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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".