Kowalchuk, Roman

Objective This study evaluates the impact of international observerships organized by Help Ukraine Group (HUG) on professional development, knowledge transfer, and clinical practice improvement in radiation oncology in Ukraine. Methods A total of 50 observerships were facilitated for Ukrainian medical professionals at 14 institutions across the USA, Canada, Europe, and Australia from 2022 to 2024, including 31 radiation oncologists and medical physicists. A survey assessed their impact on oncology care, focusing on knowledge gained, lessons learned, and challenges in implementing new techniques. The primary outcome was the success score, defined as a composite score of implementing new procedures, initiating improvement projects, and knowledge dissemination efforts. Descriptive and comparative analyses examined satisfaction and success outcomes. Results A total of 43 respondents participated in the survey (response rate 86.0%). Twenty-eight of the responders were radiation oncology professionals (67.8% radiation oncologists, 32.1% medical physicists). The median observership duration for radiation oncology professionals was four weeks, with 96.4% also attending a professional conference. Overall satisfaction was high (mean 9.5 out of 10, median 10.0, IQR 9.0-10.0) while the success score was substantial (mean 6.1 out of 10, median 7.0, IQR 5.0-8.0). Time since certification influenced success, with ≥10 years’ experience associated with higher success scores (7.4 vs. 4.7, p=0.01). Importantly, 100% of respondents learned new procedures, 89.3% reported shifts in their clinical perspective, and 60.7% successfully implemented new techniques. Subsequent knowledge dissemination in Ukraine was substantial: 82.1% provided informal training, 60.7% delivered institutional or national presentations, 39.3% incorporated materials into lectures, and 10.7% engaged in all activities. Continued mentorship with host institutions was reported by 82.1%. Conclusions Amid the war, international observerships advanced clinical practice, education, and procedures in Ukraine, demonstrating the resilience of its medical community and providing a model that other LMICs/UMICs can follow.

Abstract INTRODUCTION Glioblastomas (GBMs) are highly aggressive tumors. A common clinical challenge after standard of care treatment is differentiating tumor progression from treatment-related changes, also known as pseudoprogression (PsP). Usually, PsP resolves or stabilizes without further treatment or a course of steroids, whereas true progression (TP) requires more aggressive management. Differentiating PsP from TP will affect the patient's outcome. This study investigated using deep learning to distinguish PsP MRI features from progressive disease. METHOD We included newly diagnosed GBM patients who met the inclusion criteria, including a new or increasing enhancing lesion size within the original radiation field and who had clinical, medication, and any histopathology available. The interpretation of the MR images at the image inclusion time point had to be indeterminate. We labeled those who subsequently were stable or improved on imaging and clinically as PSP and those with clinical and imaging deterioration as TP. A subset of subjects underwent a second resection. We labeled these subjects as PSP or TP based on the histological diagnosis. We performed skull stripping, coregistered contrast-enhanced T1 MRIs with T2-weighted images for each patient and used them as input to a 3-D Densenet121 model. We used several augmentation techniques and five-fold cross-validation to achieve more robust predictions. RESULT We included 124 patients who met the criteria, and of those, 63 were PsP and 61 were TP. We trained a deep learning model that achieved 76.4% (ranged 70%-84%, SD 5.122) mean accuracy over the 5 folds, 0.7560(ranged 0.6553-0.8535, SD 0.069) mean AUROCC, 88.72% (SD 6.86) mean sensitivity, and 62.05%(SD 9.11) mean specificity. CONCLUSION We report the development of a deep learning model that distinguishes PsP from TP in GBM patients treated per the Stupp protocol. Further refinement and external validation are required prior to widespread adoption in clinical practice.

Abstract PURPOSE Though patients with glioblastoma generally have poor overall survival (OS), OS estimates range significantly. We sought to develop a radiomics-based model predictive of OS in glioblastoma using preoperative MRI scans to identify these differences and guide clinical care. METHODS The primary endpoint was OS, determined from the date of surgery to death or last follow-up. T1, FLAIR, T2, and post-contrast T1 (T1c) MRI sequences were obtained for all patients. Using HD-GLIO-AUTO, lesion segmentation yielded three masks corresponding to the regions of contrast enhancement, non-enhancement, and total volume. Radiomics features were extracted from each region from the T2 and T1c sequences independently (i.e. either from T2 or T1c) and in combination. Age at surgery and gender were also considered. The data was split into training (75%) and test (25%) sets, stratifying for age at surgery. Kaplan-Meier analysis and the Coxnet model were used. For Coxnet, Elastic Net was used as the penalty, with optimization of the L1 coefficient using 5-fold cross-validation and evaluation via the concordance index (C-index). RESULTS From 3 institutions, 383 patients were included. Median OS was 395 days: 346 patients died, and 37 were right-censored. Age > 65 years was predictive of decreased OS (p < 0.005), but gender was not (p=0.16). For the Coxnet model, the C-index was 0.67 (standard deviation: 0.01) considering T2 and T1c sequences for the total lesion volume, with a C-index of 0.65 for the test set. For the enhancing volume, the model had a C-index of 0.71 for the test set using T1c sequences. Overall, C-indices ranged from 0.62-0.71. CONCLUSIONS We present a novel radiomics-based model predictive of OS in glioblastoma across a multi-institutional dataset. Ongoing efforts will incorporate immediate postoperative imaging and IDH and MGMT status into the proposed model to further improve prognostic ability and help guide clinical decision-making.