The deep learning radiomics nomogram for risk stratification in multiple myeloma using automatic whole-body [18F]FDG PET/CT segmentation approach.
Advanced imaging analysis predicts multiple myeloma outcomes without repeated bone marrow biopsies, reducing invasive procedures.
This study develops and validates a deep learning radiomics nomogram integrating automated whole-body FDG PET/CT segmentation features with clinical variables for multiple myeloma risk stratification. The approach demonstrates that automated imaging analysis can predict outcomes non-invasively, potentially reducing the need for serial bone marrow biopsies in myeloma management.
What the study was
- Study design
- Retrospective cohort with multicenter validation
- Population
- Multiple myeloma patients undergoing whole-body FDG PET/CT
- Category
- Diagnostics
- Maturity
- Validated
- Journal
- European journal of nuclear medicine and molecular imaging
Why it surfaced
Cross-topic: AI/imaging (T4) + hematologic malignancy (T1). Automated DL segmentation + radiomics for whole-body PET/CT is a major technical advance. Validated approach in multiple myeloma, a cancer type with high unmet need for better non-invasive staging.
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