ArticleResearch square2025
Advanced Quantification Pipeline Reveals New Spatial and Temporal Tumor Characteristics in Preclinical Multiple Myeloma.
Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Radiological imaging plays an indispensable role in both preclinical and clinical studies of multiple myeloma (MM). However, manual quantification in longitudinal small animal PET/CT is limited by annotator bias, signal artifacts from urinary/fecal excretion, and voxel misalignment due to non-rigid registration. To address these challenges and improve characterization of tumor biology, we developed a semi-automated PET/CT quantification pipeline targeting defined regions of interest (ROIs) within the bone marrow-rich mouse skeleton, achieving sub-organ spatial resolution, including in anatomically complex sites such as the pelvis. We applied this MM-specific preclinical pipeline to analyze tumor distribution in a longitudinal molecular PET study using an immunocompetent mouse model of skeletally disseminated MM. An Attention U-Net was trained to segment the thoracolumbar spine, pelvis and pelvic joints, sacrum, and femurs from 2D CT slices. A custom algorithm masked spillover signal from physiological excretion, and a PCA-based projection was used to map tumor distribution along the skeletal axis. Quantification metrics included mean and maximum standardized uptake values (SUV Results: Tumor burden localized preferentially to skeletal regions near joints. Using precise CT-based alignment (DICE = 0.966 ± 0.005), we detected early disease progression and aggressive phenotypes. A marked increase in tumor uptake was observed by day 18 post-implantation, with significant SUV Conclusions: This pipeline enables reproducible, anatomically precise quantification of region-specific trends in MM progression, including joint-specific lesion tropism and sex-based differences, from longitudinal PET/CT scans. By mitigating common challenges such as excretion artifacts and inconsistent mouse positioning, our approach overcomes limitations of manual analysis and enhances evaluation of tumor biology and treatment response in preclinical models of bone-involved cancers.
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