A Comparative Study Between Apparent Diffusion Imaging and Correlated Diffusion Imaging for Prostate Cancer
Bibliographic record
Abstract
Prostate cancer is the second most common cancer in men world-wide, with approximately 174,650 new cases diagnosed in 2019 inthe U.S. [1]. However, prognosis is relatively good given sufficientlyearly detection during the non-metastatic stage, motivating the needfor fast and reliable cancer screening methods. Diffusion weightedimaging is a magnetic resonance imaging technique that is gainingtraction as a noninvasive method for cancer screening. In 2013, anew form of diffusion weighted imaging called correlated diffusionimaging (CDI) was introduced as a potential candidate modality forbuilding computer-aided clinical decision support systems [2]. Weperform a large scale study, across 101 patient cases with full PI-RADS score and histopathology, to compare the performance ofcorrelated diffusion imaging in prostate cancer detection and localization to apparent diffusion coefficient maps, the most commonlyused diffusion weighted imaging-derived imaging modality in can-cer grading. Using threshold-based classification, experimental results showed that CDI achieves higher specificity at high sensitivityvalues of 90% and 95%, suggesting that CDI is well suited for scenarios where high sensitivity is crucial, such as cancer screening.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".