Deep learning prediction of non-perfused volume without contrast agents during prostate ablation therapy
Bibliographic record
Abstract
Abstract The non-perfused volume (NPV) is an important indicator of treatment success immediately after prostate ablation. However, visualization of the NPV first requires an injection of MRI contrast agents into the bloodstream, which has many downsides. Purpose of this study was to develop a deep learning model capable of predicting the NPV immediately after prostate ablation therapy without the need for MRI contrast agents. A modified 2D deep learning UNet model was developed to predict the post-treatment NPV. MRI imaging data from 95 patients who had previously undergone prostate ablation therapy for treatment of localized prostate cancer were used to train, validate, and test the model. Model inputs were T1/T2-weighted and thermometry MRI images, which were always acquired without any MRI contrast agents and prior to the final NPV image on treatment-day. Model output was the predicted NPV. Model accuracy was assessed using the Dice-Similarity Coefficient (DSC) by comparing the predicted to ground truth NPV. A radiologist also performed a qualitative assessment of NPV. Mean (std) DSC score for predicted NPV was 85% ± 8.1% compared to ground truth. Model performance was significantly better for slices with larger prostate radii (> 24 mm) and for whole-gland rather than partial ablation slices. The predicted NPV was indistinguishable from ground truth for 31% of images. Feasibility of predicting NPV using a UNet model without MRI contrast agents was clearly established. If developed further, this could improve patient treatment outcomes and could obviate the need for contrast agents altogether. Trial Registration NumbersThree studies were used to populate the data: NCT02766543, NCT03814252 and NCT03350529.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".