Whole-Body [18F]-FDG-PET/MRI for Oncology: A Consensus Recommendation
Bibliographic record
Abstract
Combined PET/MR imaging (PET/MRI) was proposed for patient management in 2006 with first commercial versions of integrated whole-body systems becoming available as of 2010. PET/MRI followed the prior evolution of hybrid imaging as attested by the successful adoption of combined PET/CT and SPECT/CT since the early 2000 s. Today, around 150 whole-body PET/MRI systems have become operational worldwide. One of the main application fields of PET/MRI is oncologic imaging. Despite the increasing use of PET/MRI, little governance regarding standardized PET/MRI protocols has been provided to date. Standardization and harmonization of imaging protocols is, however, mandatory for efficient on-site patient management and multi-center studies. This document summarizes consensus recommendations on key aspects of patient referral and preparation, PET/MRI workflow and imaging protocols, as well as reporting strategies for whole-body [18F]-FDG-PET/MRI. These recommendations were created by early adopters and key experts in the field of PET, MRI and PET/MRI. This document is intended to provide guidance for the harmonization and standardization of PET/ MRI today and to support wider clinical adoption of this imaging modality for the benefit of patients. CITATION FORMAT:: Umutlu L, Beyer T, Grueneisen JS et al. Whole-Body [18F]-FDG-PET/MRI for Oncology: A Consensus Recommendation. Nuklearmedizin 2019, 58: 1-9.
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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 | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.017 |
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".