Predicting biochemical recurrence after prostatectomy: Can machine learning beat CAPRA score? Results of a multicentric retrospective analysis on 4,700 patients.
Bibliographic record
Abstract
343 Background: Recent advances in machine learning algorithms and deep learning solutions paved the way for improved accuracy in survival analysis. We aim to investigate the accuracy of conventional machine learning and deep learning methods in the prediction of 3-year biochemical recurrence (BCR) as compared to CAPRA score prediction. Methods: A total of 5043 men who underwent RP between 2000 and 2015 for clinically localized PCa iwere analyzed retrospectively. Three-year BCR was predicted using the following models: CAPRA score, Cox regression analysis, logistic regression, k-nearest neighbor, random forest and densely connected feed-forward neural network classifier. The discrimination of the models was quantified using the C-index or the area under the receiver operating characteristics curve. Results: Patients with CAPRA score 2 and 3 accounted for 64% of the population. C-index measuring performance for the prediction of the three-year BCR for CAPRA score was 0.63. C-index values for k-neighbor classifier, logistic regression, Cox regression analysis, random forest classifier and densely optimized neural network were respectively 0.55, 0.63, 0.64, 0.64 and 0.70 (pairwise, adjusted p-value < 0.01). After inclusion of available post-surgical variables, C-index value reached respectively 0.58, 0.77, 0.74, 0.75 and 0.84 (pairwise, adjusted p-value < 0.05). Conclusions: Our results show that CAPRA score performed poorly in intermediate-risk patients undergoing RP. Densely connected neural networks with simple architecture further increased predictive power with low computational cost. In order to predict 3-years BCR, adding post-surgical features to the model greatly enhanced its performance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".