SU-F-R-28: Correction of FCh-PET Bladder Uptake Using Virtual Sinograms and Investigation of Its Impact On the Quantification of Prostate Textural Characteristics
Bibliographic record
Abstract
Purpose: Textural biomarkers as a tool for quantifying intratumoral heterogeneity hold great promise for diagnosis and early assessment of treatment response in prostate cancer. However, spill-in counts from the bladder uptake are suspected to have an impact on the textural measurements of the prostate volume. This work proposes a correction method for the FCh-PET bladder uptake and investigates its impact on intraprostatic textural properties. Methods: Two patients with PC received pre-treatment dynamic FCh-PET scans reconstructed at four time points (interval: 2 min), for which prostate and bladder contours were obtained. Projection bins affected by bladder uptake were determined by forward-projection. For each time point and axial position, virtual sinograms were obtained and affected bins replaced by a weighted combination of original values and values interpolated using cubic spline from non-affected bins of the current and adjacent projection angles. The process was optimized using a genetic algorithm in terms of minimization of the root-mean-square error (RMSE) within the bladder between the corrected dynamic time point volume and a reference initial uptake volume. Finally, the impact of the bladder uptake correction on the prostate region was investigated using two standard SUV metrics (1) and three texture metrics (2): 1) SUVmax, SUVmean; 2) Contrast, Homogeneity, Coarseness. Results: Without bladder uptake correction, SUVmax and SUVmean were on average overestimated in the prostate by 0%, 0%, 33.2%, 51.2%, and 3.6%, 6.0%, 2.9%, 3.2%, for each time point respectively. Contrast varied by −9.1%, −6.7%, +40.4%, +107.7%, and Homogeneity and Coarseness by +4.5%, +1.8%, −8.8%, −14.8% and +1.0%, +0.5%, −9.5%, +0.9%. Conclusion: We proposed a method for FCh-PET bladder uptake correction and showed an impact on the quantification of the prostate signal. This method achieved a large reduction of intra-prostatic SUVmax while minimizing the impact on SUVmean. Further investigation is necessary to interpret changes in textural features. SL acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".