Lung Involvement in Pediatric Hodgkin Lymphoma: Imaging challenges in adequate staging
Bibliographic record
Abstract
Contemporary treatment protocols for children and adolescents with Hodgkin lymphoma (HL) aim not only to sustain high cure rates but also to minimize long-term morbidity associated with chemotherapy and/or radiation therapy (Mauz-Körholz et al. , 2022, 2023).This delicate balance between efficacy and toxicity places a premium on precise risk stratification, which is fundamentally dependent on imaging for accurate staging prior to therapy. Accurate staging definitions for both nodal and extranodal involvement in pediatric HL are critical for risk-adapted therapy but present significant challenges (Humphries, 2025; Stoevesandt et al. , 2025). Distinguishing disseminated (Ann-Arbor Stage IV disease) from contiguous (E-lesion) lung involvement (Zijtregtop et al. , 2023) by imaging profoundly impacts the stage determination and consequent treatment intensity, with potential for false upstaging and overtreatment. Radiologists face inherent challenges distinguishing pulmonary HL lesions from concurrent benign conditions. In contrast to adults, biopsy needs to be critically discussed in children due to ethical concerns and limited diagnostic yield (Kallenberg et al. , 2009). Divergent staging criteria based on number, size and metabolic activity exist across major cooperative study groups (Flerlage et al. , 2017; Stoevesandt et al. , 2025) leading to inconsistent interpretation of imaging findings (Seelisch et al. , 2023), which impacts the comparability of outcome data. These challenges are compounded by limited pediatric-specific data on lung involvement in HL, the result of technical advances in imaging and evolution of staging definitions occurring over time. Historically, imaging evolved from X-ray to CT and PET/CT, improving the detection of lung involvement. While PET/MRI offers a radiation-free alternative to PET/CT, its utility for accurate lung parenchyma evaluation remains limited (Kwee et al. , 2014; Albano et al. , 2021). The SEARCH for CAYAHL initiative aims to highlight diagnostic complexities, delineate morphological distinctions between E-lesions and disseminated lung involvement, and explore their prognostic implications with the future collaborative goal of harmonizing international staging criteria for lung involvement in pediatric HL to insure optimized, individualized patient care [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 9 ] [ 10 ]. Publication History Article published online: 02 December 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".