Impact of gadopiclenol on decision making in patients with brain metastases: A post-hoc study.
Bibliographic record
Abstract
e14003 Background: Gadopiclenol (Elucirem, Guerbet, France) is a new high relaxivity macrocyclic gadolinium-based contrast agent (GBCA), recently approved by the FDA and EMA at the dose of 0.05 mmol/kg. The aim of this post-hoc analysis was to evaluate the impact of contrast-enhanced MRI with gadopiclenol at 0.1 mmol/kg on decision making and radiotherapy (RT) treatment planning of brain metastases (BM). Methods: This is a post-hoc analysis of data from a phase IIb study where patients underwent two separate MRI examinations, one with gadopiclenol at 0.025, 0.05, 0.1 or 0.2 mmol/kg and one with gadobenate dimeglumine at 0.1 mmol/kg. MR images of patients who received both GBCAs at 0.1 mmol/kg, with ≥1 BM detected in either of the scans, were subjected to a blinded reader analysis and contouring by two radiation oncology experts. For each patient, treatment plans (stereotactic radiosurgery [SRS] or whole-brain radiotherapy [WBRT]) were determined for both MRIs, with the gross target volume (GTV) indicating the contrast-enhancing aspects of the tumor. Mean GTVs and normal tissue volumes receiving 12 Gy (V12), as well as the Dice similarity coefficient (DSC) were obtained for the paired contours. The Spearman´s rank (ρ) correlation was additionally calculated. Furthermore, three experts blindly evaluated the contrast enhancement of each lesion for contouring purposes and subjectively qualified them as “better”, in detriment of the counterpart, or “equal”. Results: In total, images from 13 adult patients were analyzed. MRI with gadopiclenol depicted additional BM as compared with gadobenate dimeglumine in 7 patients (54%). The treatment plan was changed in 2 patients (15%), from no treatment to SRS and from SRS to WBRT. Gadopiclenol depicted additional BM in these 2 patients (from 0 and 10 BM with gadobenate dimeglumine to 1 and 15 BM with gadopiclenol). The mean GTVs and V12 were comparable between gadopiclenol and gadobenate dimeglumine (p=0.694, p=1.974). The mean DSC was 0.70 (SD: 0.14, ρ0.82). From a total of 36 answers, contrast enhancement was qualified as better with gadopiclenol in 21 (58.3%) evaluations, better with gadobenate dimeglumine in 8 (22.2%) evaluations, while no difference was observed in 7 (19.4%) evaluations. Conclusions: Gadopiclenol at 0.1 mmol/kg improved BM detection and contrast enhancement with potential impact on RT treatment decisions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".