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Beste NC, Jende J, Kronlage M, Kurz F, Heiland S, Bendszus M, Meredig H. Automated peripheral nerve segmentation for MR-neurography. Eur Radiol Exp 2024; 8:97. [PMID: 39186183 PMCID: PMC11347527 DOI: 10.1186/s41747-024-00503-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 08/01/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Magnetic resonance neurography (MRN) is increasingly used as a diagnostic tool for peripheral neuropathies. Quantitative measures enhance MRN interpretation but require nerve segmentation which is time-consuming and error-prone and has not become clinical routine. In this study, we applied neural networks for the automated segmentation of peripheral nerves. METHODS A neural segmentation network was trained to segment the sciatic nerve and its proximal branches on the MRN scans of the right and left upper leg of 35 healthy individuals, resulting in 70 training examples, via 5-fold cross-validation (CV). The model performance was evaluated on an independent test set of one-sided MRN scans of 60 healthy individuals. RESULTS Mean Dice similarity coefficient (DSC) in CV was 0.892 (95% confidence interval [CI]: 0.888-0.897) with a mean Jaccard index (JI) of 0.806 (95% CI: 0.799-0.814) and mean Hausdorff distance (HD) of 2.146 (95% CI: 2.184-2.208). For the independent test set, DSC and JI were lower while HD was higher, with a mean DSC of 0.789 (95% CI: 0.760-0.815), mean JI of 0.672 (95% CI: 0.642-0.699), and mean HD of 2.118 (95% CI: 2.047-2.190). CONCLUSION The deep learning-based segmentation model showed a good performance for the task of nerve segmentation. Future work will focus on extending training data and including individuals with peripheral neuropathies in training to enable advanced peripheral nerve disease characterization. RELEVANCE STATEMENT The results will serve as a baseline to build upon while developing an automated quantitative MRN feature analysis framework for application in routine reading of MRN examinations. KEY POINTS Quantitative measures enhance MRN interpretation, requiring complex and challenging nerve segmentation. We present a deep learning-based segmentation model with good performance. Our results may serve as a baseline for clinical automated quantitative MRN segmentation.
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Affiliation(s)
- Nedim Christoph Beste
- Institute of Neuroradiology, University Hospital of Heidelberg, Heidelberg, Germany.
| | - Johann Jende
- Institute of Neuroradiology, University Hospital of Heidelberg, Heidelberg, Germany
| | - Moritz Kronlage
- Institute of Neuroradiology, University Hospital of Heidelberg, Heidelberg, Germany
| | - Felix Kurz
- DKFZ German Cancer Research Center, Heidelberg, Germany
| | - Sabine Heiland
- Institute of Neuroradiology, University Hospital of Heidelberg, Heidelberg, Germany
| | - Martin Bendszus
- Institute of Neuroradiology, University Hospital of Heidelberg, Heidelberg, Germany
| | - Hagen Meredig
- Institute of Neuroradiology, University Hospital of Heidelberg, Heidelberg, Germany
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Mooshage CM, Schimpfle L, Tsilingiris D, Kender Z, Aziz-Safaie T, Hohmann A, Szendroedi J, Nawroth P, Sturm V, Heiland S, Bendszus M, Kopf S, Jende JME, Kurz FT. Magnetization transfer ratio of the sciatic nerve differs between patients in type 1 and type 2 diabetes. Eur Radiol Exp 2024; 8:6. [PMID: 38191821 PMCID: PMC10774497 DOI: 10.1186/s41747-023-00405-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/07/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Previous studies on magnetic resonance neurography (MRN) found different patterns of structural nerve damage in type 1 diabetes (T1D) and type 2 diabetes (T2D). Magnetization transfer ratio (MTR) is a quantitative technique to analyze the macromolecular tissue composition. We compared MTR values of the sciatic nerve in patients with T1D, T2D, and healthy controls (HC). METHODS 3-T MRN of the right sciatic nerve at thigh level was performed in 14 HC, 10 patients with T1D (3 with diabetic neuropathy), and 28 patients with T2D (10 with diabetic neuropathy). Results were subsequently correlated with clinical and electrophysiological data. RESULTS The sciatic nerve's MTR was lower in patients with T2D (0.211 ± 0.07, mean ± standard deviation) compared to patients with T1D (T1D 0.285 ± 0.03; p = 0.015) and HC (0.269 ± 0.05; p = 0.039). In patients with T1D, sciatic MTR correlated positively with tibial nerve conduction velocity (NCV; r = 0.71; p = 0.021) and negatively with hemoglobin A1c (r = - 0.63; p < 0.050). In patients with T2D, we found negative correlations of sciatic nerve's MTR peroneal NCV (r = - 0.44; p = 0.031) which remained significant after partial correlation analysis controlled for age and body mass index (r = 0.51; p = 0.016). CONCLUSIONS Lower MTR values of the sciatic nerve in T2D compared to T1D and HC and diametrical correlations of MTR values with NCV in T1D and T2D indicate that there are different macromolecular changes and pathophysiological pathways underlying the development of neuropathic nerve damage in T1D and T2D. TRIAL REGISTRATION https://classic. CLINICALTRIALS gov/ct2/show/NCT03022721 . 16 January 2017. RELEVANCE STATEMENT Magnetization transfer ratio imaging may serve as a non-invasive imaging method to monitor the diseases progress and to encode the pathophysiology of nerve damage in patients with type 1 and type 2 diabetes. KEY POINTS • Magnetization transfer imaging detects distinct macromolecular nerve lesion patterns in diabetes patients. • Magnetization transfer ratio was lower in type 2 diabetes compared to type 1 diabetes. • Different pathophysiological mechanisms drive nerve damage in type 1 and 2 diabetes.
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Affiliation(s)
- Christoph M Mooshage
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg, 69120, Germany
| | - Lukas Schimpfle
- Department of Endocrinology, Diabetology and Clinical Chemistry (Internal Medicine 1), Heidelberg University Hospital, Heidelberg, Germany
- German Center of Diabetes Research, associated partner in the DZD, Munich-Neuherberg, Germany
- Institute for Diabetes and Cancer (IDC), Helmholtz Diabetes Center, Helmholtz Center, Munich, Neuherberg, Germany
| | - Dimitrios Tsilingiris
- Department of Endocrinology, Diabetology and Clinical Chemistry (Internal Medicine 1), Heidelberg University Hospital, Heidelberg, Germany
- German Center of Diabetes Research, associated partner in the DZD, Munich-Neuherberg, Germany
- Institute for Diabetes and Cancer (IDC), Helmholtz Diabetes Center, Helmholtz Center, Munich, Neuherberg, Germany
| | - Zoltan Kender
- Department of Endocrinology, Diabetology and Clinical Chemistry (Internal Medicine 1), Heidelberg University Hospital, Heidelberg, Germany
- German Center of Diabetes Research, associated partner in the DZD, Munich-Neuherberg, Germany
- Institute for Diabetes and Cancer (IDC), Helmholtz Diabetes Center, Helmholtz Center, Munich, Neuherberg, Germany
| | - Taraneh Aziz-Safaie
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg, 69120, Germany
| | - Anja Hohmann
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Julia Szendroedi
- Department of Endocrinology, Diabetology and Clinical Chemistry (Internal Medicine 1), Heidelberg University Hospital, Heidelberg, Germany
- German Center of Diabetes Research, associated partner in the DZD, Munich-Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Inner Medicine 1, Heidelberg University Hospital, Heidelberg, Germany
| | - Peter Nawroth
- Department of Endocrinology, Diabetology and Clinical Chemistry (Internal Medicine 1), Heidelberg University Hospital, Heidelberg, Germany
- German Center of Diabetes Research, associated partner in the DZD, Munich-Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Inner Medicine 1, Heidelberg University Hospital, Heidelberg, Germany
| | - Volker Sturm
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg, 69120, Germany
- Division of Experimental Radiology, Department of Neuroradiology, Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg, 69120, Germany
- Division of Experimental Radiology, Department of Neuroradiology, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg, 69120, Germany
| | - Stefan Kopf
- Department of Endocrinology, Diabetology and Clinical Chemistry (Internal Medicine 1), Heidelberg University Hospital, Heidelberg, Germany
- German Center of Diabetes Research, associated partner in the DZD, Munich-Neuherberg, Germany
- Institute for Diabetes and Cancer (IDC), Helmholtz Diabetes Center, Helmholtz Center, Munich, Neuherberg, Germany
| | - Johann M E Jende
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg, 69120, Germany
| | - Felix T Kurz
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg, 69120, Germany.
- German Cancer Research Center, Heidelberg, Germany.
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Xu TC, Liu Y, Yu Z, Xu B. Gut-targeted therapies for type 2 diabetes mellitus: A review. World J Clin Cases 2024; 12:1-8. [PMID: 38292634 PMCID: PMC10824172 DOI: 10.12998/wjcc.v12.i1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 11/24/2023] [Accepted: 12/18/2023] [Indexed: 01/02/2024] Open
Abstract
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by hyperglycemia and insulin resistance. The global prevalence of T2DM has reached epidemic proportions, affecting approximately 463 million adults worldwide in 2019. Current treatments for T2DM include lifestyle modifications, oral antidiabetic agents, and insulin therapy. However, these therapies may carry side effects and fail to achieve optimal glycemic control in some patients. Therefore, there is a growing interest in the role of gut microbiota and more gut-targeted therapies in the management of T2DM. The gut microbiota, which refers to the community of microorganisms that inhabit the human gut, has been shown to play a crucial role in the regulation of glucose metabolism and insulin sensitivity. Alterations in gut microbiota composition and diversity have been observed in T2DM patients, with a reduction in beneficial bacteria and an increase in pathogenic bacteria. This dysbiosis may contribute to the pathogenesis of the disease by promoting inflammation and impairing gut barrier function. Several gut-targeted therapies have been developed to modulate the gut microbiota and improve glycemic control in T2DM. One potential approach is the use of probiotics, which are live microorganisms that confer health benefits to the host when administered in adequate amounts. Several randomized controlled trials have demonstrated that certain probiotics, such as Lactobacillus and Bifidobacterium species, can improve glycemic control and insulin sensitivity in T2DM patients. Mechanisms may include the production of short-chain fatty acids, the improvement of gut barrier function, and the reduction of inflammation. Another gut-targeted therapy is fecal microbiota transplantation (FMT), which involves the transfer of fecal material from a healthy donor to a recipient. FMT has been used successfully in the treatment of Clostridioides difficile infection and is now being investigated as a potential therapy for T2DM. A recent randomized controlled trial showed that FMT from lean donors improved glucose metabolism and insulin sensitivity in T2DM patients with obesity. However, FMT carries potential risks, including transmission of infectious agents and alterations in the recipient's gut microbiota that may be undesirable. In addition to probiotics and FMT, other gut-targeted therapies are being investigated for the management of T2DM, such as prebiotics, synbiotics, and postbiotics. Prebiotics are dietary fibers that promote the growth of beneficial gut bacteria, while synbiotics combine probiotics and prebiotics. Postbiotics refer to the metabolic products of probiotics that may have beneficial effects on the host. The NIH SPARC program, or the Stimulating Peripheral Activity to Relieve Conditions, is a research initiative aimed at developing new therapies for a variety of health conditions, including T2DM. The SPARC program focuses on using electrical stimulation to activate peripheral nerves and organs, in order to regulate glucose levels in the body. The goal of this approach is to develop targeted, non-invasive therapies that can help patients better manage their diabetes. One promising area of research within the SPARC program is the use of electrical stimulation to activate the vagus nerve, which plays an important role in regulating glucose metabolism. Studies have shown that vagus nerve stimulation can improve insulin sensitivity and lower blood glucose levels in patients with T2DM. Gut-targeted therapies, such as probiotics and FMT, have shown potential for improving glycemic control and insulin sensitivity in T2DM patients. However, further research is needed to determine the optimal dose, duration, and safety of these therapies.
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Affiliation(s)
- Tian-Cheng Xu
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China
| | - Yun Liu
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China
| | - Zhi Yu
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China
| | - Bin Xu
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China
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