Feasibility of artificial intelligence-supported assessment of bone marrow infiltration using dual-energy computed tomography in patients with evidence of monoclonal protein - a retrospective observational study.
Philipp FerversFlorian FerversJonathan KottlorsPhilipp LohneisPhilip Pollman-SchweckhorstHasan ZaytounMiriam RinneburgerDavid MaintzNils Große HokampPublished in: European radiology (2021)
• The retrospective study provides an automated approach for quantification of the non-fatty portion of bone marrow, based on AI-supported spine segmentation and virtual non-calcium dual-energy CT data. • An increasing non-fatty portion of bone marrow is associated with a higher infiltration determined by invasive biopsy after adjusting for bone mineral density as a control variable (p = 0.007, r = 0.46). • The non-fatty portion of bone marrow might support the clinical diagnosis of multiple myeloma when conventional CT images are negative (sensitivity 0.63, specificity 0.71).
Keyphrases
- dual energy
- bone marrow
- computed tomography
- artificial intelligence
- deep learning
- bone mineral density
- image quality
- mesenchymal stem cells
- big data
- multiple myeloma
- machine learning
- positron emission tomography
- postmenopausal women
- contrast enhanced
- magnetic resonance imaging
- body composition
- convolutional neural network
- electronic health record
- fatty acid
- amino acid
- protein protein
- binding protein