Computer-assisted lesion classification and intervention planning for prostate cancer
Bibliographic record
Abstract
Multi-parametric magnetic resonance imaging (mp-MRI) is emerging as a useful tool for classifying prostate cancer (PCa); however, it suffers from two major limitations: (1) complex, multi-dimensional signals make interpretation challenging and (2) inter-observer variability of lesion classification between physicians. Critically needed are methods for augmenting the interpretability of mp-MRI to assist in lesion classification. To meet this need, we leveraged a patient cohort with post-surgery pathologist-annotated transverse histology images registered to pre-surgery in-vivo mp-MRI with a measured target registration error. We developed a radiomics-based machine learning model trained on annotations for PCa vs. non-PCa, and found that a 5-feature Naïve-Bayes classifier classified these two types of regions in leave-one-patient-out cross-validation with an area under the receiver operating characteristic curve of 0.80. We then investigated augmentation of mp-MRI interpretability via additional imaging using prostate-specific membrane antigen positron emission tomography (PSMA-PET). Using patients imaged on a PET/MRI hybrid-scanner, we investigated segmentation recommendations for classifying the dominant intraprostatic lesion. For focal therapy, we found an optimal threshold of 67% SUVmax and an 8.4 mm margin produced mean voxel-wise sensitivity of 95% with mean specificity 76%. For guided biopsy, we found that a threshold of 81% SUVmax and a 5.2 mm margin produced a mean sensitivity of 65% and mean specificity of 95%. Furthermore, we assessed the effect of boosting high-dose-rate brachytherapy treatment plans towards these segmentations. We found that dose to the underlying high-grade cancer was significantly increased in targeted plans, compared to whole-gland plans, while maintaining all dose constraints.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.001 |
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".