Prostate cancer detection with multi‐parametric MRI: Logistic regression analysis of quantitative T2, diffusion‐weighted imaging, and dynamic contrast‐enhanced MRI
Bibliographic record
Abstract
PURPOSE: To develop a multi-parametric model suitable for prospectively identifying prostate cancer in peripheral zone (PZ) using magnetic resonance imaging (MRI). MATERIALS AND METHODS: Twenty-five radical prostatectomy patients (median age, 63 years; range, 44-72 years) had T2-weighted, diffusion-weighted imaging (DWI), T2-mapping, and dynamic contrast-enhanced (DCE) MRI at 1.5 Tesla (T) with endorectal coil to yield parameters apparent diffusion coefficient (ADC), T2, volume transfer constant (K(trans)) and extravascular extracellular volume fraction (v(e)). Whole-mount histology was generated from surgical specimens and PZ tumors delineated. Thirty-eight tumor outlines, one per tumor, and pathologically normal PZ regions were transferred to MR images. Receiver operating characteristic (ROC) curves were generated using all identified normal and tumor voxels. Step-wise logistic-regression modeling was performed, testing changes in deviance for significance. Areas under the ROC curves (A(z)) were used to evaluate and compare performance. RESULTS: The best-performing single-parameter was ADC (mean A(z) [95% confidence interval]: A(z,ADC): 0.689 [0.675, 0.702]; A(z,T2): 0.673 [0.659, 0.687]; A(z,Ktrans): 0.592 [0.578, 0.606]; A(z,ve): 0.543 [0.528, 0.557]). The optimal multi-parametric model, LR-3p, consisted of combining ADC, T2 and K(trans). Mean A(z,LR-3p) was 0.706 [0.692, 0.719], which was significantly higher than A(z,T2), A(z,Ktrans), and A(z,ve) (P < 0.002). A(z,LR-3p) tended to be greater than A(z,ADC), however, this result was not statistically significant (P = 0.090). CONCLUSION: Using logistic regression, an objective model capable of mapping PZ tumor with reasonable performance can be constructed.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".