A practical approach to PET/MRI: Common abdominal pathology in oncology
Bibliographic record
Abstract
1234 Learning Objectives 1. Review the appearance of common abdominal pathology on MRI. 2. Discuss the added value which MRI may provide to PET/CT in oncology patients. 3. Provide illustrative examples where MRI may serve as a problem-solving tool for non-revealing or confounding PET/CT in oncology patients. Both PET and MRI are important imaging modalities that provide unique and often complementary information of significant clinical value. Hybrid PET/MRI scanners have recently become commercially available for clinical use. Knowledge of MRI, its clinical applications and the appearance of frequently encountered pathology is essential for all imaging physicians who will be involved in reading PET/MRI, whether they are trained in nuclear medicine, radiology or both. This knowledge will allow accurate correlation of PET and MR studies and help guide recommendations for follow-up imaging when appropriate. The aim of this paper is to review the MRI characteristics of commonly encountered abdominal pathology, to discuss the added or complementary value which MRI may provide to PET/CT in oncology patients, and to highlight scenarios where MRI may serve as a problem-solving tool for non-revealing or confounding PET/CT in this patient population.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 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".