Sci—Fri AM: Imaging — 01: Feasibility of estimating choline kinase activity with kinetic modeling of 18F‐fluorocholine pet imaging of prostate cancer
Bibliographic record
Abstract
Prostate cancer (PCa) detection and delineation remains a challenge for medical imaging. Studies have shown 18F‐Fluorocholine (FCH) PET imaging to be a promising modality in the detection of recurrent PCa. Detection of denovo PCa is more challenging, as lesions such as benign prostatic hyperplasia (BPH) may adversely affect the sensitivity and specificity of the modality. PCa and BPH have been shown to exhibit similar uptake of FCH, yet it has been shown that phosphocholine levels are much more elevated in PCa compared to BPH. Therefore, it would be useful to measure the activity of phosphorylation via choline kinase (k3) in order to differentiate PCa from BPH. This work examines the feasibility of using a compartmental model to estimate k3 with dynamic 18F‐Fluorocholine PET imaging. JSim software [1] was used to simulate the compartmental model for FCH exchange. A simulated tissue curve was generated using predefined parameters and the model's ability to estimate these parameters through fitting of the simulated tissue curve with and without noise was investigated. The fitting procedure was performed using the non‐negative least squares algorithm in MATLAB after the equation governing fitting was linearized. In the noiseless case, the model was able to accurately identify the values of each rate parameter. For the noisy case with an SNR of 10:1, the mean estimated k3 for 10,000 runs had a coefficient of variation of 14.9%. The kinetic model shows promise for quantifying k3, which would allow the differentiation of malignant and benign tumours of the prostate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".