The Relationship between D’Amico and ISUP Risk Classifications and 68Ga-PSMA PET/CT SUVmax Values in Newly Diagnosed Prostate Cancers
Bibliographic record
Abstract
Introduction: This study aimed to evaluate the relationship between pathological and clinical risk classifications in newly diagnosed prostate cancer patients, and 68Ga-PSMA PET/CT data and serum Prostate Specific Antigen (PSA) values. Method: A total of 203 patients who were diagnosed with prostate cancer between 2019 and 2023, who had not yet received treatment and who underwent 68Ga-PSMA PET/CT for staging purposes were included in this study. Results: There was a substantial correlation between D’Amico risk classification, Gleason score, ISUP classification, and the presence or absence of metastasis (p < 0.0001). The median SUVmax value of the prostate gland and the D’Amico risk classification were statistically significantly correlated. (p < 0.0001). There was a statistically significant correlation between the ISUP classification and the PSA value and prostate gland SUVmax value (p < 0.0001). There was a significant correlation between the median SUVmax values of the prostate gland at the time of diagnosis and the patients with and without metastases (p < 0.0001). According to the data obtained from ROC analysis, patients with prostate gland SUVmax values of 8.75 and above were found to have a high probability of metastasis with a sensitivity of 78.9% and a specificity of 59.05%. Conclusion: Our study showed that 68Ga-PSMA PET/CT is a highly effective method for staging newly diagnosed high-risk prostate cancer. The probability of metastasis was found to be dramatically increased in Gleason 8 and above. According to D’Amico risk classification, metastasis was detected in at least half of high-risk patients. Since the sensitivity of metastasis was 78.9% in patients with prostate gland SUVmax value above 8.75, we think that these patients should be carefully reported in terms of metastasis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".