Early Reassessment of Total Metabolic Tumor Volume on FDG-PET/CT in Advanced Melanoma Patients Treated with Pembrolizumab Predicts Long-Term Outcome
Bibliographic record
Abstract
PD-1 Immune checkpoint inhibitors, such as Pembrolizumab, can have a durable beneficial therapeutic effect in patients with advanced melanoma. However, not all patients will benefit equally from these therapies, and (potentially life-threatening) immune-related adverse events may occur. In this study, we investigate the value of early response assessment by FDG-PET/CT as a biomarker for predicting survival. We identified all patients with advanced melanoma who were treated with Pembrolizumab in our medical center and underwent a baseline and at least one follow-up FDG-PET/CT. The total metabolic tumor volume (TMTV) was calculated, and the evolution was compared to survival parameters. A total of 77 patients underwent a baseline and at least one follow-up FDG-PET/CT, 36 patients had follow-up imaging within 2-4 months, and 21 patients an FDG-PET/CT 5-6 months after baseline. When the TMTV evolution was categorized into two subgroups (stable/decrease versus increase), an association was found between stability or decrease in TMTV and better PFS and OS. A similar trend, however non-significant, was observed at 5-6 months. The evolution in TMTV as assessed by FDG-PET/CT 2-4 months after treatment initiation is associated with long-term outcomes in patients with advanced melanoma treated with Pembrolizumab.
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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) | 0.000 | 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 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".