Évaluation en TEP 18F-FDG de la réponse au traitement par CAR T-Cells et anticorps bispécifiques des lymphomes non hodgkiniens agressifs
Bibliographic record
Abstract
Les lymphomes non hodgkiniens agressifs réfractaires ou en rechute ont vu leur pronostic amélioré ces dernières années par les thérapies innovantes que sont les CAR T-cells et les anticorps bispécifiques. Dans le cadre de ces stratégies thérapeutiques, l’évaluation de la réponse tumorale par TEP FDG reste primordiale. Néanmoins, en raison de leur mode d’action, ces traitements peuvent résulter en des réponses retardées ou en des phénomènes inflammatoires qui peuvent gêner l’interprétation de l’imagerie métabolique. Les critères de Lugano basés en particulier sur le score de Deauville restent applicables, mais il est nécessaire de connaître les pièges d’interprétation et de prendre en compte l’ensemble du contexte clinicobiologique du patient lors de l’évaluation, idéalement dans un contexte pluridisciplinaire. Refractory or relapsing aggressive non-Hodgkin lymphomas have improved prognosis in the last few years thanks to innovative therapies such as CAR T-cells and bispecific antibodies. During these therapeutic strategies, tumoral response assessment with FDG PET remains of paramount importance. However, due to their mode of action, these treatments can result in delayed responses or in inflammatory events that may hamper interpretation of metabolic imaging. Lugano criteria, relying on the Deauville score, are useful, but it is crucial to be aware of the interpretation pitfalls and to consider all the clinical and biological background of the patient, ideally in a multidisciplinary approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".