Bone marrow dosimetry using blood-based models for radiolabeled antibody therapy: a multiinstitutional comparison.
Notice bibliographique
Résumé
UNLABELLED: Standardization of marrow dosimetry is of considerable importance when estimating dose-response for a multicentered clinical trial involving radionuclide therapy. However, it is only within the past five years that the intercomparison of marrow dosimetry results among separate clinical trials that use the same agent has become scientifically feasible. In this work, we have analyzed reported marrow dosimetry results from radioimmunotherapy trials and recalculated marrow absorbed doses at a central facility using a standard blood model with patient-specific source data. The basic approach used in the American Association of Physicists in Medicine (AAPM)/Sgouros marrow dosimetry methodology was common to calculation performed at all participating institutions, including the central facility. Differences in dose estimates associated with starting assumptions and the exact implementation of the AAPM/Sgouros calculation methodology used by the source institutions and the central facility were quantified and compared. METHODS: Data from 22 patients enrolled in radiolabeled antibody clinical trials were randomly selected from 7 participating institutions for the assessment of marrow dose. The analysis was restricted to those patients who were treated with 131I- or 186Re-labeled antibody and had no marrow involvement. Calculation of bone marrow dose at each participating institution was unique to the trial or institution, but all used some form of the AAPM/Sgouros blood model approach. The central facility adopted a marrow dosimetry model based on the AAPM/Sgouros model for radiolabeled antibodies using the standard MIRD approach to the remainder-of-body contribution. A standardized approach to account for variations in patient mass was used for the remainder-of-body component. To simplify clinical implementation, regional marrow uptake and time-dependent changes in the marrow-to-blood concentration ratio were not included. Methods of formatting the collection of standard datasets useful in defining dose-response parameters are also presented. RESULTS: Bone marrow doses were calculated according to the method described for each of the 22 patients based on the patient-specific data supplied by the participating institutions. These values were then individually compared with the marrow doses originally reported by each institution. Comparison of the two calculation methods was expressed as a ratio of the marrow doses for each patient. The mean ratio for the dose estimates at the participating institution calculation compared with the central laboratory value was 0.920 +/- 0.259 (mean +/- SD), with a range from 0.708 to 1.202. CONCLUSION: The independent use of the AAPM/Sgouros method blood model approach to marrow dosimetry has brought these dose estimates to within 30% of the results obtained centrally compared with substantially higher uncertainties reported previously. Variations in calculation methodology or initial assumptions adopted by individual institutions may still contribute significant uncertainty to dose estimates, even when the same data are used as a starting point for the calculation comparison shown here. A clinically relevant, standard method for marrow dosimetry for radiolabeled antibodies is proposed as a benchmark for intercomparison purposes. A parameter sensitivity analysis and a summary discussion of the use of this model for potentially improving dose-response data correlation are also presented.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».