) for CM ( P < 0.001). The average COM difference was 0.53 cm (SD ± 0.29 cm) with FM versus 1.25 cm (SD ± 1.08 cm) with CM ( P < 0.001). CONCLUSION: FM consistently outperformed CM in the setting of both standard lumpectomy and complex oncoplastic reconstruction. These data suggest the superiority of FM in TB delineation.

Tailored Mentorship for the Underrepresented and Allies in Radiation Oncology: The Association of Residents in Radiation Oncology Equity and Inclusion Subcommittee Mentorship Experience.

PURPOSE: There are limited opportunities for mentorship for underrepresented in medicine (URM) trainees and physicians in radiation oncology (RO). The purpose of this study was to create and evaluate a formal mentorship program open to URMs and allies with interests in diversity, equity, and inclusion. METHODS AND MATERIALS: A mentorship program incorporating a virtual platform was designed by the Association of Residents in Radiation Oncology Equity and Inclusion Subcommittee. It was structured to include 6 sessions over 6 months with matched mentor-mentee pairs based on responses to a publicized online interest form. A compilation of evidence-based guidelines was provided to optimize the mentorship relationship. Linked pre- and postprogram surveys were administered to collect demographic data, define baseline goals and level of support, and evaluate program satisfaction. RESULTS: Thirty-five mentor-mentee pairs were matched; 31 mentees completed the preprogram survey and 17 completed the postprogram survey. Preprogram, only 3 mentees (9.7%) reported satisfaction with current mentorship and 5 (16%) reported mechanisms or mentorship in place at their program to support URMs. On the postprogram survey, mentees reported high satisfaction with areas of mentorship, mentor attributes, and the program overall. Opportunities for improvement include implementation of mechanisms to enhance communication with mentor-mentee pairs and maintain longitudinal engagement. CONCLUSIONS: In the first tailored mentorship program in RO for URMs and those with diversity, equity, and inclusion interests, our results demonstrate that there is self-reported interest for better mentorship for URMs in RO, and that a nationwide structured mentorship program can address participants' goals with high satisfaction. Program expansion could provide URMs and allies in RO more opportunities for career development and promote a greater sense of community and inclusion within the field.

American Society of Clinical Oncology 2021 Annual Meeting Highlights for Radiation Oncologists.

The annual meeting of the American Society of Clinical Oncology is the largest multidisciplinary oncology-focused conference in the world. With more than 4900 total abstracts in 2021 alone, it is difficult for individuals to evaluate all the results. This article presents a review of 32 selected abstracts across all disease sites, focusing on those of greatest relevance to radiation oncologists.

Consolidative Thoracic Radiation Therapy After First-Line Chemotherapy and Immunotherapy in Extensive-Stage Small Cell Lung Cancer: A Multi-Institutional Case Series.

Purpose: Survival for patients with extensive-stage small cell lung cancer (ES-SCLC) remains poor. Consolidative thoracic radiation therapy (cTRT) and upfront immunotherapy with chemotherapy have each incrementally improved patient outcomes, but have not yet been combined in clinical trials. We sought to characterize outcomes and toxicities after first-line chemotherapy and immunotherapy followed by cTRT. Methods and Materials: Patients with ES-SCLC who were treated with first-line chemotherapy and immunotherapy followed by cTRT were identified at 2 institutions. Patient outcomes including overall survival (OS), progression-free survival, local progression-free survival, distant progression free-survival, and toxicity were assessed. Results: Twenty patients were included in our data set treated from 2018 to 2021 with a median follow-up of 12 months. Median OS in this cohort was 16 months with 6-month OS of 94.7% and 12-month OS of 77.5% (comparable to historical controls). There were also low rates of toxicity, including 0% grade 3+ toxicity, 0% grade 2 pneumonitis, and 5% grade 2 esophagitis. Conclusions: Treatment of ES-SCLC with first-line chemoimmunotherapy followed by cTRT appears to be safe and to have outcomes comparable to published modern clinical trials. Further studies are warranted to determine the therapeutic effect of cTRT after chemoimmunotherapy.