Semi-automated histogram analysis of normal bone marrow using 18F-FDG PET/CT: correlation with clinical indicators

Abstract

Background 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is increasingly applied to the diagnosis of bone marrow failure such as myeloproliferative neoplasm, aplastic anemia, and myelodysplastic syndrome, as well as malignant lymphoma and multiple myeloma. However, few studies have shown a normal FDG uptake pattern. This study aimed to establish a standard of bone marrow FDG uptake by a reproducible quantitative method with fewer steps using deep learning-based organ segmentation. Methods Bone marrow PET images were obtained using segmented whole-spine and pelvic bone marrow cavity CT as mask images using a commercially available imaging workstation that implemented an automatic organ segmentation algorithm based on deep learning. The correlation between clinical indicators and quantitative PET parameters, including histogram features, was evaluated. Results A total of 98 healthy adults were analyzed. The volume of bone marrow PET extracted in men was significantly higher than that in women (p < 0.0001). Univariate and multivariate regression analyses showed that mean of standardized uptake value corrected by lean body mass (SULmean) and entropy in both men and women were inversely correlated with age (all p < 0.0001), and SULmax in women were also inversely correlated with age (p = 0.011). Conclusion A normal FDG uptake pattern was demonstrated by simplified FDG PET/CT bone marrow quantification.

Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review

“…Out of the nine articles and four abstracts included, 6/14 (43%) used CT images [23][24][25][26][27][28], 3/14 (21%) used PET-CT scans [29][30][31], and 5/14 (36%) used MRI as their imaging modality [17,[32][33][34]. A total of 5/14 (36%) studies used WB imaging or imaging from affected body parts, 1/14 (%) focused on the femur bone, 1/14 (7%) focused on the spine and pelvic bones, and 3/14 (%) focused on the spine alone.…”

“…There were instances of other uses of AI in MM imaging. These included the use of bone subtraction maps [27], cumulative CT scores [26], histogram analysis of bone marrow [28,29], and calculation of bone marrow infiltration using deep learning.…”

“…Another related area of research in this field is the determination of normal bone marrow characteristics using imaging. Satoh et al [29] analyzed PET-CT images of 98 healthy individuals who underwent imaging for screening purposes. They used a commercial tool that used a three-dimensional fully automated convolutional neural network to segment bone regions in the spine and pelvis based on CT images.…”