Abstract P6-07-43: The Maximum Standardized Uptake Value of18F-FDG to Prognosticate Prognosis of Hormone-Receptor Positive Metastatic Breast Cancer
Bibliographic record
Abstract
Abstract Objectives: The purpose of this prospective study was to determine whether baseline maximum standard uptake value (SUVmax) in metastatic breast cancer (MBC) patients correlates with validated prognostic markers and their luminal subtypes. Whether baseline SUVmax could be used as a noninvasive indicator to differentiate luminal A from luminal B subtypes and whether it could help predict the survival of these patients were also investigated. Methods: MBC patients with luminal subtypes were screened with PET/CT from February 2007 until December 2010. Patients were included if they had previously undergone radical surgery, had at least one measurable metastatic lesion with abnormal fluorine-18-fluorodeoxyglucose (18F-FDG) uptake, and had not received treatment in the metastatic setting. Baseline information of eligible patients was collected and a prospective follow-up was conducted. SUVmax values were independently reviewed by two experienced nuclear medicine radiologists. The impact of different clinical parameters including luminal types on baseline SUVmax, was evaluated by Mann-Whitney U test (between 2 groups) or Kruskal-Wallis test (≥3 groups). Receiver operator characteristic (ROC) curves were used to identify potential SUV cutoffs predictive of different luminal subtype. Kaplan-Meier method was applied for survival analysis. Prognostic variables identified by univariate analysis (with P < 0.1) were analyzed in the multivariate Cox model. Results: In total, 305 MBC patients with luminal subtypes were screened and 134 were eligible for this study including 75 (56.0%) luminal A type and 59 (44.0%) luminal B type. The median age was 52 years (range, 28–74 years) and median follow-up time was 26.6 months (range, 14.1–51.2 months). Baseline SUVmax was significantly related to the number of metastatic organs (p = 0.002) and existence of visceral metastasis (p = 0.009) on univariate analysis but could not effectively differentiate patients with luminal A subtype from those with luminal B subtype (area under ROC curve was 0.516 ± 0.052). Most importantly, although luminal subtypes diagnosed according to resected specimen from the first operation could predict the relapse-free interval (RFI) (P < 0.001), they could not predict progression-free survival (PFS) (p = 0.550) and overall survival (OS) (p = 0.233) after relapse or metastasis. However, baseline SUVmax did predict PFS and OS (p = 0.002 and p = 0.009, respectively). Baseline SUVmax, RFI, and the number of metastatic organs were three independent prognostic factors for PFS, while the significant predictors of OS in the COX regression model were baseline SUVmax and RFI. Using the tertile with the lowest SUVmax as the reference group, patients in the highest tertile of SUVmax had the shortest PFS (hazard ratio, 2.063; 95% confidence interval, 1.234–3.447) and OS (hazard ratio, 3.543; 95% confidence interval, 1.661–7.554). Conclusions: Baseline SUVmax of MBC did not correlate with molecular subtypes of the primary tumor. In discriminating prognosis of luminal type breast cancer, molecular subtype is a valuable prognostic factor at primary diagnosis, but it is SUVmax, rather than molecular subtype, that is a potential surrogate marker for survival with metastatic disease. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P6-07-43.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
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".