et al. Value - based categorisation system for radiotherapy innovation. Radiother Oncol. 2025;213:111167. 4) OpenAI. API Reference. Accessed 7 Nov 2025. Keywords: health policy, innovation appraisal, MCDA
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Interdisciplinary - Health economics & health services research
ESTRO 2026
spanning clinical, operational, or technical treatment impact were predefined through a Delphi process, involving the European RO professional community through ESTRO. Using OpenAI GPT - 5 via R, full - text articles were screened, and each extracted endpoint was labelled on a three point scale according to the outcome observed across all published studies for that innovation: -1 (harm/worsening), 0 (neutral), +1 (improvement). The modified TOPSIS uses two sequential weighting phases. First, endpoint weights derive directly from the Delphi consensus, reflecting importance as determined by the RO community. Second, composite scores are re - weighted by the strength and recency of supporting evidence using a structured ranking of study designs, with weights determined in the Delphi consensus study and a publication - year factor, giving greater influence to robust and up - to - date findings. Closeness coefficients are computed against absolute reference anchors (1 for best and 0 for worst values). Sensitivity analyses varied endpoint weights, evidence multipliers, and handling of uncertainty - flagged items. Results: Mean (SD) closeness coefficients were 0.063 (0.081) for hypofractionation, 0.049 (0.078) for high - dose - rate brachytherapy, and 0.045 (0.070) for proton radiotherapy. Anchoring enabled direct comparison on a standardised 0-1 scale. Interpreted as generalised advantage indices, hypofractionation showed the greatest proximity to the ideal profile. The ordering remained stable across sensitivity analyses. Conclusion: A two - step weighted TOPSIS that combines Delphi - derived endpoint importance with artificial intelligence (AI) - assisted evidence extraction and grading offers a transparent, objective, and reproducible tool to appraise radiotherapy innovations. Anchored coefficients improve interpretability for clinicians, policy-makers, and patient representatives. The approach is readily extensible beyond the prostate cancer pilot to broader innovation portfolios and program - level decision- making. Further development and validation of the TOPSIS will support a structured appraisal framework aiming to identify high-value radiotherapy innovations, informing healthcare providers and policymakers who can support their clinical implementation or reimbursement. References: 1) Shih HS, Olson DL. TOPSIS and its Extensions: A Distance - Based MCDM Approach. Springer; 2022. 2) Vandemaele M, Lewison G, Martinussen H, Borràs JM, Leech M, Aznar M, et al. Outcomes and level of evidence in radiation therapy research and categories of innovations: an ESTRO - VBRO bibliometrics analysis. Radiother Oncol. 2025;213:111165. 3) Vandemaele M, Blanchard P, Borràs JM, Leech M, Aznar M, Aggarwal A,
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Identifying Toxicity and Inequity in Toxicity Management Access in Prostate Cancer from Population-level Electronic Healthcare Records. Sarah Elliot 1,2 , Zhuolin Yang 2,3 , Archie Macnair 3,4 , David J Noble 3,4 , Ewen M Harrison 1 , George Beckett 5 , Duncan B McLaren 3,4 , William H Nailon 2,3 1 Center for Medical Informatics, University of Edinburgh, Edinburgh, United Kingdom. 2 Department of Oncology Physics, Western General Hospital, Edinburgh, United Kingdom. 3 Edinburgh Cancer Research, CRUK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom. 4 Department of Clinical Oncology, Western General Hospital, Edinburgh, United Kingdom. 5 Edinburgh Parallel Computing Centre, University of Edinburgh, Edinburgh, United Kingdom Purpose/Objective: Population-level, Electronic Healthcare Records (EHR) hold many insights about radiotherapy outcomes and have the potential to be used in place of clinical trials where CTCAE/RTOG report toxicity. Gastrointestinal (GI) and genitourinary (GU) toxicity are common complications after prostate cancer radiotherapy. Hospital admissions (indicated with ICD-10 codes) capture severe events, but many toxicities are managed symptomatically in the community with medications. The aim of this study was to investigate, on a population level, the use of EHR medications to identify GI and GU toxicities and to compare medications as a proxy for access to primary care across different socio-economic groups. Material/Methods: We curated and analysed a population-level cohort of prostate cancer patients receiving radiotherapy (PROSECCA study: improving radiotherapy in PROState cancer using EleCtronic population-based healthCAre data) (n = 13,106). The outcome of interest was prescription of medication treating GI/GU toxicity within 2 years of radiotherapy. A random forest model predicted toxicity based on clinical history, comorbidities, cancer characteristics, and demographics. Observed and predicted toxicity rates were stratified by Scottish Index for Multiple Deprivation (SIMD) quintiles, and observed-to- expected (O/E) ratios calculated determine equity in treatment access. Hospital admissions for toxicity within 2 years of radiotherapy acted as a reference
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