The SUVmax for 18F-FDG Correlates With Molecular Subtype and Survival of Previously Untreated Metastatic Breast Cancer
Bibliographic record
Abstract
AIM: The objectives of this study were to determine if the baseline SUVmax measured by F-FDG PET/CT correlates with molecular subtype and to explore the impact of baseline SUVmax on the survival of patients with metastatic breast cancer (MBC). METHODS: Patients with MBC were screened with PET/CT from February 2007 until December 2010. Multivariate linear regression analysis was performed to identify independent variable correlation with SUVmax. Prognostic variables identified by univariate analysis, with P < 0.1, were analyzed in the multivariate Cox model. RESULTS: A total of 244 MBC patients were eligible for this study. Multivariate linear regression analysis showed that molecular subtype, visceral metastasis, and number of metastatic organs could be used to predict the logarithmic values of SUVmax (lgSUVmax) for previous untreated MBC patients, whereas for those with 1 or more line previous treatment, the number of metastatic organs was identified as the only independent variable correlating with lgSUVmax. Cox regression analysis indicated that only in patients with previously untreated MBC did baseline SUVmax (continuous variable) act as an independent prognostic factor (hazard ratio = 1.049 for progression-free survival, 1.124 for overall survival). CONCLUSIONS: Baseline SUVmax correlates with molecular subtypes only in previously untreated MBC patients. PET/CT imaging can be used as a potential prognostic tool for patients with newly diagnosed MBC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".