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Gassert FT, Kufner A, Renz M, Gassert FG, Bollwein C, Kronthaler S, Feuerriegel GC, Kirschke JS, Ganter C, Makowski MR, Braun C, Schwaiger BJ, Woertler K, Karampinos DC, Gersing AS. Comparing CT-Like Images Based on Ultra-Short Echo Time and Gradient Echo T1-Weighted MRI Sequences for the Assessment of Vertebral Disorders Using Histology and True CT as the Reference Standard. J Magn Reson Imaging 2024; 59:1542-1552. [PMID: 37501387 DOI: 10.1002/jmri.28927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/14/2023] [Accepted: 07/14/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND Several magnetic resonance (MR) techniques have been suggested for radiation-free imaging of osseous structures. PURPOSE To compare the diagnostic value of ultra-short echo time and gradient echo T1-weighted MRI for the assessment of vertebral pathologies using histology and computed tomography (CT) as the reference standard. STUDY TYPE Prospective. SUBJECTS Fifty-nine lumbar vertebral bodies harvested from 20 human cadavers (donor age 73 ± 13 years; 9 male). FIELD STRENGTH/SEQUENCE Ultra-short echo time sequence optimized for both bone (UTEb) and cartilage (UTEc) imaging and 3D T1-weighted gradient-echo sequence (T1GRE) at 3 T; susceptibility-weighted imaging (SWI) gradient echo sequence at 1.5 T. CT was performed on a dual-layer dual-energy CT scanner using a routine clinical protocol. ASSESSMENT Histopathology and conventional CT were acquired as standard of reference. Semi-quantitative and quantitative morphological features of degenerative changes of the spines were evaluated by four radiologists independently on CT and MR images independently and blinded to all other information. Features assessed were osteophytes, endplate sclerosis, visualization of cartilaginous endplate, facet joint degeneration, presence of Schmorl's nodes, and vertebral dimensions. Vertebral disorders were assessed by a pathologist on histology. STATISTICAL TESTS Agreement between T1GRE, SWI, UTEc, and UTEb sequences and CT imaging and histology as standard of reference were assessed using Fleiss' κ and intra-class correlation coefficients, respectively. RESULTS For the morphological assessment of osteophytes and endplate sclerosis, the overall agreement between SWI, T1GRE, UTEb, and UTEc with the reference standard (histology combined with CT) was moderate to almost perfect for all readers (osteophytes: SWI, κ range: 0.68-0.76; T1GRE: 0.92-1.00; UTEb: 0.92-1.00; UTEc: 0.77-0.85; sclerosis: SWI, κ range: 0.60-0.70; T1GRE: 0.77-0.82; UTEb: 0.81-0.92; UTEc: 0.61-0.71). For the visualization of the cartilaginous endplate, UTEc showed the overall best agreement with the reference standard (histology) for all readers (κ range: 0.85-0.93). DATA CONCLUSIONS Morphological assessment of vertebral pathologies was feasible and accurate using the MR-based bone imaging sequences compared to CT and histopathology. T1GRE showed the overall best performance for osseous changes and UTEc for the visualization of the cartilaginous endplate. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Florian T Gassert
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Alexander Kufner
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Martin Renz
- Department of Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Felix G Gassert
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christine Bollwein
- Department of Pathology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Sophia Kronthaler
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Georg C Feuerriegel
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Carl Ganter
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Marcus R Makowski
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christian Braun
- Institute of Forensic Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Benedikt J Schwaiger
- Department of Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Klaus Woertler
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
- Musculoskeletal Radiology Section, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Dimitrios C Karampinos
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Alexandra S Gersing
- Department of Neuroradiology, University Hospital of Munich, LMU Munich, Munich, Germany
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Gassert FG, Kranz J, Gassert FT, Schwaiger BJ, Bogner C, Makowski MR, Glanz L, Stelter J, Baum T, Braren R, Karampinos DC, Gersing AS. Longitudinal MR-based proton-density fat fraction (PDFF) and T2* for the assessment of associations between bone marrow changes and myelotoxic chemotherapy. Eur Radiol 2024; 34:2437-2444. [PMID: 37691079 PMCID: PMC10957695 DOI: 10.1007/s00330-023-10189-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 04/14/2023] [Accepted: 07/07/2023] [Indexed: 09/12/2023]
Abstract
OBJECTIVES MR imaging-based proton density fat fraction (PDFF) and T2* imaging has shown to be useful for the evaluation of degenerative changes in the spine. Therefore, the aim of this study was to investigate the influence of myelotoxic chemotherapy on the PDFF and T2* of the thoracolumbar spine in comparison to changes in bone mineral density (BMD). METHODS In this study, 19 patients were included who had received myelotoxic chemotherapy (MC) and had received a MR imaging scan of the thoracolumbar vertebrates before and after the MC. Every patient was matched for age, sex, and time between the MRI scans to two controls without MC. All patients underwent 3-T MR imaging including the thoracolumbar spine comprising chemical shift encoding-based water-fat imaging to extract PDFF and T2* maps. Moreover, trabecular BMD values were determined before and after chemotherapy. Longitudinal changes in PDFF and T2* were evaluated and compared to changes in BMD. RESULTS Absolute mean differences of PDFF values between scans before and after MC were at 8.7% (p = 0.01) and at -0.5% (p = 0.57) in the control group, resulting in significantly higher changes in PDFF in patients with MC (p = 0.008). BMD and T2* values neither showed significant changes in patients with nor in those without myelotoxic chemotherapy (p = 0.15 and p = 0.47). There was an inverse, yet non-significant correlation between changes in PDFF and BMD found in patients with myelotoxic chemotherapy (r = -0.41, p = 0.12). CONCLUSION Therefore, PDFF could be a useful non-invasive biomarker in order to detect changes in the bone marrow in patients receiving myelotoxic therapy. CLINICAL RELEVANCE STATEMENT Using PDFF as a non-invasive biomarker for early bone marrow changes in oncologic patients undergoing myelotoxic treatment may help enable more targeted countermeasures at commencing states of bone marrow degradation and reduce risks of possible fragility fractures. KEY POINTS Quantifying changes in bone marrow fat fraction, as well as T2* caused by myelotoxic pharmaceuticals using proton density fat fraction, is feasible. Proton density fat fraction could potentially be established as a non-invasive biomarker for early bone marrow changes in oncologic patients undergoing myelotoxic treatment.
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Affiliation(s)
- Felix G Gassert
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany.
| | - Julia Kranz
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
| | - Florian T Gassert
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
| | - Benedikt J Schwaiger
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
- Department of Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christian Bogner
- Department of Oncology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Marcus R Makowski
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
| | - Leander Glanz
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
| | - Jonathan Stelter
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
| | - Thomas Baum
- Department of Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Rickmer Braren
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
| | - Dimitrios C Karampinos
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
| | - Alexandra S Gersing
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany
- Department of Neuroradiology, University Hospital of Munich, Ludwig-Maximilians University Munich, Munich, Germany
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Graf M, Gassert FG, Marka AW, Gassert FT, Ziegelmayer S, Makowski M, Kallmayer M, Nadjiri J. Spectral computed tomography angiography using a gadolinium-based contrast agent for imaging of pathologies of the aorta. Int J Cardiovasc Imaging 2024:10.1007/s10554-024-03074-2. [PMID: 38421538 DOI: 10.1007/s10554-024-03074-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 02/22/2024] [Indexed: 03/02/2024]
Abstract
OBJECTIVES Especially patients with aortic aneurysms and multiple computed tomography angiographies (CTA) might show medical conditions which oppose the use of iodine-based contrast agents. CTA using monoenergetic reconstructions from dual layer CT and gadolinium (Gd-)based contrast agents might be a feasible alternative in these patients. Therefore, the purpose of this study was to evaluate the feasibility of clinical spectral CTA with a Gd-based contrast agent in patients with aortic aneurysms. METHODS Twenty-one consecutive scans in 15 patients with and without endovascular aneurysm repair showing contraindications for iodine-based contrast agents were examined using clinical routine doses (0.2 mmol/kg) of Gd-based contrast agent with spectral CT. Monoenergetic reconstructions of the spectral data set were computed. RESULTS There was a significant increase in the intravascular attenuation of the aorta between pre- and post-contrast images for the MonoE40 images in the thoracic and the abdominal aorta (p < 0.001 for both). Additionally, the ratio between pre- and post-contrast images was significantly higher in the MonoE40 images as compared to the conventional images with a factor of 6.5 ± 4.5 vs. 2.4 ± 0.5 in the thoracic aorta (p = 0.003) and 4.1 ± 1.8 vs. 1.9 ± 0.5 in the abdominal aorta (p < 0.001). CONCLUSIONS To conclude, our study showed that Gd-CTA is a valid and reliable alternative for diagnostic imaging of the aorta for clinical applications. Monoenergetic reconstructions of computed tomography angiographies using gadolinium based contrast agents may be a useful alternative in patients with aortic aneurysms and contraindications for iodine based contrast agents.
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Affiliation(s)
- Markus Graf
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
| | - Felix G Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Alexander W Marka
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Florian T Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Sebastian Ziegelmayer
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Marcus Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Michael Kallmayer
- Department of Vascular and Endovascular Surgery, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Jonathan Nadjiri
- Department of Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
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Marka AW, Luitjens J, Gassert FT, Steinhelfer L, Burian E, Rübenthaler J, Schwarze V, Froelich MF, Makowski MR, Gassert FG. Artificial intelligence support in MR imaging of incidental renal masses: an early health technology assessment. Eur Radiol 2024:10.1007/s00330-024-10643-5. [PMID: 38388721 DOI: 10.1007/s00330-024-10643-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 02/24/2024]
Abstract
OBJECTIVE This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)-assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up. MATERIALS AND METHODS For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis. RESULTS Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI. CONCLUSIONS This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future. CLINICAL RELEVANCE STATEMENT These results hint at AI's the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics. KEY POINTS • This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions. • MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions. • The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.
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Affiliation(s)
- Alexander W Marka
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Institut für diagnostische und interventionelle Radiologie, Ismaninger Str. 22, 81675, Munich, Germany.
| | - Johanna Luitjens
- Department of Radiology, Klinikum Großhadern, Ludwig-Maximilians-Universität, Marchioninistraße 15, 81377, Munich, Germany
| | - Florian T Gassert
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Institut für diagnostische und interventionelle Radiologie, Ismaninger Str. 22, 81675, Munich, Germany
| | - Lisa Steinhelfer
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Institut für diagnostische und interventionelle Radiologie, Ismaninger Str. 22, 81675, Munich, Germany
| | - Egon Burian
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Institut für diagnostische und interventionelle Radiologie, Ismaninger Str. 22, 81675, Munich, Germany
| | - Johannes Rübenthaler
- Department of Radiology, Klinikum Großhadern, Ludwig-Maximilians-Universität, Marchioninistraße 15, 81377, Munich, Germany
| | - Vincent Schwarze
- Department of Radiology, Klinikum Großhadern, Ludwig-Maximilians-Universität, Marchioninistraße 15, 81377, Munich, Germany
| | - Matthias F Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Institut für diagnostische und interventionelle Radiologie, Ismaninger Str. 22, 81675, Munich, Germany
| | - Felix G Gassert
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Institut für diagnostische und interventionelle Radiologie, Ismaninger Str. 22, 81675, Munich, Germany
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Paciorek AM, von Schacky CE, Foreman SC, Gassert FG, Gassert FT, Kirschke JS, Laugwitz KL, Geith T, Hadamitzky M, Nadjiri J. Automated assessment of cardiac pathologies on cardiac MRI using T1-mapping and late gadolinium phase sensitive inversion recovery sequences with deep learning. BMC Med Imaging 2024; 24:43. [PMID: 38350900 PMCID: PMC10865672 DOI: 10.1186/s12880-024-01217-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND A deep learning (DL) model that automatically detects cardiac pathologies on cardiac MRI may help streamline the diagnostic workflow. To develop a DL model to detect cardiac pathologies on cardiac MRI T1-mapping and late gadolinium phase sensitive inversion recovery (PSIR) sequences were used. METHODS Subjects in this study were either diagnosed with cardiac pathology (n = 137) including acute and chronic myocardial infarction, myocarditis, dilated cardiomyopathy, and hypertrophic cardiomyopathy or classified as normal (n = 63). Cardiac MR imaging included T1-mapping and PSIR sequences. Subjects were split 65/15/20% for training, validation, and hold-out testing. The DL models were based on an ImageNet pretrained DenseNet-161 and implemented using PyTorch and fastai. Data augmentation with random rotation and mixup was applied. Categorical cross entropy was used as the loss function with a cyclic learning rate (1e-3). DL models for both sequences were developed separately using similar training parameters. The final model was chosen based on its performance on the validation set. Gradient-weighted class activation maps (Grad-CAMs) visualized the decision-making process of the DL model. RESULTS The DL model achieved a sensitivity, specificity, and accuracy of 100%, 38%, and 88% on PSIR images and 78%, 54%, and 70% on T1-mapping images. Grad-CAMs demonstrated that the DL model focused its attention on myocardium and cardiac pathology when evaluating MR images. CONCLUSIONS The developed DL models were able to reliably detect cardiac pathologies on cardiac MR images. The diagnostic performance of T1 mapping alone is particularly of note since it does not require a contrast agent and can be acquired quickly.
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Affiliation(s)
- Aleksandra M Paciorek
- Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
| | - Claudio E von Schacky
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Sarah C Foreman
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Felix G Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Florian T Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Jan S Kirschke
- TUM-Neuroimaging Center, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Karl-Ludwig Laugwitz
- Department of Medicine I, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Tobias Geith
- Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Martin Hadamitzky
- Department of Radiology, German Heart Center Munich, Technical University of Munich, Lazarettstraße 36, 80636, Munich, Germany
| | - Jonathan Nadjiri
- Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
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Luitjens J, Gassert FG, Patwardhan V, Bhattacharjee R, Joseph GB, Zhang AL, Souza RB, Majumdar S, Link TM. Is hip capsule morphology associated with hip pain in patients without another structural correlate? Eur Radiol 2024:10.1007/s00330-023-10307-w. [PMID: 38170264 DOI: 10.1007/s00330-023-10307-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 08/15/2023] [Accepted: 08/21/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVE The goals of this study were (i) to assess the association between hip capsule morphology and pain in patients without any other MRI abnormalities that would correlate with pain and (ii) to investigate whether hip capsule morphology in hip pain patients is different from that of controls. METHODS In this study, 76 adults with hip pain who did not show any structural abnormalities on MRI and 46 asymptomatic volunteers were included. Manual segmentation of the anterior and posterior hip capsules was performed. Total and mean anterior hip capsule area, posterior capsule area, anterior-to-posterior capsule area ratio, and medial-to-lateral area ratio in the anterior capsule were quantified. Differences between the pain and control groups were evaluated using logistic regression models. RESULTS Patients with hip pain showed a significantly lower anterior-to-posterior area ratio as compared with the control group (p = 0.002). The pain group's posterior hip capsule area was significantly larger than that of controls (p = 0.001). Additionally, the ratio between the medial and lateral sections of the anterior capsule was significantly lower in the pain group (p = 0.004). CONCLUSIONS Patients with hip pain are more likely to have thicker posterior capsules and a lower ratio of the anterior-to-posterior capsule area and thinner medial anterior capsules with a lower ratio of the medial-to-lateral anterior hip capsule compartment, compared with controls. CLINICAL RELEVANCE STATEMENT During MRI evaluations of patients with hip pain, morphology of the hip capsule should be assessed. This study aims to be a foundation for future analyses to identify thresholds distinguishing normal from abnormal hip capsule measurements. KEY POINTS • Even with modern image modalities such as MRI, one of the biggest challenges in handling hip pain patients is finding a structural link for their pain. • Hip capsule morphologies that correlated with hip pain showed a larger posterior hip capsule area and a lower anterior-to-posterior capsule area ratio, as well as a smaller medial anterior capsule area with a lower medial-to-lateral anterior hip capsule ratio. • The hip capsule morphology is correlated with hip pain in patients who do not show other morphology abnormalities in MRI and should get more attention in clinical practice.
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Affiliation(s)
- Johanna Luitjens
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, USA.
- Department of Radiology, University Hospital, LMU, Munich, Germany.
| | - Felix G Gassert
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, USA
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Vasant Patwardhan
- John A. Burns School of Medicine, University of Hawai'i, Honolulu, USA
| | - Rupsa Bhattacharjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, USA
| | - Gabby B Joseph
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, USA
| | - Alan L Zhang
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, USA
| | - Richard B Souza
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, USA
| | - Thomas M Link
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, USA
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Tolpadi AA, Luitjens J, Gassert FG, Li X, Link TM, Majumdar S, Pedoia V. Synthetic Inflammation Imaging with PatchGAN Deep Learning Networks. Bioengineering (Basel) 2023; 10:bioengineering10050516. [PMID: 37237586 DOI: 10.3390/bioengineering10050516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 04/14/2023] [Accepted: 04/22/2023] [Indexed: 05/28/2023] Open
Abstract
Background: Gadolinium (Gd)-enhanced Magnetic Resonance Imaging (MRI) is crucial in several applications, including oncology, cardiac imaging, and musculoskeletal inflammatory imaging. One use case is rheumatoid arthritis (RA), a widespread autoimmune condition for which Gd MRI is crucial in imaging synovial joint inflammation, but Gd administration has well-documented safety concerns. As such, algorithms that could synthetically generate post-contrast peripheral joint MR images from non-contrast MR sequences would have immense clinical utility. Moreover, while such algorithms have been investigated for other anatomies, they are largely unexplored for musculoskeletal applications such as RA, and efforts to understand trained models and improve trust in their predictions have been limited in medical imaging. Methods: A dataset of 27 RA patients was used to train algorithms that synthetically generated post-Gd IDEAL wrist coronal T1-weighted scans from pre-contrast scans. UNets and PatchGANs were trained, leveraging an anomaly-weighted L1 loss and global generative adversarial network (GAN) loss for the PatchGAN. Occlusion and uncertainty maps were also generated to understand model performance. Results: UNet synthetic post-contrast images exhibited stronger normalized root mean square error (nRMSE) than PatchGAN in full volumes and the wrist, but PatchGAN outperformed UNet in synovial joints (UNet nRMSEs: volume = 6.29 ± 0.88, wrist = 4.36 ± 0.60, synovial = 26.18 ± 7.45; PatchGAN nRMSEs: volume = 6.72 ± 0.81, wrist = 6.07 ± 1.22, synovial = 23.14 ± 7.37; n = 7). Occlusion maps showed that synovial joints made substantial contributions to PatchGAN and UNet predictions, while uncertainty maps showed that PatchGAN predictions were more confident within those joints. Conclusions: Both pipelines showed promising performance in synthesizing post-contrast images, but PatchGAN performance was stronger and more confident within synovial joints, where an algorithm like this would have maximal clinical utility. Image synthesis approaches are therefore promising for RA and synthetic inflammatory imaging.
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Affiliation(s)
- Aniket A Tolpadi
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Johanna Luitjens
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Radiology, Klinikum Großhadern, Ludwig-Maximilians-Universität, 81377 Munich, Germany
| | - Felix G Gassert
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Xiaojuan Li
- Department of Biomedical Imaging, Cleveland Clinic, Cleveland, OH 44106, USA
| | - Thomas M Link
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
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8
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Tolpadi AA, Bharadwaj U, Gao KT, Bhattacharjee R, Gassert FG, Luitjens J, Giesler P, Morshuis JN, Fischer P, Hein M, Baumgartner CF, Razumov A, Dylov D, van Lohuizen Q, Fransen SJ, Zhang X, Tibrewala R, de Moura HL, Liu K, Zibetti MVW, Regatte R, Majumdar S, Pedoia V. K2S Challenge: From Undersampled K-Space to Automatic Segmentation. Bioengineering (Basel) 2023; 10:bioengineering10020267. [PMID: 36829761 PMCID: PMC9952400 DOI: 10.3390/bioengineering10020267] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/01/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could be improved by treating both as end-to-end, we hosted the K2S challenge, in which challenge participants segmented knee bones and cartilage from 8× undersampled k-space. We curated the 300-patient K2S dataset of multicoil raw k-space and radiologist quality-checked segmentations. 87 teams registered for the challenge and there were 12 submissions, varying in methodologies from serial reconstruction and segmentation to end-to-end networks to another that eschewed a reconstruction algorithm altogether. Four teams produced strong submissions, with the winner having a weighted Dice Similarity Coefficient of 0.910 ± 0.021 across knee bones and cartilage. Interestingly, there was no correlation between reconstruction and segmentation metrics. Further analysis showed the top four submissions were suitable for downstream biomarker analysis, largely preserving cartilage thicknesses and key bone shape features with respect to ground truth. K2S thus showed the value in considering reconstruction and image analysis as end-to-end tasks, as this leaves room for optimization while more realistically reflecting the long-term use case of tools being developed by the MR community.
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Affiliation(s)
- Aniket A. Tolpadi
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Correspondence:
| | - Upasana Bharadwaj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Kenneth T. Gao
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Rupsa Bhattacharjee
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Felix G. Gassert
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Johanna Luitjens
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Radiology, Klinikum Großhadern, Ludwig-Maximilians-Universität, 81377 Munich, Germany
| | - Paula Giesler
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jan Nikolas Morshuis
- Cluster of Excellence Machine Learning, University of Tübingen, 72076 Tübingen, Germany
| | - Paul Fischer
- Cluster of Excellence Machine Learning, University of Tübingen, 72076 Tübingen, Germany
| | - Matthias Hein
- Cluster of Excellence Machine Learning, University of Tübingen, 72076 Tübingen, Germany
| | | | - Artem Razumov
- Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Dmitry Dylov
- Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Quintin van Lohuizen
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Stefan J. Fransen
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Xiaoxia Zhang
- Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Radhika Tibrewala
- Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Hector Lise de Moura
- Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Kangning Liu
- Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Marcelo V. W. Zibetti
- Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Ravinder Regatte
- Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
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9
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Gassert FG, Breden S, Neumann J, Gassert FT, Bollwein C, Knebel C, Lenze U, von Eisenhart-Rothe R, Mogler C, Makowski MR, Peeken JC, Wörtler K, Gersing AS. Differentiating Enchondromas and Atypical Cartilaginous Tumors in Long Bones with Computed Tomography and Magnetic Resonance Imaging. Diagnostics (Basel) 2022; 12:diagnostics12092186. [PMID: 36140587 PMCID: PMC9497620 DOI: 10.3390/diagnostics12092186] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/27/2022] [Accepted: 09/06/2022] [Indexed: 11/16/2022] Open
Abstract
The differentiation between the atypical cartilaginous tumor (ACT) and the enchondromas is crucial as ACTs require a curettage and clinical as well as imaging follow-ups, whereas in the majority of cases enchondromas require neither a treatment nor follow-ups. Differentiating enchondromas from ACTs radiologically remains challenging. Therefore, this study evaluated imaging criteria in a combination of computed tomography (CT) and magnetic resonance (MR) imaging for the differentiation between enchondromas and ACTs in long bones. A total of 82 patients who presented consecutively at our institution with either an ACT (23, age 52.7 ±18.8 years; 14 women) or an enchondroma (59, age 46.0 ± 11.1 years; 37 women) over a period of 10 years, who had undergone preoperative MR and CT imaging and subsequent biopsy or/and surgical removal, were included in this study. A histopathological diagnosis was available in all cases. Two experienced radiologists evaluated several imaging criteria on CT and MR images. Likelihood of an ACT was significantly increased if either edema within the bone (p = 0.049), within the adjacent soft tissue (p = 0.006) or continuous growth pattern (p = 0.077) were present or if the fat entrapment (p = 0.027) was absent on MR images. Analyzing imaging features on CT, the likelihood of the diagnosis of an ACT was significantly increased if endosteal scalloping >2/3 (p < 0.001), cortical penetration (p < 0.001) and expansion of bone (p = 0.002) were present and if matrix calcifications were observed in less than 1/3 of the tumor (p = 0.013). All other imaging criteria evaluated showed no significant influence on likelihood of ACT or enchondroma (p > 0.05). In conclusion, both CT and MR imaging show suggestive signs which can help to adequately differentiate enchondromas from ACTs in long bones and therefore can improve diagnostics and consequently patient management. Nevertheless, these features are rare and a combination of CT and MR imaging features did not improve the diagnostic performance substantially.
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Affiliation(s)
- Felix G. Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
- Musculoskeletal Quantitative Imaging Research Group, Department of Radiology & Biomedical Imaging, University of California San Francisco, 185 Berry St., Suite 350, San Francisco, CA 94107, USA
- Correspondence: ; Tel.: +49-89-4140-8797
| | - Sebastian Breden
- Department of Orthopaedic Surgery, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Jan Neumann
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Florian T. Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Christine Bollwein
- Department of Pathology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Carolin Knebel
- Department of Orthopaedic Surgery, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Ulrich Lenze
- Department of Orthopaedic Surgery, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Rüdiger von Eisenhart-Rothe
- Department of Orthopaedic Surgery, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Carolin Mogler
- Department of Pathology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Marcus R. Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Jan C. Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
- Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, 85764 Munich, Germany
| | - Klaus Wörtler
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Alexandra S. Gersing
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany
- Department of Neuroradiology, University Hospital of Munich (LMU), Marchioninistrasse 15, 81377 Munich, Germany
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10
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Nadjiri J, Koppara T, Kafka A, Weis F, Rasper M, Gassert FG, von Schacky CE, Pfeiffer D, Laugwitz KL, Makowski MR, Ibrahim T. Coronary plaque characterization assessed by delayed enhancement dual-layer spectral CT angiography and optical coherence tomography. Int J Cardiovasc Imaging 2022; 38:2491-2500. [DOI: 10.1007/s10554-022-02638-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 04/28/2022] [Indexed: 11/29/2022]
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11
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Burian E, Palla B, Callahan N, Pyka T, Wolff C, von Schacky CE, Schmid A, Froelich MF, Rübenthaler J, Makowski MR, Gassert FG. Comparison of CT, MRI, and F-18 FDG PET/CT for initial N-staging of oral squamous cell carcinoma: a cost-effectiveness analysis. Eur J Nucl Med Mol Imaging 2022; 49:3870-3877. [PMID: 35606526 PMCID: PMC9399011 DOI: 10.1007/s00259-022-05843-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 05/12/2022] [Indexed: 11/30/2022]
Abstract
Background and purpose Treatment of oral squamous cell carcinoma (OSCC) is based on clinical exam, biopsy, and a precise imaging-based TNM-evaluation. A high sensitivity and specificity for magnetic resonance imaging (MRI) and F-18 FDG PET/CT are reported for N-staging. Nevertheless, staging of oral squamous cell carcinoma is most often based on computed tomography (CT) scans. This study aims to evaluate cost-effectiveness of MRI and PET/CT compared to standard of care imaging in initial staging of OSCC within the US Healthcare System. Methods A decision model was constructed using quality-adjusted life years (QALYs) and overall costs of different imaging strategies including a CT of the head, neck, and the thorax, MRI of the neck with CT of the thorax, and whole body F-18 FDG PET/CT using Markov transition simulations for different disease states. Input parameters were derived from literature and willingness to pay (WTP) was set to US $100,000/QALY. Deterministic sensitivity analysis of diagnostic parameters and costs was performed. Monte Carlo modeling was used for probabilistic sensitivity analysis. Results In the base-case scenario, total costs were at US $239,628 for CT, US $240,001 for MRI, and US $239,131 for F-18 FDG PET/CT whereas the model yielded an effectiveness of 5.29 QALYs for CT, 5.30 QALYs for MRI, and 5.32 QALYs for F-18 FDG PET/CT respectively. F-18 FDG PET/CT was the most cost-effective strategy over MRI as well as CT, and MRI was the cost-effective strategy over CT. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with incremental cost effectiveness ratio remaining below US $100,000/QALY for a wide range of variability of input parameters. Conclusion F-18 FDG PET/CT is the most cost-effective strategy in the initial N-staging of OSCC when compared to MRI and CT. Despite less routine use, both whole body PET/CT and MRI are cost-effective modalities in the N-staging of OSCC. Based on these findings, the implementation of PET/CT for initial staging could be suggested to help reduce costs while increasing effectiveness in OSCC.
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Affiliation(s)
- Egon Burian
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany. .,Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany.
| | - Benjamin Palla
- Department of Oral and Maxillofacial Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Nicholas Callahan
- Department of Oral and Maxillofacial Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Thomas Pyka
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland
| | - Constantin Wolff
- Department of Oral and Maxillofacial Surgery and Facial Plastic Surgery, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Claudio E von Schacky
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Annabelle Schmid
- Department of Oral and Maxillofacial Surgery and Facial Plastic Surgery, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Matthias F Froelich
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Johannes Rübenthaler
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Felix G Gassert
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
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12
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Gassert FT, Kufner A, Gassert FG, Leonhardt Y, Kronthaler S, Schwaiger BJ, Boehm C, Makowski MR, Kirschke JS, Baum T, Karampinos DC, Gersing AS. MR-based proton density fat fraction (PDFF) of the vertebral bone marrow differentiates between patients with and without osteoporotic vertebral fractures. Osteoporos Int 2022; 33:487-496. [PMID: 34537863 PMCID: PMC8813693 DOI: 10.1007/s00198-021-06147-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 09/03/2021] [Indexed: 12/20/2022]
Abstract
UNLABELLED The bone marrow proton density fat fraction (PDFF) assessed with MRI enables the differentiation between osteoporotic/osteopenic patients with and without vertebral fractures. Therefore, PDFF may be a potentially useful biomarker for bone fragility assessment. INTRODUCTION To evaluate whether magnetic resonance imaging (MRI)-based proton density fat fraction (PDFF) of vertebral bone marrow can differentiate between osteoporotic/osteopenic patients with and without vertebral fractures. METHODS Of the 52 study patients, 32 presented with vertebral fractures of the lumbar spine (66.4 ± 14.4 years, 62.5% women; acute low-energy osteoporotic/osteopenic vertebral fractures, N = 25; acute high-energy traumatic vertebral fractures, N = 7). These patients were frequency matched for age and sex to patients without vertebral fractures (N = 20, 69.3 ± 10.1 years, 70.0% women). Trabecular bone mineral density (BMD) values were derived from quantitative computed tomography. Chemical shift encoding-based water-fat MRI of the lumbar spine was performed, and PDFF maps were calculated. Associations between fracture status and PDFF were assessed using multivariable linear regression models. RESULTS Over all patients, mean PDFF and trabecular BMD correlated significantly (r = - 0.51, P < 0.001). In the osteoporotic/osteopenic group, those patients with osteoporotic/osteopenic fractures had a significantly higher PDFF than those without osteoporotic fractures after adjusting for age, sex, weight, height, and trabecular BMD (adjusted mean difference [95% confidence interval], 20.8% [10.4%, 30.7%]; P < 0.001), although trabecular BMD values showed no significant difference between the subgroups (P = 0.63). For the differentiation of patients with and without vertebral fractures in the osteoporotic/osteopenic subgroup using mean PDFF, an area under the receiver operating characteristic (ROC) curve (AUC) of 0.88 (P = 0.006) was assessed. When evaluating all patients with vertebral fractures, those with high-energy traumatic fractures had a significantly lower PDFF than those with low-energy osteoporotic/osteopenic vertebral fractures (P < 0.001). CONCLUSION MR-based PDFF enables the differentiation between osteoporotic/osteopenic patients with and without vertebral fractures, suggesting the use of PDFF as a potential biomarker for bone fragility.
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Affiliation(s)
- F T Gassert
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaningerstr. 22, 81675, Munich, Germany.
| | - A Kufner
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaningerstr. 22, 81675, Munich, Germany
| | - F G Gassert
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaningerstr. 22, 81675, Munich, Germany
| | - Y Leonhardt
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaningerstr. 22, 81675, Munich, Germany
| | - S Kronthaler
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaningerstr. 22, 81675, Munich, Germany
| | - B J Schwaiger
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaningerstr. 22, 81675, Munich, Germany
- Department of Neuroradiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - C Boehm
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaningerstr. 22, 81675, Munich, Germany
| | - M R Makowski
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaningerstr. 22, 81675, Munich, Germany
| | - J S Kirschke
- Department of Neuroradiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - T Baum
- Department of Neuroradiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - D C Karampinos
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaningerstr. 22, 81675, Munich, Germany
| | - A S Gersing
- Department of Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaningerstr. 22, 81675, Munich, Germany
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13
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von Schacky CE, Wilhelm NJ, Schäfer VS, Leonhardt Y, Jung M, Jungmann PM, Russe MF, Foreman SC, Gassert FG, Gassert FT, Schwaiger BJ, Mogler C, Knebel C, von Eisenhart-Rothe R, Makowski MR, Woertler K, Burgkart R, Gersing AS. Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors. Eur Radiol 2022; 32:6247-6257. [PMID: 35396665 PMCID: PMC9381439 DOI: 10.1007/s00330-022-08764-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 01/03/2022] [Accepted: 02/17/2022] [Indexed: 01/19/2023]
Abstract
OBJECTIVES To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists. METHODS In 880 patients (age 33.1 ± 19.4 years, 395 women) diagnosed with malignant (n = 213, 24.2%) or benign (n = 667, 75.8%) primary bone tumors, preoperative radiographs were obtained, and the diagnosis was established using histopathology. Data was split 70%/15%/15% for training, validation, and internal testing. Additionally, 96 patients from another institution were obtained for external testing. Machine learning models were developed and validated using radiomic features and demographic information. The performance of each model was evaluated on the test sets for accuracy, area under the curve (AUC) from receiver operating characteristics, sensitivity, and specificity. For comparison, the external test set was evaluated by two radiology residents and two radiologists who specialized in musculoskeletal tumor imaging. RESULTS The best machine learning model was based on an artificial neural network (ANN) combining both radiomic and demographic information achieving 80% and 75% accuracy at 75% and 90% sensitivity with 0.79 and 0.90 AUC on the internal and external test set, respectively. In comparison, the radiology residents achieved 71% and 65% accuracy at 61% and 35% sensitivity while the radiologists specialized in musculoskeletal tumor imaging achieved an 84% and 83% accuracy at 90% and 81% sensitivity, respectively. CONCLUSIONS An ANN combining radiomic features and demographic information showed the best performance in distinguishing between benign and malignant bone lesions. The model showed lower accuracy compared to specialized radiologists, while accuracy was higher or similar compared to residents. KEY POINTS • The developed machine learning model could differentiate benign from malignant bone tumors using radiography with an AUC of 0.90 on the external test set. • Machine learning models that used radiomic features or demographic information alone performed worse than those that used both radiomic features and demographic information as input, highlighting the importance of building comprehensive machine learning models. • An artificial neural network that combined both radiomic and demographic information achieved the best performance and its performance was compared to radiology readers on an external test set.
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Affiliation(s)
- Claudio E. von Schacky
- grid.6936.a0000000123222966Department of Radiology, Klinikum rechts der Isar, School of Medicine, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Nikolas J. Wilhelm
- Department for Orthopedics and Orthopedic Sports Medicine, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Valerie S. Schäfer
- grid.6936.a0000000123222966Department of Radiology, Klinikum rechts der Isar, School of Medicine, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Yannik Leonhardt
- grid.6936.a0000000123222966Department of Radiology, Klinikum rechts der Isar, School of Medicine, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Matthias Jung
- grid.7708.80000 0000 9428 7911Department of Diagnostic and Interventional Radiology, Medical Center–University of Freiburg, Faculty of Medicine, Freiburg, Germany
| | - Pia M. Jungmann
- grid.7708.80000 0000 9428 7911Department of Diagnostic and Interventional Radiology, Medical Center–University of Freiburg, Faculty of Medicine, Freiburg, Germany
| | - Maximilian F. Russe
- grid.7708.80000 0000 9428 7911Department of Diagnostic and Interventional Radiology, Medical Center–University of Freiburg, Faculty of Medicine, Freiburg, Germany
| | - Sarah C. Foreman
- grid.6936.a0000000123222966Department of Radiology, Klinikum rechts der Isar, School of Medicine, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Felix G. Gassert
- grid.6936.a0000000123222966Department of Radiology, Klinikum rechts der Isar, School of Medicine, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Florian T. Gassert
- grid.6936.a0000000123222966Department of Radiology, Klinikum rechts der Isar, School of Medicine, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Benedikt J. Schwaiger
- grid.6936.a0000000123222966Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technische Universität München, Munich, Germany
| | - Carolin Mogler
- grid.15474.330000 0004 0477 2438Institute of Pathology, Klinikum rechts der Isar, School of Medicine, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Carolin Knebel
- Department for Orthopedics and Orthopedic Sports Medicine, Ismaninger Strasse 22, 81675 Munich, Germany
| | | | - Marcus R. Makowski
- grid.6936.a0000000123222966Department of Radiology, Klinikum rechts der Isar, School of Medicine, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Klaus Woertler
- grid.6936.a0000000123222966Department of Radiology, Klinikum rechts der Isar, School of Medicine, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Rainer Burgkart
- Department for Orthopedics and Orthopedic Sports Medicine, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Alexandra S. Gersing
- grid.6936.a0000000123222966Department of Radiology, Klinikum rechts der Isar, School of Medicine, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany ,grid.5252.00000 0004 1936 973XDepartment of Neuroradiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 Munich, Germany
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14
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Leonhardt Y, Ketschau J, Ruschke S, Gassert FT, Glanz L, Feuerriegel GC, Gassert FG, Baum T, Kirschke JS, Braren RF, Schwaiger BJ, Makowski MR, Karampinos DC, Gersing AS. Associations of incidental vertebral fractures and longitudinal changes of MR-based proton density fat fraction and T2* measurements of vertebral bone marrow. Front Endocrinol (Lausanne) 2022; 13:1046547. [PMID: 36465625 PMCID: PMC9713243 DOI: 10.3389/fendo.2022.1046547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/02/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Quantitative magnetic resonance imaging (MRI) techniques such as chemical shift encoding-based water-fat separation techniques (CSE-MRI) are increasingly applied as noninvasive biomarkers to assess the biochemical composition of vertebrae. This study aims to investigate the longitudinal change of proton density fat fraction (PDFF) and T2* derived from CSE-MRI of the thoracolumbar vertebral bone marrow in patients that develop incidental vertebral compression fractures (VCFs), and whether PDFF and T2* enable the prediction of an incidental VCF. METHODS In this study we included 48 patients with CT-derived bone mineral density (BMD) measurements at baseline. Patients that presented an incidental VCF at follow up (N=12, mean age 70.5 ± 7.4 years, 5 female) were compared to controls without incidental VCF at follow up (N=36, mean age 71.1 ± 8.6 years, 15 females). All patients underwent 3T MRI, containing a significant part of the thoracolumbar spine (Th11-L4), at baseline, 6-month and 12 month follow up, including a gradient echo sequence for chemical shift encoding-based water-fat separation, from which PDFF and T2* maps were obtained. Associations between changes in PDFF, T2* and BMD measurements over 12 months and the group (incidental VCF vs. no VCF) were assessed using multivariable regression models. Mixed-effect regression models were used to test if there is a difference in the rate of change in PDFF, T2* and BMD between patients with and without incidental VCF. RESULTS Prior to the occurrence of an incidental VCF, PDFF in vertebrae increased in the VCF group (ΔPDFF=6.3 ± 3.1%) and was significantly higher than the change of PDFF in the group without VCF (ΔPDFF=2.1 ± 2.5%, P=0.03). There was no significant change in T2* (ΔT2*=1.7 ± 1.1ms vs. ΔT2*=1.1 ± 1.3ms, P=0.31) and BMD (ΔBMD=-1.2 ± 11.3mg/cm3 vs. ΔBMD=-11.4 ± 24.1mg/cm3, P= 0.37) between the two groups over 12 months. At baseline, no significant differences were detected in the average PDFF, T2* and BMD of all measured vertebrae (Th11-L4) between the VCF group and the group without VCF (P=0.66, P=0.35 and P= 0.21, respectively). When assessing the differences in rates of change, there was a significant change in slope for PDFF (2.32 per 6 months, 95% confidence interval (CI) 0.31-4.32; P=0.03) but not for T2* (0.02 per 6 months, CI -0.98-0.95; P=0.90) or BMD (-4.84 per 6 months, CI -23.4-13.7; P=0.60). CONCLUSIONS In our study population, the average change of PDFF over 12 months is significantly higher in patients that develop incidental fractures at 12-month follow up compared to patients without incidental VCF, while T2* and BMD show no significant changes prior to the occurrence of the incidental vertebral fractures. Therefore, a longitudinal increase in bone marrow PDFF may be predictive for vertebral compression fractures.
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Affiliation(s)
- Yannik Leonhardt
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
- *Correspondence: Yannik Leonhardt,
| | - Jannik Ketschau
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Stefan Ruschke
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Florian T. Gassert
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Leander Glanz
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Georg C. Feuerriegel
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Felix G. Gassert
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department on Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Jan S. Kirschke
- Department on Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Rickmer F. Braren
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Benedikt J. Schwaiger
- Department on Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Marcus R. Makowski
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Dimitrios C. Karampinos
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Alexandra S. Gersing
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Neuroradiology, University Hospital of Munich (LMU), Munich, Germany
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15
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Gassert FG, Ziegelmayer S, Luitjens J, Gassert FT, Tollens F, Rink J, Makowski MR, Rübenthaler J, Froelich MF. Additional MRI for initial M-staging in pancreatic cancer: a cost-effectiveness analysis. Eur Radiol 2021; 32:2448-2456. [PMID: 34837511 PMCID: PMC8921086 DOI: 10.1007/s00330-021-08356-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 09/21/2021] [Accepted: 09/24/2021] [Indexed: 12/27/2022]
Abstract
OBJECTIVE Pancreatic cancer is portrayed to become the second leading cause of cancer-related death within the next years. Potentially complicating surgical resection emphasizes the importance of an accurate TNM classification. In particular, the failure to detect features for non-resectability has profound consequences on patient outcomes and economic costs due to incorrect indication for resection. In the detection of liver metastases, contrast-enhanced MRI showed high sensitivity and specificity; however, the cost-effectiveness compared to the standard of care imaging remains unclear. The aim of this study was to analyze whether additional MRI of the liver is a cost-effective approach compared to routinely acquired contrast-enhanced computed tomography (CE-CT) in the initial staging of pancreatic cancer. METHODS A decision model based on Markov simulation was developed to estimate the quality-adjusted life-years (QALYs) and lifetime costs of the diagnostic modalities. Model input parameters were assessed based on evidence from recent literature. The willingness-to-pay (WTP) was set to $100,000/QALY. To evaluate model uncertainty, deterministic and probabilistic sensitivity analyses were performed. RESULTS In the base-case analysis, the model yielded a total cost of $185,597 and an effectiveness of 2.347 QALYs for CE-MR/CT and $187,601 and 2.337 QALYs for CE-CT respectively. With a net monetary benefit (NMB) of $49,133, CE-MR/CT is shown to be dominant over CE-CT with a NMB of $46,117. Deterministic and probabilistic survival analysis showed model robustness for varying input parameters. CONCLUSION Based on our results, combined CE-MR/CT can be regarded as a cost-effective imaging strategy for the staging of pancreatic cancer. KEY POINTS • Additional MRI of the liver for initial staging of pancreatic cancer results in lower total costs and higher effectiveness. • The economic model showed high robustness for varying input parameters.
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Affiliation(s)
- Felix G Gassert
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, München, Germany.
| | - Sebastian Ziegelmayer
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, München, Germany
| | - Johanna Luitjens
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Florian T Gassert
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, München, Germany
| | - Fabian Tollens
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Johann Rink
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, München, Germany
| | - Johannes Rübenthaler
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Matthias F Froelich
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
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16
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von Schacky CE, Wilhelm NJ, Schäfer VS, Leonhardt Y, Gassert FG, Foreman SC, Gassert FT, Jung M, Jungmann PM, Russe MF, Mogler C, Knebel C, von Eisenhart-Rothe R, Makowski MR, Woertler K, Burgkart R, Gersing AS. Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs. Radiology 2021; 301:398-406. [PMID: 34491126 DOI: 10.1148/radiol.2021204531] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. Materials and Methods This retrospective study analyzed bone tumors on radiographs acquired prior to treatment and obtained from patient data from January 2000 to June 2020. Benign or malignant bone tumors were diagnosed in all patients by using the histopathologic findings as the reference standard. By using split-sample validation, 70% of the patients were assigned to the training set, 15% were assigned to the validation set, and 15% were assigned to the test set. The final performance was evaluated on an external test set by using geographic validation, with accuracy, sensitivity, specificity, and 95% CIs being used for classification, the intersection over union (IoU) being used for bounding box placements, and the Dice score being used for segmentations. Results Radiographs from 934 patients (mean age, 33 years ± 19 [standard deviation]; 419 women) were evaluated in the internal data set, which included 667 benign bone tumors and 267 malignant bone tumors. Six hundred fifty-four patients were in the training set, 140 were in the validation set, and 140 were in the test set. One hundred eleven patients were in the external test set. The multitask DL model achieved 80.2% (89 of 111; 95% CI: 72.8, 87.6) accuracy, 62.9% (22 of 35; 95% CI: 47, 79) sensitivity, and 88.2% (67 of 76; CI: 81, 96) specificity in the classification of bone tumors as malignant or benign. The model achieved an IoU of 0.52 ± 0.34 for bounding box placements and a mean Dice score of 0.60 ± 0.37 for segmentations. The model accuracy was higher than that of two radiologic residents (71.2% and 64.9%; P = .002 and P < .001, respectively) and was comparable with that of two musculoskeletal fellowship-trained radiologists (83.8% and 82.9%; P = .13 and P = .25, respectively) in classifying a tumor as malignant or benign. Conclusion The developed multitask deep learning model allowed for accurate and simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Carrino in this issue.
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Affiliation(s)
- Claudio E von Schacky
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Nikolas J Wilhelm
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Valerie S Schäfer
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Yannik Leonhardt
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Felix G Gassert
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Sarah C Foreman
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Florian T Gassert
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Matthias Jung
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Pia M Jungmann
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Maximilian F Russe
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Carolin Mogler
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Carolin Knebel
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Rüdiger von Eisenhart-Rothe
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Marcus R Makowski
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Klaus Woertler
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Rainer Burgkart
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
| | - Alexandra S Gersing
- From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.)
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Schultheiss M, Schmette P, Bodden J, Aichele J, Müller-Leisse C, Gassert FG, Gassert FT, Gawlitza JF, Hofmann FC, Sasse D, von Schacky CE, Ziegelmayer S, De Marco F, Renger B, Makowski MR, Pfeiffer F, Pfeiffer D. Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance. Sci Rep 2021; 11:15857. [PMID: 34349135 PMCID: PMC8339004 DOI: 10.1038/s41598-021-94750-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 07/15/2021] [Indexed: 12/24/2022] Open
Abstract
We present a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge. We evaluated the detection performance of nine radiologists and two convolutional neural networks in a reader study. Nodules were artificially inserted into the lung of a CT volume and synthetic radiographs were obtained by forward-projecting the volume. Hence, our framework allowed for a detailed evaluation of CAD systems' and radiologists' performance due to the availability of accurate ground-truth labels for nodules from synthetic data. Radiographs for network training (U-Net and RetinaNet) were generated from 855 CT scans of a public dataset. For the reader study, 201 radiographs were generated from 21 nodule-free CT scans with altering nodule positions, sizes and nodule counts of inserted nodules. Average true positive detections by nine radiologists were 248.8 nodules, 51.7 false positive predicted nodules and 121.2 false negative predicted nodules. The best performing CAD system achieved 268 true positives, 66 false positives and 102 false negatives. Corresponding weighted alternative free response operating characteristic figure-of-merits (wAFROC FOM) for the radiologists range from 0.54 to 0.87 compared to a value of 0.81 (CI 0.75-0.87) for the best performing CNN. The CNN did not perform significantly better against the combined average of the 9 readers (p = 0.49). Paramediastinal nodules accounted for most false positive and false negative detections by readers, which can be explained by the presence of more tissue in this area.
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Affiliation(s)
- Manuel Schultheiss
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany.
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany.
| | - Philipp Schmette
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
| | - Jannis Bodden
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Juliane Aichele
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Christina Müller-Leisse
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Felix G Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Florian T Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Joshua F Gawlitza
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Felix C Hofmann
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Daniel Sasse
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Claudio E von Schacky
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Sebastian Ziegelmayer
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Fabio De Marco
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
| | - Bernhard Renger
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Franz Pfeiffer
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Daniela Pfeiffer
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
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Leonhardt Y, Gassert FT, Feuerriegel G, Gassert FG, Kronthaler S, Boehm C, Kufner A, Ruschke S, Baum T, Schwaiger BJ, Makowski MR, Karampinos DC, Gersing AS. Vertebral bone marrow T2* mapping using chemical shift encoding-based water-fat separation in the quantitative analysis of lumbar osteoporosis and osteoporotic fractures. Quant Imaging Med Surg 2021; 11:3715-3725. [PMID: 34341744 PMCID: PMC8245952 DOI: 10.21037/qims-20-1373] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 04/07/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND Chemical shift encoding-based water-fat separation techniques have been used for fat quantification [proton density fat fraction (PDFF)], but they also enable the assessment of bone marrow T2*, which has previously been reported to be a potential biomarker for osteoporosis and may give insight into the cause of vertebral fractures (i.e., osteoporotic vs. traumatic) and the microstructure of the bone when applied to vertebral bone marrow. METHODS The 32 patients (78.1% with low-energy osteopenic/osteoporotic fractures, mean age 72.3±9.8 years, 76% women; 21.9% with high-energy traumatic fractures, 47.3±12.8 years, no women) were frequency-matched for age and sex to subjects without vertebral fractures (n=20). All study patients underwent 3T-MRI of the lumbar spine including sagittally acquired spoiled gradient echo sequences for chemical shift encoding-based water-fat separation, from which T2* values were obtained. Volumetric trabecular bone mineral density (BMD) and trabecular bone parameters describing the three-dimensional structural integrity of trabecular bone were derived from quantitative CT. Associations between T2* measurements, fracture status and trabecular bone parameters were assessed using multivariable linear regression models. RESULTS Mean T2* values of non fractured vertebrae in all patients showed a significant correlation with BMD (r=-0.65, P<0.001), trabecular number (TbN) (r=-0.56, P<0.001) and trabecular spacing (TbSp) (r=0.61, P<0.001); patients with low-energy osteoporotic vertebral fractures showed significantly higher mean T2* values than those with traumatic fractures (13.6±4.3 vs. 8.4±2.2 ms, P=0.01) as well as a significantly lower TbN (0.69±0.08 vs. 0.93±0.03 mm-1, P<0.01) and a significantly larger trabecular spacing (1.06±0.16 vs. 0.56±0.08 mm, P<0.01). Mean T2* values of osteoporotic patients with and without vertebral fracture showed no significant difference (13.5±3.4 vs. 15.6±3.5 ms, P=0.40). When comparing the mean T2* of the fractured vertebrae, no significant difference could be detected between low-energy osteoporotic fractures and high-energy traumatic fractures (12.6±5.4 vs. 8.1±2.4 ms, P=0.10). CONCLUSIONS T2* mapping of vertebral bone marrow using using chemical shift encoding-based water-fat separation allows for assessing osteoporosis as well as the trabecular microstructure and enables a radiation-free differentiation between patients with low-energy osteoporotic and high-energy traumatic vertebral fractures, suggesting its potential as a biomarker for bone fragility.
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Affiliation(s)
- Yannik Leonhardt
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Florian T. Gassert
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Georg Feuerriegel
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Felix G. Gassert
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Sophia Kronthaler
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christof Boehm
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Alexander Kufner
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Stefan Ruschke
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Benedikt J. Schwaiger
- Department of Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Marcus R. Makowski
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Dimitrios C. Karampinos
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Alexandra S. Gersing
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Neuroradiology, University Hospital of Munich (LMU), Munich, Germany
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Gassert FG, Rübenthaler J, Cyran CC, Rink JS, Schwarze V, Luitjens J, Gassert FT, Makowski MR, Schoenberg SO, Mayerhoefer ME, Tamandl D, Froelich MF. 18F FDG PET/MRI with hepatocyte-specific contrast agent for M staging of rectal cancer: a primary economic evaluation. Eur J Nucl Med Mol Imaging 2021; 48:3268-3276. [PMID: 33686457 PMCID: PMC8426298 DOI: 10.1007/s00259-021-05193-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 01/03/2021] [Indexed: 12/25/2022]
Abstract
Purpose Rectal cancer is one of the most frequent causes of cancer-related morbidity and mortality in the world. Correct identification of the TNM state in primary staging of rectal cancer has critical implications on patient management. Initial evaluations revealed a high sensitivity and specificity for whole-body PET/MRI in the detection of metastases allowing for metastasis-directed therapy regimens. Nevertheless, its cost-effectiveness compared with that of standard-of-care imaging (SCI) using pelvic MRI + chest and abdominopelvic CT is yet to be investigated. Therefore, the aim of this study was to analyze the cost-effectiveness of whole-body 18F FDG PET/MRI as an alternative imaging method to standard diagnostic workup for initial staging of rectal cancer. Methods For estimation of quality-adjusted life years (QALYs) and lifetime costs of diagnostic modalities, a decision model including whole-body 18F FDG PET/MRI with a hepatocyte-specific contrast agent and pelvic MRI + chest and abdominopelvic CT was created based on Markov simulations. For obtaining model input parameters, review of recent literature was performed. Willingness to pay (WTP) was set to $100,000/QALY. Deterministic sensitivity analysis of diagnostic parameters and costs was applied, and probabilistic sensitivity was determined using Monte Carlo modeling. Results In the base-case scenario, the strategy whole-body 18F FDG PET/MRI resulted in total costs of $52,186 whereas total costs of SCI were at $51,672. Whole-body 18F FDG PET/MRI resulted in an expected effectiveness of 3.542 QALYs versus 3.535 QALYs for SCI. This resulted in an incremental cost-effectiveness ratio of $70,291 per QALY for PET/MRI. Thus, from an economic point of view, whole-body 18F FDG PET/MRI was identified as an adequate diagnostic alternative to SCI with high robustness of results to variation of input parameters. Conclusion Based on the results of the analysis, use of whole-body 18F FDG PET/MRI was identified as a feasible diagnostic strategy for initial staging of rectal cancer from a cost-effectiveness perspective.
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Affiliation(s)
- Felix G Gassert
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Johannes Rübenthaler
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr 15, 81377, Munich, Germany
| | - Clemens C Cyran
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr 15, 81377, Munich, Germany
| | - Johann S Rink
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Vincent Schwarze
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr 15, 81377, Munich, Germany
| | - Johanna Luitjens
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr 15, 81377, Munich, Germany
| | - Florian T Gassert
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Stefan O Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Marius E Mayerhoefer
- Department of Radiology, Memorial Sloan Kettering Cancer Center New York, New York City, NY, USA
| | - Dietmar Tamandl
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Matthias F Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
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Gassert FG, Gassert FT, Specht K, Knebel C, Lenze U, Makowski MR, von Eisenhart-Rothe R, Gersing AS, Woertler K. Soft tissue masses: distribution of entities and rate of malignancy in small lesions. BMC Cancer 2021; 21:93. [PMID: 33482754 PMCID: PMC7825232 DOI: 10.1186/s12885-020-07769-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 12/25/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Small soft tissue masses are often falsely assumed to be benign and resected with failure to achieve tumor-free margins. Therefore, this study retrospectively investigated the distribution of histopathologic diagnosis to be encountered in small soft tissue tumors (≤ 5 cm) in a large series of a tertiary referral center. METHODS Patients with a soft tissue mass (STM) with a maximum diameter of 5 cm presenting at our institution over a period of 10 years, who had undergone preoperative Magnetic resonance imaging and consequent biopsy or/and surgical resection, were included in this study. A final histopathological diagnosis was available in all cases. The maximum tumor diameter was determined on MR images by one radiologist. Moreover, tumor localization (head/neck, trunk, upper extremity, lower extremity, hand, foot) and depth (superficial / deep to fascia) were assessed. RESULTS In total, histopathologic results and MR images of 1753 patients were reviewed. Eight hundred seventy patients (49.63%) showed a STM ≤ 5 cm and were therefore included in this study (46.79 +/- 18.08 years, 464 women). Mean maximum diameter of the assessed STMs was 2.88 cm. Of 870 analyzed lesions ≤ 5 cm, 170 (19.54%) were classified as superficial and 700 (80.46%) as deep. The malignancy rate of all lesions ≤ 5 cm was at 22.41% (superficial: 23.53% / deep: 22.14%). The malignancy rate dropped to 16.49% (20.79% / 15.32%) when assessing lesions ≤ 3 cm (p = 0.007) and to 15.0% (18.18% / 13.79%) when assessing lesions ≤ 2 cm (p = 0.006). Overall, lipoma was the most common benign lesion of superficial STMs (29.41%) and tenosynovial giant cell tumor was the most common benign lesion of deep STMs (23.29%). Undifferentiated pleomorphic sarcoma was the most common malignant diagnosis among both, superficial (5.29%) and deep (3.57%) STMs. CONCLUSIONS The rate of malignancy decreased significantly with tumor size in both, superficial and deep STMs. The distribution of entities was different between superficial and deep STMs, yet there was no significant difference found in the malignancy rate.
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Affiliation(s)
- Felix G. Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Florian T. Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Katja Specht
- Department of Pathology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Carolin Knebel
- Department of Orthopaedic Surgery, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Ulrich Lenze
- Department of Orthopaedic Surgery, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Marcus R. Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Rüdiger von Eisenhart-Rothe
- Department of Orthopaedic Surgery, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Alexandra S. Gersing
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Klaus Woertler
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
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Schnitzer ML, Froelich MF, Gassert FG, Huber T, Gresser E, Schwarze V, Nörenberg D, Todica A, Rübenthaler J. Follow-Up 18F-FDG PET/CT versus Contrast-Enhanced CT after Ablation of Liver Metastases of Colorectal Carcinoma-A Cost-Effectiveness Analysis. Cancers (Basel) 2020; 12:cancers12092432. [PMID: 32867107 PMCID: PMC7565889 DOI: 10.3390/cancers12092432] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 08/19/2020] [Accepted: 08/24/2020] [Indexed: 12/28/2022] Open
Abstract
PURPOSE After a percutaneous ablation of colorectal liver metastases (CRLM), follow-up investigations to evaluate potential tumor recurrence are necessary. The aim of this study was to analyze whether a combined 18F-Fluordesoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) scan is cost-effective compared to a contrast-enhanced computed tomography (CE-CT) scan for detecting local tumor progression. MATERIALS AND METHODS A decision model based on Markov simulations that estimated lifetime costs and quality-adjusted life years (QALYs) was developed. Model input parameters were obtained from the recent literature. Deterministic sensitivity analysis of diagnostic parameters based on a Monte-Carlo simulation with 30,000 iterations was performed. The willingness-to-pay (WTP) was set to $100,000/QALY. RESULTS In the base-case scenario, CE-CT resulted in total costs of $28,625.08 and an efficacy of 0.755 QALYs, whereas 18F-FDG PET/CT resulted in total costs of $29,239.97 with an efficacy of 0.767. Therefore, the corresponding incremental cost-effectiveness ratio (ICER) of 18F-FDG PET/CT was $50,338.96 per QALY indicating cost-effectiveness based on the WTP threshold set above. The results were stable in deterministic and probabilistic sensitivity analyses. CONCLUSION Based on our model, 18F-FDG PET/CT can be considered as a cost-effective imaging alternative for follow-up investigations after percutaneous ablation of colorectal liver metastases.
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Affiliation(s)
- Moritz L. Schnitzer
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany; (M.L.S.); (E.G.); (V.S.)
| | - Matthias F. Froelich
- Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (M.F.F.); (T.H.); (D.N.)
| | - Felix G. Gassert
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany;
| | - Thomas Huber
- Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (M.F.F.); (T.H.); (D.N.)
| | - Eva Gresser
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany; (M.L.S.); (E.G.); (V.S.)
| | - Vincent Schwarze
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany; (M.L.S.); (E.G.); (V.S.)
| | - Dominik Nörenberg
- Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (M.F.F.); (T.H.); (D.N.)
| | - Andrei Todica
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany;
| | - Johannes Rübenthaler
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany; (M.L.S.); (E.G.); (V.S.)
- Correspondence:
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Gassert FT, Gassert FG, Topping GJ, Rummeny EJ, Wildgruber M, Meier R, Kimm MA. SNR analysis of contrast-enhanced MR imaging for early detection of rheumatoid arthritis. PLoS One 2019; 14:e0213082. [PMID: 30822342 PMCID: PMC6396898 DOI: 10.1371/journal.pone.0213082] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 02/14/2019] [Indexed: 12/16/2022] Open
Abstract
Objective To investigate whether signal to noise (SNR) analysis of contrast-enhanced MRI gives additional benefit for early disease detection by Magnetic Resonance Imaging (MRI) of experimental rheumatoid arthritis (RA) in a small animal model. Methods We applied contrast-enhanced MRI at 7T in DBA mice with or without collagen-induced arthritis (CIA). Clinical score, OMERACT RAMRIS analysis and analysis of signal to noise ratios (SNR) of regions of interest in RA bearing mice, methotrexate/methylprednisolone acetate treated RA and control animals were compared with respect to benefit for early diagnosis. Results While treated RA and control animals did not show signs of RA activity in any of the above-mentioned scoring methods at any time point analyzed, RA animals revealed characteristic signs of RA in RAMRIS at the same time point when RA was detected clinically through scoring of the paws. The MR-based SNR analysis detected signs of synovitis, the earliest indication of RA, not only in late clinical stages, but also at an early stage when little or no clinical signs of RA were present in CIA animals and RAMRIS did not allow a distinct early detection. Conclusion SNR analysis of contrast-enhanced MR imaging provides additional benefit for early arthritis detection in CIA mice.
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Affiliation(s)
- Florian T. Gassert
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universitaet Muenchen, Munich, Germany
| | - Felix G. Gassert
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universitaet Muenchen, Munich, Germany
| | - Geoffrey J. Topping
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universitaet Muenchen, Munich, Germany
| | - Ernst J. Rummeny
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universitaet Muenchen, Munich, Germany
| | - Moritz Wildgruber
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universitaet Muenchen, Munich, Germany
- Translational Research Imaging Center, Department of Clinical Radiology, Universitätsklinikum Muenster, Muenster, Germany
| | - Reinhard Meier
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universitaet Muenchen, Munich, Germany
| | - Melanie A. Kimm
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universitaet Muenchen, Munich, Germany
- * E-mail:
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