Kinetic Analysis of Dominant Intraprostatic Lesion of Prostate Cancer Using Quantitative Dynamic [18F]DCFPyL-PET: Comparison to [18F]fluorocholine-PET
Bibliographic record
Abstract
Abstract PurposeIdentification of the dominant intraprostatic lesion(s) (DILs) can facilitate diagnosis and treatment by targeting biologically significant intra-prostatic foci. A PSMA ligand, [18F]DCFPyL (2-(3-{1-carboxy-5-[(6-[18F]fluoro-pyridine-3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid), is better than choline-based [18F]FCH (Fluorocholine) in detecting and localizing DIL because of higher tumour contrast, particularly when imaging is delayed to one hour post injection. The goal of this study was to investigate whether the different imaging performance of [18F]FCH and [18F]DCFPyL can be explained by their kinetic behaviour in prostate cancer (PCa) and to evaluate whether DIL can be accurately detected and localized using a short duration dynamic positron emission tomography (PET).Methods19 and 23 PCa patients were evaluated with dynamic [18F]DCFPyL and [18F]FCH PET, respectively. The dynamic imaging protocol with each tracer had a total imaging time of 22 mins and consisted of multiple frames with acquisition times from 10-180 s. Tumour and benign tissue regions identified by sextant biopsy were compared using standardized uptake value (SUV) and tracer kinetic parameters from kinetic analysis of time-activity curves.ResultsFor [18F]DCFPyL, logistic regression identified Ki and k4 as the optimal model to discriminate tumour from benign tissue (84.2% sensitivity and 94.7% specificity) while only SUV was predictive for [18F]FCH (82.6% sensitivity and 87.0% specificity) The higher k3 (binding) of [18F]FCH than [18F]DCFPyL explains why [18F]FCH SUV can differentiate tumour from benign tissue within minutes of injection. Superior [18F]DCFPyL tumour contrast was due to the higher k4/k3 (more rapid washout) in benign tissue compared to tumour tissue.ConclusionsDIL was detected with good sensitivity and specificity using 22-min dynamic [18F]DCFPyL PET and avoids the need for delayed post-injection imaging timepoints. The dissimilar in-vivo kinetic behaviour of [18F]DCFPyL and [18F]FCH could explain their different SUV images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".