Combined diffusion‐weighted and dynamic contrast‐enhanced MRI for prostate cancer diagnosis—Correlation with biopsy and histopathology
Bibliographic record
Abstract
PURPOSE: To determine whether the combination of diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI provides higher diagnostic sensitivity for prostate cancer than each technique alone. MATERIALS AND METHODS: Fourteen patients with a clinical suspicion of prostate cancer underwent endorectal MRI on a 1.5T scanner prior to transrectal ultrasound (TRUS)-guided biopsies. The average values of the apparent diffusion coefficient (ADC, calculated from b-values of 0 and 600), K(trans), v(e), maximum gadolinium (Gd) concentration, onset time, mean gradient, and maximum enhancement were determined. Correlation with histology was based on biopsy (six patients) and prostatectomy specimen (eight patients) results. The Tukey-Kramer test was used for statistical analysis. RESULTS: The average values of all MRI parameters, except v(e) and maximum Gd concentration, showed significant differences between tumor and normal prostate. The sensitivity and specificity values were respectively 54% (35-72%) and 100% (95-100%) for the ADC data, and 59% (39-77%) and 74% (63-83%) for the DCE data. When both ADC and DCE results were combined, the sensitivity increased to 87% (68-95%) and specificity decreased to 74% (62-83%). CONCLUSION: All but two DW- and DCE-MRI parameters showed significant differences between tumor and normal prostate. Combining both techniques provides better sensitivity, with a small decrease in specificity.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".