Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [ 18 F]FDG PET/CT predicts survival in multiple myeloma.
Christos SachpekidisOlof EnqvistJohannes UlénAnnette Kopp-SchneiderLeyun PanElias K MaiMarina HajiyianniMaximilian MerzMarc S RaabAnna JauchHartmut GoldschmidtLars EdenbrandtAntonia Dimitrakopoulou-StraussPublished in: European journal of nuclear medicine and molecular imaging (2024)
The AI-based, whole-body calculations of BM metabolism via the parameters MTV and TLG not only correlate with the degree of BM plasma cell infiltration, but also predict patient survival in MM. In particular, the parameter MTV, using the liver uptake as reference for BM segmentation, provides solid prognostic information for disease progression. In addition to highlighting the prognostic significance of automated, global volumetric estimation of metabolic tumor burden, these data open up new perspectives towards solving the complex problem of interpreting PET scans in MM with a simple, fast, and robust method that is not affected by operator-dependent interventions.
Keyphrases
- artificial intelligence
- deep learning
- big data
- machine learning
- bone marrow
- multiple myeloma
- computed tomography
- convolutional neural network
- mesenchymal stem cells
- minimally invasive
- free survival
- cell therapy
- single cell
- molecular dynamics
- molecular dynamics simulations
- health information
- positron emission tomography
- high throughput
- healthcare
- case report
- stem cells
- magnetic resonance imaging
- magnetic resonance
- risk factors
- pet imaging