Predicting PSA50 response to $$^{177}$$Lu-PSMA therapy using machine learning and automated total tumor volume
Why this work is in the frame
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Bibliographic record
Abstract
The 177Lu-PSMA therapy is an established treatment for metastatic castration-resistant prostate cancer (mCRPC), targeting the prostate-specific membrane antigen (PSMA). Despite well-established correlations between 68Ga-PSMA PET/CT imaging and outcome, predicting individual patient responses remains a significant challenge. This study introduces an automated method for computing the total tumor volume (TTV) from 68Ga-PSMA PET/CT imaging and develops predictive models to assess patient biological response via the PSA50 criterion. A retrospective analysis was conducted on a real-world data cohort of 139 mCRPC patients treated in our institution. TTV was automatically extracted from PET/CT images and correlated with treatment response, defined by PSA50 criteria. Machine learning models, including Logictic Regression with L1 (LASSO) and Support Vector Machine (SVM), were developed to predict PSA50 response using imaging and clinical features. The best-performing models achieved F1-scores of 0.68 and 0.67, comparable to existing nomograms. Correlation analysis identified TTV-derived features and time since diagnosis as significant predictors of response. The proposed workflow offers an automated and reproducible approach to predicting treatment response in 177Lu-PSMA therapy. Limitations remain for lesion segmentation within physiological regions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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