MP53-18 PROSTATE SPECIFIC MEMBRANE ANTIGEN POSITRON EMISSION TOMOGRAPHY FOR THE IDENTIFICATION OF INTRA-PROSTATIC TUMORS: INVESTIGATING DELINEATION GUIDELINES FOR FOCAL THERAPY AND GUIDED BIOPSY
Notice bibliographique
Résumé
You have accessJournal of UrologyProstate Cancer: Localized: Radiation Therapy (MP53)1 Apr 2020MP53-18 PROSTATE SPECIFIC MEMBRANE ANTIGEN POSITRON EMISSION TOMOGRAPHY FOR THE IDENTIFICATION OF INTRA-PROSTATIC TUMORS: INVESTIGATING DELINEATION GUIDELINES FOR FOCAL THERAPY AND GUIDED BIOPSY Ryan Alfano*, Glenn Bauman, Jonathan Thiessen, Irina Rachinsky, William Pavlosky, John Butler, Madeleine Moussa, Jose Gomez-Lemus, Mena Gaed, Stephen Pautler, Joseph Chin, and Aaron Ward Ryan Alfano*Ryan Alfano* More articles by this author , Glenn BaumanGlenn Bauman More articles by this author , Jonathan ThiessenJonathan Thiessen More articles by this author , Irina RachinskyIrina Rachinsky More articles by this author , William PavloskyWilliam Pavlosky More articles by this author , John ButlerJohn Butler More articles by this author , Madeleine MoussaMadeleine Moussa More articles by this author , Jose Gomez-LemusJose Gomez-Lemus More articles by this author , Mena GaedMena Gaed More articles by this author , Stephen PautlerStephen Pautler More articles by this author , Joseph ChinJoseph Chin More articles by this author , and Aaron WardAaron Ward More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000915.018AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate specific membrane antigen positron emission tomography (PSMA PET) has shown good concordance with histology in involved sextants, but there is a need, addressed in this study, to investigate the ability of PSMA PET to delineate dominant intraprostatic lesion (DIL) boundaries for guided biopsy and focal therapy planning. METHODS: We registered pathologist-annotated whole-mount mid-gland prostatectomy histology sections from 12 patients to pre-surgical PSMA PET/MRI scans using our previously published accurate method. We generated PET derived tumor volumes using boundaries defined by thresholded PET volumes from 1–100% of max SUV in 1% intervals. At each interval, we applied a margin of 0–30 voxels in one voxel increments, giving 3,000 volumes per patient. We calculated sensitivity and specificity for cancer detection within the 2D oblique histologic planes that intersected with the 3D PET volume for each patient. We determined the threshold and margin combination that satisfied the following criteria: ≥95% sensitivity with max specificity (supporting focal therapy) and ≥95% specificity with max sensitivity (supporting guided biopsy). RESULTS: Figure 1 shows histologic cancer sensitivity (left) and specificity (right) as a function of SUV threshold and expansion margin. A threshold of 67% SUV max with an 8.4 mm margin (white circle) achieved a (mean ± std.) sensitivity of 95.0 ± 7.8% and specificity of 76.4 ± 14.7%. A threshold of 81% SUV max with a 5.1 mm margin (white diamond) achieved sensitivity of 65.1 ± 28.4% and specificity of 95.1 ± 5.2%. CONCLUSIONS: This study used accurate co-registration of PSMA PET/MRI and histopathology to determine SUV thresholds and margin expansions having high sensitivity, supporting focal therapy, and high specificity, supporting guided biopsy. These parameters can be used in a larger validation study supporting clinical translation. Source of Funding: Prostate Cancer Canada, Natural Sciences and Engineering Research Council, Canadian Institutes of Health Research, Ontario Institute for Cancer Research © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e789-e790 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ryan Alfano* More articles by this author Glenn Bauman More articles by this author Jonathan Thiessen More articles by this author Irina Rachinsky More articles by this author William Pavlosky More articles by this author John Butler More articles by this author Madeleine Moussa More articles by this author Jose Gomez-Lemus More articles by this author Mena Gaed More articles by this author Stephen Pautler More articles by this author Joseph Chin More articles by this author Aaron Ward More articles by this author Expand All Advertisement PDF downloadLoading ...
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 | 0,012 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,006 |
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 ».