Assessing the optimal technique to determine reference SUV for tissue to reference ratios using F18-DCFPyL in Whole Body PET-MR imaging
Notice bibliographique
Résumé
2005 Objectives: Prostate specific membrane antigen (PSMA) is highly expressed in most prostate cancer cells [1]. New PET radiolabeled tracers like F18-DCFPyL, bind to PSMA and show promise in the diagnosis, staging and longitudinal assessment of response to therapeutic interventions [1]. Degree of tracer uptake at tumor sites is semi-quantitatively assessed in comparison to reference tissues. This decreases variability in interpretation and is the basis for the newly proposed molecular imaging classification system for prostate cancer (‘PROMISE’) [2]. Currently, there is a lack of data on the variability in the reference tissues used to assess degree of uptake, namely blood pool activity, liver and parotid glands. The purpose of the current study was to determine the variability in SUV measurement for reference tissues on F18-DCFPyL PET-MR imaging. Methods: Whole body PET-MR images from 5 male patients injected with F18-DCFPyL (325MBq ±10%) were analyzed retrospectively. At 120mins (±10mins) post injection, images were acquired from vertex to mid-thigh using 5 bed positions at 4mins each. PET-MR data were reconstructed using the 2-point Dixon MR attenuation correction, HDPET (Point-spread function), 3 iterations with 21 subsets, 172x172 matrix, and absolute scatter correction. In addition to no post-reconstruction smoothing (All-Pass), Gaussian filters at different full width half maximum (4, 5, 6 cm) were applied (4 sets of images), in order to evaluate the effect of image noise on the analyses. Sphere VOIs were drawn over the right parotid gland (1cm3 ±0.5cm3), the left ventricle cavity as a representative of blood pool (3cm3 ±0.5cm3), and the caudal aspect of the right liver lobe (3cm3 ±0.5cm3) on all 4 sets of images. SUVmax, SUVmin, SUVmean as well as standard deviations were calculated for all VOIs. Three out of the 5 patients underwent post-treatment imaging with F18-DCFPyL within a 5-7 month timeframe. The same reconstruction methods and VOIs were applied to the post-treatment image sets. Coefficient of variation (CV) was calculated for all SUV values in each reference region and in all reconstructed image sets. To determine test-retest variability in the 3 patients with pre and post treatment imaging, mean % differences (% ± SD) were calculated in each reference tissue VOI. Results: Results showed that the SUVmean for the VOI in the right lower lobe of the liver (CV ranging from 0.11% to 0.51%) and left ventricle (CV ranging from 0.23% to 1.00%) were relatively more stable for all reconstruction methods on the PET-MR images than the VOI in the right parotid gland (CV ranging from 0.19% to 8.44%). As for test-retest variability, the absolute SUVmean difference was less than 10% for the VOIs in the right lower lobe of the liver (2.55% ± 2.4) and left ventricle (4.79% ± 1.96) and greater than 10% for the right parotid gland (15.35 % ± 12.53). Conclusions: Test-retest for tissue VOIs in the right lower lobe of the liver and the left ventricle showed a more reliable finding than the right parotid gland. Both liver and blood pool VOIs produced less variability than the parotid gland. These findings warrant consideration in future studies applying semi-quantitative methods for longitudinal assessment of lesions over time using F18- DCFPyL in PET-MR imaging.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,006 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 ».