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Lemainque T, Pridöhl N, Zhang S, Huppertz M, Post M, Yüksel C, Yoneyama M, Prescher A, Kuhl C, Truhn D, Nebelung S. Time-efficient combined morphologic and quantitative joint MRI: an in situ study of standardized knee cartilage defects in human cadaveric specimens. Eur Radiol Exp 2024; 8:66. [PMID: 38834751 DOI: 10.1186/s41747-024-00462-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 03/27/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Quantitative techniques such as T2 and T1ρ mapping allow evaluating the cartilage and meniscus. We evaluated multi-interleaved X-prepared turbo-spin echo with intuitive relaxometry (MIXTURE) sequences with turbo spin-echo (TSE) contrast and additional parameter maps versus reference TSE sequences in an in situ model of human cartilage defects. METHODS Standardized cartilage defects of 8, 5, and 3 mm in diameter were created in the lateral femora of ten human cadaveric knee specimens (81 ± 10 years old; nine males, one female). MIXTURE sequences providing proton density-weighted fat-saturated images and T2 maps or T1-weighted images and T1ρ maps as well as the corresponding two- and three-dimensional TSE reference sequences were acquired before and after defect creation (3-T scanner; knee coil). Defect delineability, bone texture, and cartilage relaxation times were quantified. Appropriate parametric or non-parametric tests were used. RESULTS Overall, defect delineability and texture features were not significantly different between the MIXTURE and reference sequences (p ≤ 0.47). After defect creation, relaxation times significantly increased in the central femur (T2pre = 51 ± 4 ms [mean ± standard deviation] versus T2post = 56 ± 4 ms; p = 0.002) and all regions combined (T1ρpre = 40 ± 4 ms versus T1ρpost = 43 ± 4 ms; p = 0.004). CONCLUSIONS MIXTURE permitted time-efficient simultaneous morphologic and quantitative joint assessment based on clinical image contrasts. While providing T2 or T1ρ maps in clinically feasible scan time, morphologic image features, i.e., cartilage defects and bone texture, were comparable between MIXTURE and reference sequences. RELEVANCE STATEMENT Equally time-efficient and versatile, the MIXTURE sequence platform combines morphologic imaging using familiar contrasts, excellent image correspondence versus corresponding reference sequences and quantitative mapping information, thereby increasing the diagnostic value beyond mere morphology. KEY POINTS • Combined morphologic and quantitative MIXTURE sequences are based on three-dimensional TSE contrasts. • MIXTURE sequences were studied in an in situ human cartilage defect model. • Morphologic image features, i.e., defect delineabilty and bone texture, were investigated. • Morphologic image features were similar between MIXTURE and reference sequences. • MIXTURE allowed time-efficient simultaneous morphologic and quantitative knee joint assessment.
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Affiliation(s)
- Teresa Lemainque
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany.
| | - Nicola Pridöhl
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
| | - Shuo Zhang
- Philips GmbH Market DACH, Hamburg, Germany
| | - Marc Huppertz
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
| | - Manuel Post
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
| | - Can Yüksel
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
| | | | - Andreas Prescher
- Institute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, 52074, Germany
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
| | - Sven Nebelung
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
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Fan G, Wang D, Li Y, Xu Z, Wang H, Liu H, Liao X. Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography. Diagnostics (Basel) 2023; 14:53. [PMID: 38201362 PMCID: PMC10795799 DOI: 10.3390/diagnostics14010053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/17/2023] [Accepted: 11/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND The accurate preoperative identification of decompression levels is crucial for the success of surgery in patients with multi-level lumbar spinal stenosis (LSS). The objective of this study was to develop machine learning (ML) classifiers that can predict decompression levels using computed tomography myelography (CTM) data from LSS patients. METHODS A total of 1095 lumbar levels from 219 patients were included in this study. The bony spinal canal in CTM images was manually delineated, and radiomic features were extracted. The extracted data were randomly divided into training and testing datasets (8:2). Six feature selection methods combined with 12 ML algorithms were employed, resulting in a total of 72 ML classifiers. The main evaluation indicator for all classifiers was the area under the curve of the receiver operating characteristic (ROC-AUC), with the precision-recall AUC (PR-AUC) serving as the secondary indicator. The prediction outcome of ML classifiers was decompression level or not. RESULTS The embedding linear support vector (embeddingLSVC) was the optimal feature selection method. The feature importance analysis revealed the top 5 important features of the 15 radiomic predictors, which included 2 texture features, 2 first-order intensity features, and 1 shape feature. Except for shape features, these features might be eye-discernible but hardly quantified. The top two ML classifiers were embeddingLSVC combined with support vector machine (EmbeddingLSVC_SVM) and embeddingLSVC combined with gradient boosting (EmbeddingLSVC_GradientBoost). These classifiers achieved ROC-AUCs over 0.90 and PR-AUCs over 0.80 in independent testing among the 72 classifiers. Further comparisons indicated that EmbeddingLSVC_SVM appeared to be the optimal classifier, demonstrating superior discrimination ability, slight advantages in the Brier scores on the calibration curve, and Net benefits on the Decision Curve Analysis. CONCLUSIONS ML successfully extracted valuable and interpretable radiomic features from the spinal canal using CTM images, and accurately predicted decompression levels for LSS patients. The EmbeddingLSVC_SVM classifier has the potential to assist surgical decision making in clinical practice, as it showed high discrimination, advantageous calibration, and competitive utility in selecting decompression levels in LSS patients using canal radiomic features from CTM.
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Affiliation(s)
- Guoxin Fan
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Dongdong Wang
- Department of Orthopaedics, Putuo People’s Hospital, Tongji University, Shanghai 200060, China;
| | - Yufeng Li
- Department of Sports Medicine, Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China;
| | - Zhipeng Xu
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
| | - Hong Wang
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
| | - Huaqing Liu
- Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Guangzhou 510700, China
| | - Xiang Liao
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
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Goller SS, Foreman SC, Rischewski JF, Weißinger J, Dietrich AS, Schinz D, Stahl R, Luitjens J, Siller S, Schmidt VF, Erber B, Ricke J, Liebig T, Kirschke JS, Dieckmeyer M, Gersing AS. Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:4314-4320. [PMID: 37401945 DOI: 10.1007/s00586-023-07838-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/25/2023] [Accepted: 06/20/2023] [Indexed: 07/05/2023]
Abstract
PURPOSE To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs). METHODS A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( https://anduin.bonescreen.de ). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs. RESULTS Skewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64-0.76]; malignant fracture group: 0.59 [0.56-0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs. CONCLUSION Three-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.
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Affiliation(s)
- Sophia S Goller
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
| | - Sarah C Foreman
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jon F Rischewski
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Jürgen Weißinger
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Anna-Sophia Dietrich
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - David Schinz
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Robert Stahl
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Johanna Luitjens
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Sebastian Siller
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
| | - Vanessa F Schmidt
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Bernd Erber
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Thomas Liebig
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University Hospital, University of Bern, Bern, Switzerland
| | - Alexandra S Gersing
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
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Kim MW, Huh JW, Noh YM, Seo HE, Lee DH. Assessing bone mineral density in non-weight bearing regions of the body: a texture analysis approach using abdomen and pelvis computed tomography Hounsfield units-a cross-sectional study. Quant Imaging Med Surg 2023; 13:7484-7493. [PMID: 37969628 PMCID: PMC10644143 DOI: 10.21037/qims-23-512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/29/2023] [Indexed: 11/17/2023]
Abstract
Background Highlighting a gap in comprehending bone microarchitecture's intricacies using dual-energy X-ray absorptiometry (DXA), this study aims to bridge this chasm by analyzing texture in non-weight bearing regions on axial computed tomography (CT) scans. Our goal is to enrich osteoporosis patient management by enhancing bone quality and microarchitecture insights. Methods Conducted at Busan Medical Center from March 1, 2013, to August 30, 2022, 1,320 cases (782 patients) were screened. After applying exclusion criteria, 458 samples (296 patients) underwent bone mineral density (BMD) assessment with both CT and DXA. Regions of interest (ROIs) included spine pedicle's maximum trabecular area, sacrum Zone 1, superior/inferior pubic ramus, and femur's greater/lesser trochanters. Texture features (n=45) were extracted from ROIs using gray-level co-occurrence matrices. A regression model predicted BMD, spotlighting the top five influential texture features. Results Correlation coefficients ranged from 0.709 (lowest for total femur BMD) to 0.804 (highest for femur intertrochanter BMD). Mean squared error (MSE) values were also provided for lumbar and femur BMD/bone mineral content (BMC) metrics. The most influential texture features included contrast_32, correlation_32_v, and three other metrics. Conclusions By melding traditional DXA and CT texture analysis, our approach presents a comprehensive bone health perspective, potentially revolutionizing osteoporosis diagnostics.
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Bodden J, Dieckmeyer M, Sollmann N, Rühling S, Prucker P, Löffler MT, Burian E, Subburaj K, Zimmer C, Kirschke JS, Baum T. 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. Quant Imaging Med Surg 2023; 13:5472-5482. [PMID: 37711780 PMCID: PMC10498219 DOI: 10.21037/qims-23-19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 06/08/2023] [Indexed: 09/16/2023]
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.
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Affiliation(s)
- Jannis Bodden
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Sebastian Rühling
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Philipp Prucker
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Maximilian T. Löffler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Freiburg im Breisgau, Germany
| | - Egon Burian
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Karupppasamy Subburaj
- Department of Mechanical and Production Engineering, Aarhus University, Aarhus, Denmark
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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Bott KN, Matheson BE, Smith ACJ, Tse JJ, Boyd SK, Manske SL. Addressing Challenges of Opportunistic Computed Tomography Bone Mineral Density Analysis. Diagnostics (Basel) 2023; 13:2572. [PMID: 37568935 PMCID: PMC10416827 DOI: 10.3390/diagnostics13152572] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/20/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023] Open
Abstract
Computed tomography (CT) offers advanced biomedical imaging of the body and is broadly utilized for clinical diagnosis. Traditionally, clinical CT scans have not been used for volumetric bone mineral density (vBMD) assessment; however, computational advances can now leverage clinically obtained CT data for the secondary analysis of bone, known as opportunistic CT analysis. Initial applications focused on using clinically acquired CT scans for secondary osteoporosis screening, but opportunistic CT analysis can also be applied to answer research questions related to vBMD changes in response to various disease states. There are several considerations for opportunistic CT analysis, including scan acquisition, contrast enhancement, the internal calibration technique, and bone segmentation, but there remains no consensus on applying these methods. These factors may influence vBMD measures and therefore the robustness of the opportunistic CT analysis. Further research and standardization efforts are needed to establish a consensus and optimize the application of opportunistic CT analysis for accurate and reliable assessment of vBMD in clinical and research settings. This review summarizes the current state of opportunistic CT analysis, highlighting its potential and addressing the associated challenges.
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Affiliation(s)
- Kirsten N. Bott
- Department of Radiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.N.B.); (S.K.B.)
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB T2N 4Z6, Canada
| | - Bryn E. Matheson
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB T2N 4Z6, Canada
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Ainsley C. J. Smith
- Department of Radiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.N.B.); (S.K.B.)
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB T2N 4Z6, Canada
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Justin J. Tse
- Department of Radiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.N.B.); (S.K.B.)
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB T2N 4Z6, Canada
| | - Steven K. Boyd
- Department of Radiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.N.B.); (S.K.B.)
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB T2N 4Z6, Canada
| | - Sarah L. Manske
- Department of Radiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.N.B.); (S.K.B.)
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB T2N 4Z6, Canada
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Goller SS, Rischewski JF, Liebig T, Ricke J, Siller S, Schmidt VF, Stahl R, Kulozik J, Baum T, Kirschke JS, Foreman SC, Gersing AS. Automated Opportunistic Trabecular Volumetric Bone Mineral Density Extraction Outperforms Manual Measurements for the Prediction of Vertebral Fractures in Routine CT. Diagnostics (Basel) 2023; 13:2119. [PMID: 37371014 DOI: 10.3390/diagnostics13122119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/16/2023] [Accepted: 06/18/2023] [Indexed: 06/29/2023] Open
Abstract
Opportunistic osteoporosis screening using multidetector CT-scans (MDCT) and convolutional neural network (CNN)-derived segmentations of the spine to generate volumetric bone mineral density (vBMD) bears the potential to improve incidental osteoporotic vertebral fracture (VF) prediction. However, the performance compared to the established manual opportunistic vBMD measures remains unclear. Hence, we investigated patients with a routine MDCT of the spine who had developed a new osteoporotic incidental VF and frequency matched to patients without incidental VFs as assessed on follow-up MDCT images after 1.5 years. Automated vBMD was generated using CNN-generated segmentation masks and asynchronous calibration. Additionally, manual vBMD was sampled by two radiologists. Automated vBMD measurements in patients with incidental VFs at 1.5-years follow-up (n = 53) were significantly lower compared to patients without incidental VFs (n = 104) (83.6 ± 29.4 mg/cm3 vs. 102.1 ± 27.7 mg/cm3, p < 0.001). This comparison was not significant for manually assessed vBMD (99.2 ± 37.6 mg/cm3 vs. 107.9 ± 33.9 mg/cm3, p = 0.30). When adjusting for age and sex, both automated and manual vBMD measurements were significantly associated with incidental VFs at 1.5-year follow-up, however, the associations were stronger for automated measurements (β = -0.32; 95% confidence interval (CI): -20.10, 4.35; p < 0.001) compared to manual measurements (β = -0.15; 95% CI: -11.16, 5.16; p < 0.03). In conclusion, automated opportunistic measurements are feasible and can be useful for bone mineral density assessment in clinical routine.
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Affiliation(s)
- Sophia S Goller
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Jon F Rischewski
- Institute for Diagnostic and Interventional Neuroradiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Thomas Liebig
- Institute for Diagnostic and Interventional Neuroradiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Sebastian Siller
- Department of Neurosurgery, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Vanessa F Schmidt
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Robert Stahl
- Institute for Diagnostic and Interventional Neuroradiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Julian Kulozik
- Institute of Micro Technology and Medical Device Technology (MIMED), Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Sarah C Foreman
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Alexandra S Gersing
- Institute for Diagnostic and Interventional Neuroradiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
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Application of preoperative CT texture analysis in papillary gastric adenocarcinoma. BMC Cancer 2022; 22:1161. [DOI: 10.1186/s12885-022-10261-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 10/31/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
This study aimed to analyze the ability of computed tomography (CT) texture analysis to discriminate papillary gastric adenocarcinoma (PGC) and to explore the diagnostic efficacy of multivariate models integrating clinical information and CT texture parameters for discriminating PGCs.
Methods
This retrospective study included 20 patients with PGC and 80 patients with tubular adenocarcinoma (TAC). The clinical data and CT texture parameters based on the arterial phase (AP) and venous phase (VP) of all patients were collected and analyzed. Two CT signatures based on the AP and VP were built with the optimum features selected by the least absolute shrinkage and selection operator method. The performance of CT signatures was tested by regression analysis. Multivariate models based on regression analysis and the support vector machine (SVM) algorithm were established. The diagnostic performance of the established nomogram based on regression analysis was evaluated by receiver operating characteristic curve analysis.
Results
Thirty-two and fifteen CT texture parameters extracted from AP and VP CT images, respectively, differed significantly between PGCs and TACs (all p < 0.05). The diagnostic performance of CT signatures based on the AP and VP achieved AUCs of 0.873 and 0.859 in distinguishing PGCs. Multivariate models that integrated two CT signatures and age based on regression analysis and the SVM algorithm showed favorable performance in preoperatively predicting PGCs (AUC = 0.922 and 0.914, respectively).
Conclusion
CT texture analysis based multivariate models could preoperatively predict PGCs with satisfactory diagnostic efficacy.
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Xu M, Liu S, Li L, Qiao X, Ji C, Tan L, Zhou Z. Development and validation of multivariate models integrating preoperative clinicopathological and radiographic findings to predict HER2 status in gastric cancer. Sci Rep 2022; 12:14177. [PMID: 35986169 PMCID: PMC9391326 DOI: 10.1038/s41598-022-18433-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 08/11/2022] [Indexed: 12/24/2022] Open
Abstract
AbstractThe combination of trastuzumab and chemotherapy is recommended as first-line therapy for patients with human epidermal growth factor receptor 2 (HER2) positive advanced gastric cancers (GCs). Successful trastuzumab-induced targeted therapy should be based on the assessment of HER2 overexpression. This study aimed to evaluate the feasibility of multivariate models based on hematological parameters, endoscopic biopsy, and computed tomography (CT) findings for assessing HER2 overexpression in GC. This retrospective study included 183 patients with GC, and they were divided into primary (n = 137) and validation (n = 46) cohorts at a ratio of 3:1. Hematological parameters, endoscopic biopsy, CT morphological characteristics, and CT value-related and texture parameters of all patients were collected and analyzed. The mean corpuscular hemoglobin concentration value, morphological type, 3 CT value-related parameters, and 22 texture parameters in three contrast-enhanced phases differed significantly between the two groups (all p < 0.05). Multivariate models based on the regression analysis and support vector machine algorithm achieved areas under the curve of 0.818 and 0.879 in the primary cohort, respectively. The combination of hematological parameters, CT morphological characteristics, CT value-related and texture parameters could predict HER2 overexpression in GCs with satisfactory diagnostic efficiency. The decision curve analysis confirmed the clinical utility.
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