Imaging Pitfalls in Pediatric, Adolescent, and Young Adult Hodgkin Lymphoma: A SEARCH for CAYAHL Initiative to Bridge Multidisciplinary Patient Care
Bibliographic record
Abstract
Introduction Hodgkin lymphoma (HL) is a highly curable cancer in children, adolescents, and young adults. Treatment strategies prioritize minimizing long-term toxicity while maintaining high survival rates. Clinical trials often include centralized imaging review to accurately determine the stage of the disease as it significantly impacts treatment decisions. Accurate and reliable imaging reports are required. Discrepancies sometimes arise between academic guidelines and real-life imaging scenarios leading to uncertainties in image interpretation. Methods The Staging, Evaluation, and Response Criteria Harmonization for Childhood, Adolescent, and Young Adult Hodgkin Lymphoma (SEARCH for CAYAHL) initiative, launched in 2011, aims to standardize imaging criteria for HL among cooperative study groups, resulting in more comparable data across trials. With representation from the Children's Oncology Group, the European Network for Pediatric Hodgkin Lymphoma and the Pediatric Hodgkin Consortium, a working group – that included specialists in diagnostic radiology, nuclear medicine, radiation oncology, and pediatric oncology – identified recurrent imaging pitfalls in HL that may lead to incorrect staging. This project is intended to assist imaging professionals and clinicians interested in HL to improve interdisciplinary cancer care for this patient population. Results Image reporting may be influenced by both, errors in image acquisition, and the misinterpretation of imaging findings. The collection of disease-specific pitfalls in this project provides clinical scenarios dedicated to both topics illustrated through typical PET, CT, and MRI findings in illustrative case vignettes. In case of uncertainty, possible strategies for identifying differentials are also provided. Given the growing importance of PET imaging in HL, this project also discusses the limitations of 18 FDG, thereby emphasizing the importance of integrating the metabolic and morphological components of hybrid imaging. Conclusion This project provides a practical complement to existing scientific literature that addresses recurring pitfalls that may lead to diagnostic uncertainty and their consequences. By promoting interdisciplinary dialogue, the project aims to improve interdisciplinary decision-making in the real world and ultimately enhance outcomes for patients with HL. 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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".