Three-dimensional ultrasound-based spectroscopic imaging for the detection of prostate cancer.
Bibliographic record
Abstract
234 Background: The objective of this translational research was to investigate the use of real-time novel three-dimension, quantitative ultrasound-based spectroscopic imaging of the prostate as a means of cancer detection. Methods: Fourteen patients with T2-3 prostate cancer underwent a 6–9 MHz trans-rectal ultrasound scan of the prostate prior to radical prostatectomy. Equally spaced axial ultrasound images (0.5 cm separation) corresponding elasticity and spectroscopy data were collected in each patient. Colour-coded spectroscopic parametric maps of 0-Mhz intercept (0-Mhz), mid-band fit (MBF) and slope of line of best fit (slope) were generated indicating where the disease in the prostate gland is hypothetically located. Quantitative data (% volume of cancer over the prostate gland) were compared to whole-mount radical prostatectomy histopathology maps to determine the sensitivity and accuracy in parametrically delineating prostate cancer. Results: Representative data indicate spectral changes were associated with the presence of co-incident disease as located on correlative histopathology whole mount sections. Of the 14 patients enrolled, 7 have been analyzed and presented here. The mean % difference between 0-MHz and MBF, with H&E, was 14% (SD 38%) and 21% (SD 24%), respectively. Gross areas of disease were readily visualized in ultrasound parametric maps and corresponded to a maximum 10dB decrease in 0-MHz or MBF. Parametric maps generated from the spectral slope offered no discrimination of disease. There were differences in scatterer size estimates and scatter concentration estimates between putative disease areas and the remaining tissue. Conclusions: Initial results suggest that there is good correlation between spectroscopic maps with disease on whole-mount specimens. This method may ultimately permit ultrasound-guided targeted biopsies to improve detection rates and non-invasive assessment of disease for radiotherapy planning.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".