ArticlePloS one2020
Whole body MRI in multiple myeloma: Optimising image acquisition and read times.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Influence of imaging method on fat fraction estimation for assessing bone marrow in metastatic prostate cancer.European radiology · 2025Article
- Present and future of whole-body MRI in metastatic disease and myeloma: how and why you will do it.Skeletal radiology · 2024Review
- Test-retest, inter- and intra-rater reproducibility of size measurements of focal bone marrow lesions in MRI in patients with multiple myeloma.The British journal of radiology · 2023Article
- Modern imaging techniques for monitoring patients with multiple myeloma.Medicine and pharmacy reports · 2022Review
- Myeloma Response Assessment and Diagnosis System (MY-RADS): strategies for practice implementation.Skeletal radiology · 2022Review
- Review of diffusion-weighted imaging and dynamic contrast-enhanced MRI for multiple myeloma and its precursors (monoclonal gammopathy of undetermined significance and smouldering myeloma).Skeletal radiology · 2022Review
- Imaging for Plasma Cell Dyscrasias: What, When, and How?Frontiers in oncology · 2022Review
- Pictorial review of whole body MRI in myeloma: emphasis on diffusion-weighted imaging.The British journal of radiology · 2020Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo identify the whole-body MRI (WB-MRI) image type(s) with the highest value for assessment of multiple myeloma, in order to optimise acquisition protocols and read times.
methodsThirty patients with clinically-suspected MM underwent WB-MRI at 3 Tesla. Unenhanced Dixon images [fat-only (FO) and water-only (WO)], post contrast Dixon [fat-only plus contrast (FOC) and water-only plus contrast (WOC)] and diffusion weighted images (DWI) of the pelvis from all 30 patients were randomised and read by three experienced readers. For each image type, each reader identified and labelled all visible myeloma lesions. Each identified lesion was compared with a composite reference standard achieved by review of a complete imaging dataset by a further experienced consultant radiologist to determine truly positive lesions. Lesion count, true positives, sensitivity, and positive predictive value were determined. Time to read each scan set was recorded. Confidence for a diagnosis of myeloma was scored using a Likert scale. Conspicuity of focal lesions was assessed in terms of percent contrast and contrast to noise ratio (CNR).
resultsLesion count, true positives, sensitivity and confidence scores were significantly higher when compared to other image types for DWI (P<0.0001 to 0.003), followed by WOC (significant for sensitivity (P<0.0001 to 0.004), true positives (P = 0.003 to 0.049) and positive predictive value (P< 0.0001 to 0.006)). There was no statistically significant difference in these metrics between FO and FOC. Percent contrast was highest for WOC (P = 0.001 to 0.005) and contrast to noise ratio (CNR) was highest for DWI (P = 0.03 to 0.05). Reading times were fastest for DWI across all observers (P< 0.0001 to 0.014). DISCUSSION: Observers detected more myeloma lesions on DWI images and WOC images when compared to other image types. We suggest that these image types should be read preferentially by radiologists to improve diagnostic accuracy and reporting efficiency.
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