A Comparative Study Between Apparent Diffusion Imaging and Correlated Diffusion Imaging for Prostate Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it