Lozko, Yuliia

PURPOSE Cancer recurrence is a critical outcome for patients and physicians. Retrospective cancer recurrence data can evaluate recurrence-directed treatment and generate novel interventions targeting recurrent cancers. However, large cancer databases do not provide recurrence-related information, stymying study at scale, and consequently require significant manual record review. Automated evaluation of records may allow for the rapid generation of easily analyzed data sets, accelerating the evaluation of recurrence altogether. METHODS Patients treated with radiation therapy at one tertiary referral center from 2010 to 2018 with a verified cancer status (cancer recurrence v no cancer recurrence) were identified. Patients with recurrent disease were initially identified through manual record review, and the associated pathology report was collected. Google Automated Machine Learning with Natural Language Processing (AutoNLP) and Google Gemini 1.5 Pro were used to generate a model for binary classification, with comparison to the gold-standard manually developed data set. RESULTS A total of 7,054 patients were identified. 3,431 (48.6%) were female, with a median age of 64 years. Head and neck (1,482, 21%), breast (1,480, 21%), upper GI (1,307, 18.5%), and lung/thorax (1,107, 15.7%) were the most common disease sites. Recurrence was verified for 1,546 patients (21.9%) using pathology reports, of which 1,249 positive cases were paired with 651 negative pathology reports for model development. Google Gemini 1.5 Pro consistently outperformed AutoNLP across all measurements of accuracy, generating a greater absolute difference in precision, recall, negative predictive value, and specificity, and a higher likelihood of correct classification at the individual level, rendering Gemini superior in recurrence status extraction. CONCLUSION AutoNLP and Google Gemini 1.5 Pro are promising tools for identifying recurrence from pathology reports, with the latter demonstrating superior overall performance, making it particularly suitable for clinical translation.

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.