Prostate Tissue Composition and MR Measurements: Investigating the Relationships between ADC, T2,<i>K</i><sup>trans</sup>,<i>v</i><sub>e</sub>, and Corresponding Histologic Features
Bibliographic record
Abstract
PURPOSE: To investigate relationships between magnetic resonance (MR) imaging measurements and the underlying composition of normal and malignant prostate tissue. MATERIALS AND METHODS: Twenty-four patients (median age, 63 years; age range, 44-72 years) gave informed consent to be examined for this research ethics board-approved study. Before undergoing prostatectomy, patients were examined with T2-weighted, diffusion-weighted, T2 mapping, and dynamic contrast material-enhanced MR imaging at 1.5 T. Maps of apparent diffusion coefficient (ADC), T2, volume transfer constant (K(trans)), and extravascular extracellular space (v(e)) were calculated. Whole-mount hematoxylin-eosin-stained sections were generated and digitized at histologic resolution. Percentage areas of tissue components (nuclei, cytoplasm, stroma, luminal space) were measured by using image segmentation. Corresponding regions on MR images and histologic specimens were defined by using anatomically defined segments in peripheral zone (PZ) and central gland tissue. Cancer and normal PZ regions were identified at histopathologic analysis. Each MR parameter-histologic tissue component pair was assessed by using linear mixed-effects models, and cancer versus normal PZ values were compared by using nonparametric tests. RESULTS: ADC and T2 were inversely related to percentage area of nuclei and percentage area of cytoplasm and positively related to percentage area of luminal space (P < or = .01). These trends were reversed for K(trans) (P < .001). K(trans) had a significantly negative (P = .01) slope versus percentage area of stroma, and v(e) had a positive (P = .008) slope versus percentage area of stroma. The v(e) was inversely proportional to the percentage area of nuclei (P = .05). All MR imaging parameters (P < or = .05) and the percentage areas of all tissue components (P < or = .001) except stroma (P > .48) were significantly different between cancer and normal PZ tissue. CONCLUSION: MR imaging-derived parameters measured in the prostate were significantly related to the proportion of specific histologic components that differ between normal and malignant PZ tissue. These relationships may help define imaging-related histologic prognostic parameters for prostate cancer.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".