Utility of FDG-PET/CT in Patients with Advanced Renal Cell Carcinoma with Osseous Metastases: Comparison with CT and <sup>99m</sup> Tc-MDP Bone Scan in a Prospective Clinical Trial
Bibliographic record
Abstract
Objective: Compare FDG-PET/CT, CT, and bone scan for detecting and monitoring bone metastases’ response in metastatic renal cell cancer (mRCC). Methods: Patients with mRCC prospectively underwent FDG-PET/CT, CT, and bone scans at baseline and after 8 weeks of therapy. Tumor visibility and metabolic activity were retrospectively recorded. Response was evaluated by PERCIST, RECIST, and MD Anderson bone criteria. Kaplan-Meier methodology estimated event-time distributions for PFS, OS, and time to symptomatic skeletal event (SSE). Log-rank test tested differences in event-time distributions between response at 8 weeks by response criteria. Results: Sixteen patients ( n = 30; 53%) were evaluable. Baseline FDG-PET/CT detected more osseous metastases ( n = 55) than CT ( n = 45) or bone scan ( n = 34). From baseline to 8 weeks, metabolic activity of lesions decreased >20%, while qualitative and quantitative CT and bone scan parameters were unchanged for most patients. Partial metabolic responders by PERCIST had longer PFS and OS ( n = 5, 20+ months) versus those with stable ( n = 9; PFS = 9.2 mos, OS = 8.7 mos) and progressive ( n = 2; PFS = 5.4 mos, OS = 12.1 mos) metabolic disease, p = 0.09 and 0.42, respectively. By RECIST, longer PFS and OS was seen for stable ( n = 12, PFS = 8.3 mos, OS = 17.7 mos) versus progressive ( n = 4; PFS = 3.7 mos, OS = 7.5 mos) disease, p = 0.16, 0.02, respectively. OS was not reached, but estimated ≥20 mos, for 4 patients with RECIST SD and PERCIST PMR, compared to OS of 17.7 mos for other patients with RECIST SD. Conclusions: FDG-PET/CT identified more bone metastases and greater numbers of quantitative and qualitative treatment responses in mRCC compared to CT and bone scan. FDG-PET/CT also may identify a sub-group of patients with better outcomes than predicted by standard imaging modalities.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".