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Loh WJ, Yaligar J, Hooper AJ, Sadananthan SA, Kway Y, Lim SC, Watts GF, Velan SS, Leow MKS, Khoo J. Clinical and imaging features of women with polygenic partial lipodystrophy: a case series. Nutr Diabetes 2024; 14:3. [PMID: 38321009 PMCID: PMC10847407 DOI: 10.1038/s41387-024-00260-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 01/13/2024] [Accepted: 01/19/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Familial partial lipodystrophy (FPLD) is an inherited disorder of white adipose tissue that causes premature cardiometabolic disease. There is no clear diagnostic criteria for FPLD, and this may explain the under-detection of this condition. AIM This pilot study aimed to describe the clinical features of women with FPLD and to explore the value of adipose tissue measurements that could be useful in diagnosis. METHODS In 8 women with FPLD and 4 controls, skinfold measurements, DXA and whole-body MRI were undertaken. RESULTS Whole genome sequencing was negative for monogenic metabolic causes, but polygenic scores for partial lipodystrophy were elevated in keeping with FPLD type 1. The mean age of diagnosis of DM was 31 years in the FPLD group. Compared with controls, the FPLD group had increased HOMA-IR (10.3 vs 2.9, p = 0.028) and lower mean thigh skinfold thickness (19.5 mm vs 48.2 mm, p = 0.008). The FPLD group had lower percentage of leg fat and an increased ratio of trunk to leg fat percentage on DXA. By MRI, the FPLD group had decreased subcutaneous adipose tissue (SAT) volume in the femoral and calf regions (p < 0.01); abdominal SAT, visceral adipose tissue, and femoral and calf muscle volumes were not different from controls. CONCLUSION Women with FPLD1 in Singapore have significant loss of adipose but not muscle tissue in lower limbs and have early onset of diabetes. Reduced thigh skinfold, and increased ratio of trunk to leg fat percentage on DXA are potentially clinically useful markers to identify FPLD1.
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Affiliation(s)
- Wann Jia Loh
- Department of Endocrinology, Changi General Hospital, Singapore, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
| | - Jadegoud Yaligar
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
| | - Amanda J Hooper
- Department of Biochemistry, Pathwest and Fiona Stanley Hospital Network, Perth, Australia
- School of Medicine, University of Western Australia, Perth, Australia
| | - Suresh Anand Sadananthan
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
| | - Yeshe Kway
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
- Departments of Medicine and Physiology, NUS Yong Loo School of Medicine, NUS, Singapore, Singapore
| | - Su Chi Lim
- Diabetes Centre, Admiralty Medical Centre, Singapore, Singapore
| | - Gerald F Watts
- School of Medicine, University of Western Australia, Perth, Australia
- Department of Cardiology and Internal Medicine, Royal Perth Hospital, Perth, Australia
| | - Sambasivam Sendhil Velan
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
- Departments of Medicine and Physiology, NUS Yong Loo School of Medicine, NUS, Singapore, Singapore
| | - Melvin Khee Shing Leow
- Duke-NUS Medical School, Singapore, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
- Department of Endocrinology, Tan Tock Seng Hospital, Singapore, Singapore
- LKC School of Medicine, NTU, Singapore, Singapore
| | - Joan Khoo
- Department of Endocrinology, Changi General Hospital, Singapore, Singapore
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Kafali SG, Shih SF, Li X, Kim GHJ, Kelly T, Chowdhury S, Loong S, Moretz J, Barnes SR, Li Z, Wu HH. Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-023-01146-3. [PMID: 38300360 DOI: 10.1007/s10334-023-01146-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 12/18/2023] [Accepted: 12/27/2023] [Indexed: 02/02/2024]
Abstract
OBJECTIVE Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs. MATERIALS AND METHODS 920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC). RESULTS ACD 3D U-Net achieved rapid (< 4.8 s/subject) segmentation with high DICE-SAT (median ≥ 0.994) and DICE-VAT (median ≥ 0.976), small FN (median ≤ 0.7%), and FP (median ≤ 1.1%). 3D nnU-Net yielded rapid (< 2.5 s/subject) segmentation with similar DICE-SAT (median ≥ 0.992), DICE-VAT (median ≥ 0.979), FN (median ≤ 1.1%) and FP (median ≤ 1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC > 0.997) in longitudinal analysis. DISCUSSION ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.
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Affiliation(s)
- Sevgi Gokce Kafali
- Department of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California, Los Angeles, CA, USA
| | - Shu-Fu Shih
- Department of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California, Los Angeles, CA, USA
| | - Xinzhou Li
- Department of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
| | - Grace Hyun J Kim
- Department of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
| | - Tristan Kelly
- Department of Physiological Science, University of California, Los Angeles, CA, USA
| | - Shilpy Chowdhury
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Spencer Loong
- Department of Psychology, Loma Linda University School of Behavioral Health, Loma Linda, CA, USA
| | - Jeremy Moretz
- Department of Neuroradiology, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Samuel R Barnes
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Zhaoping Li
- Department of Medicine, University of California, Los Angeles, CA, USA
| | - Holden H Wu
- Department of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.
- Department of Bioengineering, University of California, Los Angeles, CA, USA.
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Ahmad N, Strand R, Sparresäter B, Tarai S, Lundström E, Bergström G, Ahlström H, Kullberg J. Automatic segmentation of large-scale CT image datasets for detailed body composition analysis. BMC Bioinformatics 2023; 24:346. [PMID: 37723444 PMCID: PMC10506248 DOI: 10.1186/s12859-023-05462-2] [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: 03/27/2023] [Accepted: 09/01/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND Body composition (BC) is an important factor in determining the risk of type 2-diabetes and cardiovascular disease. Computed tomography (CT) is a useful imaging technique for studying BC, however manual segmentation of CT images is time-consuming and subjective. The purpose of this study is to develop and evaluate fully automated segmentation techniques applicable to a 3-slice CT imaging protocol, consisting of single slices at the level of the liver, abdomen, and thigh, allowing detailed analysis of numerous tissues and organs. METHODS The study used more than 4000 CT subjects acquired from the large-scale SCAPIS and IGT cohort to train and evaluate four convolutional neural network based architectures: ResUNET, UNET++, Ghost-UNET, and the proposed Ghost-UNET++. The segmentation techniques were developed and evaluated for automated segmentation of the liver, spleen, skeletal muscle, bone marrow, cortical bone, and various adipose tissue depots, including visceral (VAT), intraperitoneal (IPAT), retroperitoneal (RPAT), subcutaneous (SAT), deep (DSAT), and superficial SAT (SSAT), as well as intermuscular adipose tissue (IMAT). The models were trained and validated for each target using tenfold cross-validation and test sets. RESULTS The Dice scores on cross validation in SCAPIS were: ResUNET 0.964 (0.909-0.996), UNET++ 0.981 (0.927-0.996), Ghost-UNET 0.961 (0.904-0.991), and Ghost-UNET++ 0.968 (0.910-0.994). All four models showed relatively strong results, however UNET++ had the best performance overall. Ghost-UNET++ performed competitively compared to UNET++ and showed a more computationally efficient approach. CONCLUSION Fully automated segmentation techniques can be successfully applied to a 3-slice CT imaging protocol to analyze multiple tissues and organs related to BC. The overall best performance was achieved by UNET++, against which Ghost-UNET++ showed competitive results based on a more computationally efficient approach. The use of fully automated segmentation methods can reduce analysis time and provide objective results in large-scale studies of BC.
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Affiliation(s)
- Nouman Ahmad
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.
| | - Robin Strand
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Björn Sparresäter
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | - Sambit Tarai
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | - Elin Lundström
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | - Göran Bergström
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Physiology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - Håkan Ahlström
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
- Antaros Medical, Mölndal, Sweden
| | - Joel Kullberg
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
- Antaros Medical, Mölndal, Sweden
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Álvarez-Zaragoza C, Vásquez-Garibay EM, Sánchez-Ramírez CA. Adiposity and feeding practices in the first two years of life among toddlers in Guadalajara, Mexico. BMC Pediatr 2023; 23:61. [PMID: 36739378 PMCID: PMC9898890 DOI: 10.1186/s12887-023-03877-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 01/31/2023] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Feeding practices in the first two years of life have a direct impact on nutritional status and adiposity. The purpose of this study was to identify the differences in feeding practices during the first two years of life by sex and type of feeding in the first semester of postnatal life and their relationships with adiposity in toddlers. METHODS An analytical cross-sectional study that included 150 toddlers aged 12 to 24 months who were healthy, full-term, and had adequate weight for their gestational ages, was conducted at the New Civil Hospital and at a private practice in Guadalajara. Body compositions were obtained by bioelectrical impedance (BIA) measurements, and a modified questionnaire was used. Then, the parents completed two 24-h dietary recalls. In addition to the descriptive statistics, ANOVA, Kruskal-Wallis and Mann-Whitney U tests were used in the contrast analysis of the quantitative variables. To analyze the qualitative variables, we used X2 tests. Afterward, linear regression tests were conducted to identify the relationships between adiposity and feeding practices during the first two years. RESULTS There were direct relationships between adiposity and duration of full breastfeeding (r = 0.610, p = 0.021), age of introduction of ultra-processed products (r = 0.311, p = 0.011), sugar (r = 0.186; p = 0.024) and age at which eggs were introduced (r = -0.202; p = 0.016). CONCLUSIONS Adiposity was related to feeding practices in the first two years of life in toddlers.
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Affiliation(s)
- Citlalli Álvarez-Zaragoza
- grid.412890.60000 0001 2158 0196Institute of Human Nutrition, University of Guadalajara, Guadalajara, México
| | - Edgar M. Vásquez-Garibay
- grid.412890.60000 0001 2158 0196Institute of Human Nutrition, University of Guadalajara, Guadalajara, México
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A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging. Tomography 2023; 9:139-149. [PMID: 36648999 PMCID: PMC9844424 DOI: 10.3390/tomography9010012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/09/2023] [Accepted: 01/12/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The development of adipose tissue during adolescence may provide valuable insights into obesity-associated diseases. We propose an automated convolutional neural network (CNN) approach using Dixon-based magnetic resonance imaging (MRI) to quantity abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in children and adolescents. METHODS 474 abdominal Dixon MRI scans of 136 young healthy volunteers (aged 8-18) were included in this study. For each scan, an axial fat-only Dixon image located at the L2-L3 disc space and another image at the L4-L5 disc space were selected for quantification. For each image, an outer and an inner region around the abdomen wall, as well as SAT and VAT pixel masks, were generated by expert readers as reference standards. A standard U-Net CNN architecture was then used to train two models: one for region segmentation and one for fat pixel classification. The performance was evaluated using the dice similarity coefficient (DSC) with fivefold cross-validation, and by Pearson correlation and the Student's t-test against the reference standards. RESULTS For the DSC results, means and standard deviations of the outer region, inner region, SAT, and VAT comparisons were 0.974 ± 0.026, 0.997 ± 0.003, 0.981 ± 0.025, and 0.932 ± 0.047, respectively. Pearson coefficients were 1.000 for both outer and inner regions, and 1.000 and 0.982 for SAT and VAT comparisons, respectively (all p = NS). CONCLUSION These results show that our method not only provides excellent agreement with the reference SAT and VAT measurements, but also accurate abdominal wall region segmentation. The proposed combined region- and pixel-based CNN approach provides automated abdominal wall segmentation as well as SAT and VAT quantification with Dixon MRI and enables objective longitudinal assessment of adipose tissues in children during adolescence.
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Lee SB, Cho YJ, Yoon SH, Lee YY, Kim SH, Lee S, Choi YH, Cheon JE. Automated segmentation of whole-body CT images for body composition analysis in pediatric patients using a deep neural network. Eur Radiol 2022; 32:8463-8472. [PMID: 35524785 DOI: 10.1007/s00330-022-08829-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/21/2022] [Accepted: 04/20/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To develop an automatic segmentation algorithm using a deep neural network with transfer learning applicable to whole-body PET-CT images in children. METHODS For model development, we utilized transfer learning with a pre-trained model based on adult patients. We used CT images of 31 pediatric patients under 19 years of age (mean age, 9.6 years) who underwent PET-CT from institution #1 for transfer learning. Two radiologists manually labeled the skin, bone, muscle, abdominal visceral fat, subcutaneous fat, internal organs, and central nervous system in each CT slice and used these as references. For external validation, we collected 14 pediatric PET/CT scans from institution #2 (mean age, 9.1 years). The Dice similarity coefficients (DSCs), sensitivities, and precision were compared between the algorithms before and after transfer learning. In addition, we evaluated segmentation performance according to sex, age (≤ 8 vs. > 8 years), and body mass index (BMI, ≤ 20 vs. > 20 kg/m2). RESULTS The algorithm after transfer learning showed better performance than the algorithm before transfer learning for all body compositions (p < 0.001). The average DSC, sensitivity, and precision of each algorithm before and after transfer learning were 98.23% and 99.28%, 98.16% and 99.28%, and 98.29% and 99.28%, respectively. The segmentation performance of the algorithm was generally not affected by age, sex, or BMI, except for precision in the body muscle compartment. CONCLUSION The developed model with transfer learning enabled accurate and fully automated segmentation of multiple tissues on whole-body CT scans in children. KEY POINTS • We utilized transfer learning with a pre-trained segmentation algorithm for adult to develop an algorithm for automated segmentation of pediatric whole-body CT. • This algorithm showed excellent performance and was not affected by sex, age, or body mass index, except for precision in body muscle.
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Affiliation(s)
- Seul Bi Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Yeon Jin Cho
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,MEDICALIP Co. Ltd., Seoul, Republic of Korea
| | - Yun Young Lee
- Department of Radiology, Chonnam National University Hospital, 42 Jebong-ro, Dong-gu, Gwangju, 61469, Republic of Korea
| | - Soo-Hyun Kim
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Seunghyun Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Young Hun Choi
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Jung-Eun Cheon
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
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