Utilizing Standardized Uptake Value (SUVmax) on FDG-PET/CT for the Prediction of Survival of Patients with Soft Tissue Sarcoma
Bibliographic record
Abstract
1572 Objectives This study is performed to determine the relationship between the baseline maximum standardized uptake value (SUVmax) as measured on 18F-FDG PET/CT and overall survival (OS) in patients with soft tissue sarcoma (STS). Methods Fifty patients with newly diagnosed STS who had a pre-treatment PET/CT between May-2006 and Oct-2014 were included. The tumor SUVmax was measured for each primary tumor and correlated with follow-up data. Kaplan-Meier was used to calculate the overall survival (OS). A log rank test was run to determine whether there were differences in the survival distribution at different SUVmax levels: SUVmax less than 5, SUVmax 5-9.9 and SUVmax 10 or more. Results A total of 50 cases with diagnosis of STS were included. The mean age was 59.6 ± 15 years. Thirty-two patients (64%) were alive at the time of the study; twenty (40%) were alive without disease. The survival distributions for the three categories of SUVmax were statistically significantly different, χ2 = 7.815, p 10, only sixty-two percent (62%) were alive at 24 months from diagnosis compared to 84% for cases with SUVmax 5-9.9 and 78% for cases with SUVmax 10 or more was 48.1 months (CI 95% 31.0-65.2). The mean OS was not reached with SUVmax 5-9.9 nor with SUVmax Conclusions High baseline level of metabolic activity using SUVmax appears to be associated with decreased survival of patients with STS. This may impact treatment decision making for individual patients.
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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 | 0.001 | 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".