18F-Fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) is accurate for high-grade prostate cancer bone staging when compared to bone scintigraphy
Bibliographic record
Abstract
Introduction: In this study, we compared 18F-FDG-postron emission tomography/computed tomography (PET/CT) and bone scintigraphy accuracies for the detection of bone metastases for primary staging in high-grade prostate cancer (PCa) patients to determine if 18F-FDG-PET/CT could be used alone as a staging modality. Methods: Men with localized high-grade PCa (n=256, Gleason 8–10, International Society of Urological Pathology [ISUP] grades 4 or 5) were imaged with bone scintigraphy and 18F-FDG-PET/CT. We compared on a per-patient basis the accuracy of the two imaging modalities, taking intermodality agreement as the standard of truth (SOT). Results: 18F-FDG-PET/CT detected at least one bone metastasis in 33 patients compared to only 26 with bone scan. Of the seven false-negative bone scintigraphies, four (57.1%) were solitary metastases (monometastatic), three (42.9%) were oligometastatic (2–4 lesions), and none were plurimetastatic (>4 lesions). Compared to SOT, 18F-FDG-PET/CT showed higher sensitivity and accuracy than bone scintigraphy (100% vs. 78.8%, and 98.7% vs. 98.2%) for the detection of skeletal lesions. Conclusions: 18F-FDG-PET/CT appears similar or better than conventional bone scans to assess for bone metastases in patients newly diagnosed with high-grade PCa. Since intraprostatic FDG-uptake is also a biomarker of failure to radical prostatectomy and that FDG-PET/CT has been shown to be accurate in detecting PCa lymph node metastasis, FDG-PET/CT has the potential to be used as the sole preoperative staging modality in high-grade PCa.
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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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".