Test characteristics of end-of-treatment PET scans in diffuse large b-cell lymphoma in a population-level cohort
Notice bibliographique
Résumé
Abstract Introduction: First-line treatment of diffuse large B-cell lymphoma (DLBCL) consists of chemoimmunotherapy followed by end-of-treatment (EOT) positron-emission tomography (PET) scan to assess response. However, EOT PET can have false-positive results, thus, positive results must be followed by further testing to clarify response status, either by tissue biopsy or repeat PET. Guidelines suggest waiting preferably at least 6 weeks from last chemotherapy to perform PET, to avoid false positives from post-treatment inflammation. However, prior studies examining the false positive rate of EOT PET have small sample sizes and have not examined the impact of timing of the EOT PET. We conducted an exploratory analysis of EOT PET test characteristics using population-level data and evaluated the impact of time to EOT PET. Methods: We conducted a retrospective cohort study using population-level administrative health data of all patients in Ontario, Canada, ≥18 years old with DLBCL, who received frontline rituximab-based chemoimmunotherapy followed by PET within 16 weeks of last chemotherapy from October 2009-May 2021. We evaluated the time from last chemoimmunotherapy to EOT PET, with short time to PET defined as ≤6 weeks, and long time to PET >6 weeks. The presence of follow-up testing, defined as either repeat PET or tissue biopsy within 6 months of EOT PET was used as a surrogate for a positive EOT PET. True positive was defined as the presence of follow-up testing, as well as either cancer relapse or cancer-specific death within 2 years of the EOT PET. False positive was follow-up testing in the absence of relapse or cancer-specific death within 2 years. True negative was defined as no follow-up testing and no relapse or cancer-specific death within 2 years. False negative was defined as no follow-up testing, and relapse or cancer-specific death within 2 years of the EOT PET. These definitions were used to calculate the positive and negative predictive values, sensitivity and specificity of EOT PET both for the entire cohort, and according to time to EOT PET. Results: A total of 3833 individuals with DLBCL had EOT PET scan following first-line treatment; 2247 (59%) had short time to PET (≤6 weeks), 1586 (41%) had long time to PET (>6 weeks). For the overall cohort, 715 (19%) patients had a false positive EOT PET, 428 (11%) had true positive, 358 (9%) had false negative, and 2332 (61%) had true negative EOT PET. In the group with short time to EOT PET (≤6 weeks), there were 457 (20%) false positives, and 185 (8%) false negatives. In the group with long time to EOT PET (>6 weeks), there were 258 (16%) false positives, and 173 (11%) false negatives. The positive predictive value (PPV) was 37% for the overall cohort, 37% for the short time to EOT PET group, and 39% in the long time to EOT PET group. The negative predictive value (NPV) was 87% for the whole cohort, 88% in the short time to EOT PET group, and 85% in the long time to EOT PET group. Specificity was 76% for the whole cohort, 75% for short time to EOT PET, and 79% for long time to EOT PET. Sensitivity was 54% for the whole cohort, 59% for short time to EOT PET, and 49% for long time to EOT PET. Conclusions:Positive predictive value of EOT PET is suboptimal, and a substantial number of patients require follow up testing despite not ultimately relapsing. Waiting a longer time to PET (>6 weeks) results in slightly lower false positives, higher PPV and specificity, with slightly lower NPV and sensitivity.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».