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
Bibliographic record
Abstract
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 ...
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".