Novel Bone Scan Features for Predicting Prognosis in Men With Bone Metastatic Prostate Cancer: A Retrospective Study
Bibliographic record
Abstract
Background: Bone metastasis frequently occurs in patients with prostate cancer, however, a consensus has not been reached regarding bone scan image analysis.We aimed to analyse various bone scan imaging features of metastatic prostate cancer and to assess their impact on prognosis.Methods: One thousand five hundred sixty-three paired sets of bone scan images (anterior and posterior) were obtained from patients with metastatic prostate cancer at Seoul National University Hospital.U-Net architecture was used for the segmentation of metastatic bone lesions.Imaging features describing the overall metastatic burden (n = 18) and largest metastatic burden (n = 32) were extracted using computer vision techniques.Kaplan-Meier survival analysis and Cox proportional risk model were used to analyse the prognostic impact of each feature. Results:The correlation coefficient between the actual number of lesions and that predicted by the deep learning model was 0.87, indicating a strong correlation.Multivariate Cox regression showed that metastasis intensity difference (hazard ratio [HR], 0.53; P = 0.002) and the largest metastasis percentage (HR, 0.62; P = 0.038) were independently associated with disease progression and were even more strongly associated with the number of metastases (current standard).The Kaplan-Meier curve revealed that a higher total metastasis ratio (P < 0.001), a higher total metastasis intensity difference (P = 0.030), the largest metastatic lesion percentage (P < 0.001), compactness (P = 0.028), and eccentricity (P = 0.070) were associated with shorter progression-free survival.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".