Hyperpolarized 13C Lactate, Pyruvate, and Alanine: Noninvasive Biomarkers for Prostate Cancer Detection and Grading
Bibliographic record
Abstract
An extraordinary new technique using hyperpolarized (13)C-labeled pyruvate and taking advantage of increased glycolysis in cancer has the potential to improve the way magnetic resonance imaging is used for detection and characterization of prostate cancer. The aim of this study was to quantify, for the first time, differences in hyperpolarized [1-(13)C] pyruvate and its metabolic products between the various histologic grades of prostate cancer using the transgenic adenocarcinoma of mouse prostate (TRAMP) model. Fast spectroscopic imaging techniques were used to image lactate, alanine, and total hyperpolarized carbon (THC = lactate + pyruvate + alanine) from the entire abdomen of normal mice and TRAMP mice with low- and high-grade prostate tumors in 14 s. Within 1 week, the mice were dissected and the tumors were histologically analyzed. Hyperpolarized lactate SNR levels significantly increased (P < 0.05) with cancer development and progression (41 +/- 11, 74 +/- 17, and 154 +/- 24 in normal prostates, low-grade primary tumors, and high-grade primary tumors, respectively) and had a correlation coefficient of 0.95 with the histologic grade. In addition, there was minimal overlap in the lactate levels between the three groups with only one of the seven normal prostates overlapping with the low-grade primary tumors. The amount of THC, a possible measure of substrate uptake, and hyperpolarized alanine also increased with tumor grade but showed more overlap between the groups. In summary, elevated hyperpolarized lactate and potentially THC and alanine are noninvasive biomarkers of prostate cancer presence and histologic grade that could be used in future three-dimensional (13)C spectroscopic imaging studies of prostate cancer patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".