Long-term reproducibility of opportunistically assessed vertebral bone mineral density and texture features in routine clinical multi-detector computed tomography using an automated segmentation framework.

Bodden, Jannis; Dieckmeyer, Michael; Sollmann, Nico; Rühling, Sebastian; Prucker, Philipp; Löffler, Maximilian T; Burian, Egon; Subburaj, Karupppasamy; Zimmer, Claus; Kirschke, Jan S; Baum, Thomas

doi:10.21037/qims-23-19

journal article

Titel:: Long-term reproducibility of opportunistically assessed vertebral bone mineral density and texture features in routine clinical multi-detector computed tomography using an automated segmentation framework.
Dokumenttyp:: Journal Article
Autor(en):: Bodden, Jannis; Dieckmeyer, Michael; Sollmann, Nico; Rühling, Sebastian; Prucker, Philipp; Löffler, Maximilian T; Burian, Egon; Subburaj, Karupppasamy; Zimmer, Claus; Kirschke, Jan S; Baum, Thomas
Abstract:: BACKGROUND: To investigate reproducibility of texture features and volumetric bone mineral density (vBMD) extracted from trabecular bone in the thoracolumbar spine in routine clinical multi-detector computed tomography (MDCT) data in a single scanner environment. METHODS: Patients who underwent two routine clinical thoraco-abdominal MDCT exams at a single scanner with a time interval of 6 to 26 months (n=203, 131 males; time interval mean, 13 months; median, 12 months) were included in this observational study. Exclusion criteria were metabolic and hematological disorders, bone metastases, use of bone-active medications, and history of osteoporotic vertebral fractures (VFs) or prior diagnosis of osteoporosis. A convolutional neural network (CNN)-based framework was used for automated spine labeling and segmentation (T5-L5), asynchronous Hounsfield unit (HU)-to-BMD calibration, and correction for the intravenous contrast medium phase. Vertebral vBMD and six texture features [varianceglobal, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP)] were extracted for mid- (T5-T8) and lower thoracic (T9-T12), and lumbar vertebrae (L1-L5), respectively. Relative annual changes were calculated in texture features and vBMD for each vertebral level and sorted by sex, and changes were checked for statistical significance (P<0.05) using paired t-tests. Root mean square coefficient of variation (RMSCV) and root mean square error (RMSE) were calculated as measures of variability. RESULTS: SRE, LRE, RLN, and RP exhibited substantial reproducibility with RMSCV-values below 2%, for both sexes and at all spine levels, while vBMD was less reproducible (RMSCV =11.9-16.2%). Entropy showed highest variability (RMSCV =4.34-7.69%) due to statistically significant increases [range, mean ± standard deviation: (4.40±5.78)% to (8.36±8.66)%, P<0.001]. RMSCV of varianceglobal ranged from 1.60% to 3.03%. CONCLUSIONS: Opportunistic assessment of texture features in a single scanner environment using the presented CNN-based framework yields substantial reproducibility, outperforming vBMD reproducibility. Lowest scan-rescan variability was found for higher-order texture features. Further studies are warranted to determine, whether microarchitectural changes to the trabecular bone may be assessed through texture features.
Zeitschriftentitel:: Quant Imaging Med Surg
Jahr:: 2023
Band / Volume:: 13
Heft / Issue:: 9
Seitenangaben Beitrag:: 5472-5482
Volltext / DOI:: doi:10.21037/qims-23-19
PubMed:: http://view.ncbi.nlm.nih.gov/pubmed/37711780
Print-ISSN:: 2223-4292
TUM Einrichtung:: Institut für Diagnostische und Interventionelle Radiologie (Prof. Makowski); Professur für Neuroradiologie (Prof. Zimmer)
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