FDG-PET/CT for pre-operative staging and prognostic stratification of patients with high-grade prostate cancer at biopsy
Bibliographic record
Abstract
BACKGROUND: The role of (18)F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) in prostate cancer (PCa) has not been well defined yet. Because high-grade PCa tends to exhibit increased glycolytic rate, FDG-PET/CT could be useful in this setting. The aim of this study was to assess the value of FDG-PET/CT for pre-operative staging and prognostic stratification of patients with high-grade PCa at biopsy. METHODS: Fifty-four patients with a Gleason sum≥8 PCa at biopsy underwent FDG-PET/CT as part of the staging workup. Thirty-nine patients underwent radical prostatectomy (RP) and pelvic lymph node (LN) dissection, 2 underwent LN dissection only, and 13 underwent non-surgical treatments. FDG-PET/CT findings from clinical reports, blinded reading and quantitative analysis were correlated with clinico-pathological characteristics at RP. RESULTS: Suspicious foci of increased FDG uptake were found in the prostate, LNs and bones in 44, 13 and 6% of patients, respectively. Higher clinical stage, post-RP Gleason sum and pattern, and percentage of cancer involvement within the prostate were significantly associated with the presence of intraprostatic FDG uptake (IPFU) (P<0.05 in all cases). Patients without IPFU who underwent RP were downgraded to Gleason≤7 in 84.6% of cases, as compared to 30.8% when IPFU was reported (P=0.003). Qualitative and quantitative IPFU were significantly positively correlated with post-RP Gleason pattern and sum, and pathological T stage. Absence and presence of IPFU were associated with a median 5-year cancer-free survival probability of 70.2 and 26.9% (P=0.0097), respectively, using the CAPRA-S prognostic tool. CONCLUSION: These results suggest that, among patients with a high-grade PCa at biopsy, FDG-PET/CT could improve pre-treatment prognostic stratification by predicting primary PCa pathological grade and survival probability following RP.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".