Enhancing Radiotherapy Quality Assurance in Lymphoma: A Rigorous Real-Time Central Review Process in AHOD2131
Bibliographic record
Abstract
Introduction Radiation therapy quality assurance (RTQA) is essential in clinical trials to ensure protocol-compliant treatment. As lymphoma radiotherapy (RT) has evolved to use smaller volumes and more conformal techniques, treatment planning has become increasingly complex, requiring a rigorous RTQA process. We report early experiences with centralized RT review in the NCTN AHOD2131 trial (NCT05675410) for frontline treatment of early-stage classic Hodgkin lymphoma. Methods Our central review process for AHOD2131 incorporates a structured, multi-step process designed to ensure accurate target volume delineation, appropriate dose coverage, and adherence to organ-at-risk (OAR) constraints. The process involves institutional submission of simulation CT, PET/CTs, and contours to the Imaging and Radiation Oncology Core Group Rhode Island (IROC-RI). Nuclear medicine radiologists perform an initial review of PET/CTs, identifying sites of disease involvement and assigning Deauville scores. The RTQA team of IROC-RI staff and radiation oncologists (ROs) promptly conducts a virtual review. This review process ensures that PET/CTs are accurately registered with simulation CT, target volumes are appropriately delineated, and OAR doses comply with protocol-defined constraints. Feedback is provided to treating ROs, who must address any required revisions before final approval and treatment initiation. Results Early implementation of this rigorous RTQA process has been successful in identifying potential protocol deviations and providing timely feedback within 1-2 days. Common errors include suboptimal PET/CT fusion leading to inaccurate contouring, omission of sites of initial disease involvement from ISRT volumes, and contouring that unnecessarily includes uninvolved OARs. Errors related to PET/CT fusion are often related to differences in body positioning between PET/CT and simulation CT scans, necessitating multiple registrations or “mental fusion” for accurate delineation. Additionally, misinterpretation of pre-treatment imaging contributes to target volume errors, underscoring the importance of careful review of baseline imaging. Conclusion A standardized, centralized RTQA process is critical in modern lymphoma trials. The AHOD2131 RTQA model, integrating nuclear medicine and radiation oncology expertise, ensures high-quality RT delivery and provides a framework for future trials. Publication History Article published online: 02 December 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.519 | 0.462 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".