Prognostic Value of [18F]FDG PET/CT in Multiple Myeloma Patients at the Time of Initial Diagnosis
Bibliographic record
Abstract
BACKGROUND: To investigate newly diagnosed multiple myeloma (MM) patients and determine whether a combination of baseline [ 18 F]FDG-PET-derived parameters and clinical parameters would improve patient prognostication. PATIENTS AND METHODS: In this IRB-approved study, patients who underwent [ 18 F]FDG-PET/CT as part of their initial diagnostic workup for MM in our centre between 2018 and 2024 were included. Various [ 18 F]FDG-PET/CT parameters were extracted, including bone marrow, focal lesion, and total body measurements. Also, clinical parameters were gathered. The Cox proportional model was employed to estimate hazard ratios (HRs) for each parameter. A P -value <0.05 was considered statistically significant. RESULTS: A total of 42 patients (mean age =67 y) entered this study. The median follow-up was 24 months. From continuous [ 18 F]FDG-PET/CT-derived parameters, total-body metabolic tumor volume (TMTV), total-body total lesion glycolysis (TTLG), and bone marrow SUVmax were found to be significantly correlated with patient survival. Following dichotomization, TMTV and TTLG lost their statistical significance, while bone marrow SUVmax retained its significance, showing an HR of 8.5 ( P = 0.039). Moreover, the presence of extramedullary disease was the other significant predictor of survival, with an HR of 5.5 ( P = 0.002). Among the continuous clinical parameters, serum free light chain ratio, β2-microglobulin, LDH, and creatinine levels significantly correlated with patient survival. Only serum β2-microglobulin retained its significance following dichotomization, showing an HR of 4.0 ( P = 0.015). CONCLUSIONS: [ 18 F]FDG-PET/CT-derived parameters, particularly high bone marrow SUVmax and the presence of extramedullary disease, as well as their combination with clinical parameters, particularly high serum β2-microglobulin level, have the potential to enhance MM prognostication at the time of baseline staging.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".