Role of Magnetic Resonance Imaging in the Management of Patients With Multiple Myeloma: A Consensus Statement
Bibliographic record
Abstract
PURPOSE: The aim of International Myeloma Working Group was to develop practical recommendations for the use of magnetic resonance imaging (MRI) in multiple myeloma (MM). METHODS: An interdisciplinary panel of clinical experts on MM and myeloma bone disease developed recommendations for the value of MRI based on data published through March 2014. RECOMMENDATIONS: MRI has high sensitivity for the early detection of marrow infiltration by myeloma cells compared with other radiographic methods. Thus, MRI detects bone involvement in patients with myeloma much earlier than the myeloma-related bone destruction, with no radiation exposure. It is the gold standard for the imaging of axial skeleton, for the evaluation of painful lesions, and for distinguishing benign versus malignant osteoporotic vertebral fractures. MRI has the ability to detect spinal cord or nerve compression and presence of soft tissue masses, and it is recommended for the workup of solitary bone plasmacytoma. Regarding smoldering or asymptomatic myeloma, all patients should undergo whole-body MRI (WB-MRI; or spine and pelvic MRI if WB-MRI is not available), and if they have > one focal lesion of a diameter > 5 mm, they should be considered to have symptomatic disease that requires therapy. In cases of equivocal small lesions, a second MRI should be performed after 3 to 6 months, and if there is progression on MRI, the patient should be treated as having symptomatic myeloma. MRI at diagnosis of symptomatic patients and after treatment (mainly after autologous stem-cell transplantation) provides prognostic information; however, to date, this does not change treatment selection.
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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 | 0.031 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".