Zone-DR: Discovery Radiomics via Zone-level Deep Radiomic Sequencer Discovery for Zone-based Prostate Cancer Grading using Diffusion Weighted Imaging
Bibliographic record
Abstract
Prostate cancer is the most commonly diagnosed cancer in men, however prognosis is relatively good given sufficiently early diagnosis. This motivates the need for fast and reliable prostate cancer grading. In this study, we investigate the efficacy of a discovery radiomics strategy for prostate zone-based cancer grading using a deep radiomic sequencer discovered from diffusion weighted imaging (DWI) data. More specifically, we propose Zone-DR, a discoveryradiomics approach based on zone-level deep radiomic sequencer discovery that discover radiomic feature directly from DWI data. Experimental results using 12, 466 pathology-verified zones obtainedfrom DWI data of 101 patients showed that the proposed Zone-DR approach achieved higher accuracy than a threshold-based approach for both ADC and CHB-DWI. Furthermore, the results also showed that the trade-off between sensitivity and specificity can be based approach and Zone-DR optimized based on the particular clinical scenario we wish to employ Zone-DR for, such as clinical screening versus surgical planning.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".