Relationship between Gleason score and apparent diffusion coefficients of diffusion-weighted magnetic resonance imaging in prostate cancer patients
Bibliographic record
Abstract
Introduction: We assessed the correlation between the apparent diffusion coefficient (ADC) and pathological Gleason score (GS) of prostate cancer patients.Methods: A total of 125 patients who underwent multiparametric magnetic resonance imaging before radical prostatectomy for prostate cancer were included in this study. ADC values were compared with different GS. We used receiver operating characteristic analysis and determined the ADC cutoff value to differentiate tumours with a GS of 6 from those with a GS ≥7.Results: We identified 34 patients (27.2%) with a GS of 6; 33 patients (26.4%) with a GS of 7; 22 patients (17.6%) with a GS of 8; and 36 patients (28.8%) with a GS of ≥9. The mean ADC value for disease with a GS of 6 was 0.914 ± 0.161 x10-3 mm2/s; GS of 7: 0.741 ± 0.164 x10-3 mm2/s; GS of 8: 0.679 ± 0.130 x10-3 mm2/s; and GS of ≥9: 0.593 ± 0.089 x10-3 mm2/s. An ADC value of 0.830 ×10−3mm2/s was the best cutoff value to identify prostate cancer with a GS of 6.Conclusions: We observed an inverse relationship between GS and ADC value. Moreover, a cutoff ADC value may help differentiate disease with a GS of 6 from disease with a GS ≥7.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".