1
|
Yang X, Sullivan PF, Li B, Fan Z, Ding D, Shu J, Guo Y, Paschou P, Bao J, Shen L, Ritchie MD, Nave G, Platt ML, Li T, Zhu H, Zhao B. Multi-organ imaging-derived polygenic indexes for brain and body health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.04.18.23288769. [PMID: 38883759 PMCID: PMC11177904 DOI: 10.1101/2023.04.18.23288769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.
Collapse
Affiliation(s)
- Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Patrick F. Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxuan Li
- UCLA Samueli School of Engineering, Los Angeles, CA 90095, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dezheng Ding
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yuxin Guo
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Peristera Paschou
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marylyn D. Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Gideon Nave
- Marketing Department, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael L. Platt
- Marketing Department, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
2
|
Helgesson S, Tarai S, Langner T, Ahlström H, Johansson L, Kullberg J, Lundström E. Spleen volume is independently associated with non-alcoholic fatty liver disease, liver volume and liver fibrosis. Heliyon 2024; 10:e28123. [PMID: 38665588 PMCID: PMC11043861 DOI: 10.1016/j.heliyon.2024.e28123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 03/12/2024] [Accepted: 03/12/2024] [Indexed: 04/28/2024] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) can lead to irreversible liver damage manifesting in systemic effects (e.g., elevated portal vein pressure and splenomegaly) with increased risk of deadly outcomes. However, the association of spleen volume with NAFLD and related type 2-diabetes (T2D) is not fully understood. The UK Biobank contains comprehensive health-data of 500,000 participants, including clinical data and MR images of >40,000 individuals. The present study estimated the spleen volume of 37,066 participants through automated deep learning-based image segmentation of neck-to-knee MR images. The aim was to investigate the associations of spleen volume with NAFLD, T2D and liver fibrosis, while adjusting for natural confounders. The recent redefinition and new designation of NAFLD to metabolic dysfunction-associated steatotic liver disease (MASLD), promoted by major organisations of studies on liver disease, was not employed as introduced after the conduct of this study. The results showed that spleen volume decreased with age, correlated positively with body size and was smaller in females compared to males. Larger spleens were observed in subjects with NAFLD and T2D compared to controls. Spleen volume was also positively and independently associated with liver fat fraction, liver volume and the fibrosis-4 score, with notable volumetric increases already at low liver fat fractions and volumes, but not independently associated with T2D. These results suggest a link between spleen volume and NAFLD already at an early stage of the disease, potentially due to initial rise in portal vein pressure.
Collapse
Affiliation(s)
- Samuel Helgesson
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
| | - Sambit Tarai
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
- Antaros Medical AB, BioVenture Hub, Sweden
| | | | - Håkan Ahlström
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
- Antaros Medical AB, BioVenture Hub, Sweden
| | | | - Joel Kullberg
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
- Antaros Medical AB, BioVenture Hub, Sweden
| | - Elin Lundström
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
| |
Collapse
|
3
|
Bane O, Seeliger E, Cox E, Stabinska J, Bechler E, Lewis S, Hickson LJ, Francis S, Sigmund E, Niendorf T. Renal MRI: From Nephron to NMR Signal. J Magn Reson Imaging 2023; 58:1660-1679. [PMID: 37243378 PMCID: PMC11025392 DOI: 10.1002/jmri.28828] [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: 02/03/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
Renal diseases pose a significant socio-economic burden on healthcare systems. The development of better diagnostics and prognostics is well-recognized as a key strategy to resolve these challenges. Central to these developments are MRI biomarkers, due to their potential for monitoring of early pathophysiological changes, renal disease progression or treatment effects. The surge in renal MRI involves major cross-domain initiatives, large clinical studies, and educational programs. In parallel with these translational efforts, the need for greater (patho)physiological specificity remains, to enable engagement with clinical nephrologists and increase the associated health impact. The ISMRM 2022 Member Initiated Symposium (MIS) on renal MRI spotlighted this issue with the goal of inspiring more solutions from the ISMRM community. This work is a summary of the MIS presentations devoted to: 1) educating imaging scientists and clinicians on renal (patho)physiology and demands from clinical nephrologists, 2) elucidating the connection of MRI parameters with renal physiology, 3) presenting the current state of leading MR surrogates in assessing renal structure and functions as well as their next generation of innovation, and 4) describing the potential of these imaging markers for providing clinically meaningful renal characterization to guide or supplement clinical decision making. We hope to continue momentum of recent years and introduce new entrants to the development process, connecting (patho)physiology with (bio)physics, and conceiving new clinical applications. We envision this process to benefit from cross-disciplinary collaboration and analogous efforts in other body organs, but also to maximally leverage the unique opportunities of renal physiology. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2.
Collapse
Affiliation(s)
- Octavia Bane
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
- Icahn School of Medicine at Mount Sinai, BioMedical Engineering and Imaging Institute, New York City, New York, USA
| | - Erdmann Seeliger
- Institute of Translational Physiology, Charité-University Medicine Berlin, Berlin, Germany
| | - Eleanor Cox
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Julia Stabinska
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Eric Bechler
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sara Lewis
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - LaTonya J Hickson
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USA
| | - Sue Francis
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Eric Sigmund
- Bernard and Irene Schwartz Center for Biomedical Imaging Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Health, New York City, New York, USA
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| |
Collapse
|
4
|
Gatidis S, Kart T, Fischer M, Winzeck S, Glocker B, Bai W, Bülow R, Emmel C, Friedrich L, Kauczor HU, Keil T, Kröncke T, Mayer P, Niendorf T, Peters A, Pischon T, Schaarschmidt BM, Schmidt B, Schulze MB, Umutle L, Völzke H, Küstner T, Bamberg F, Schölkopf B, Rueckert D. Better Together: Data Harmonization and Cross-Study Analysis of Abdominal MRI Data From UK Biobank and the German National Cohort. Invest Radiol 2023; 58:346-354. [PMID: 36729536 PMCID: PMC10090309 DOI: 10.1097/rli.0000000000000941] [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: 09/15/2022] [Accepted: 10/28/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES The UK Biobank (UKBB) and German National Cohort (NAKO) are among the largest cohort studies, capturing a wide range of health-related data from the general population, including comprehensive magnetic resonance imaging (MRI) examinations. The purpose of this study was to demonstrate how MRI data from these large-scale studies can be jointly analyzed and to derive comprehensive quantitative image-based phenotypes across the general adult population. MATERIALS AND METHODS Image-derived features of abdominal organs (volumes of liver, spleen, kidneys, and pancreas; volumes of kidney hilum adipose tissue; and fat fractions of liver and pancreas) were extracted from T1-weighted Dixon MRI data of 17,996 participants of UKBB and NAKO based on quality-controlled deep learning generated organ segmentations. To enable valid cross-study analysis, we first analyzed the data generating process using methods of causal discovery. We subsequently harmonized data from UKBB and NAKO using the ComBat approach for batch effect correction. We finally performed quantile regression on harmonized data across studies providing quantitative models for the variation of image-derived features stratified for sex and dependent on age, height, and weight. RESULTS Data from 8791 UKBB participants (49.9% female; age, 63 ± 7.5 years) and 9205 NAKO participants (49.1% female, age: 51.8 ± 11.4 years) were analyzed. Analysis of the data generating process revealed direct effects of age, sex, height, weight, and the data source (UKBB vs NAKO) on image-derived features. Correction of data source-related effects resulted in markedly improved alignment of image-derived features between UKBB and NAKO. Cross-study analysis on harmonized data revealed comprehensive quantitative models for the phenotypic variation of abdominal organs across the general adult population. CONCLUSIONS Cross-study analysis of MRI data from UKBB and NAKO as proposed in this work can be helpful for future joint data analyses across cohorts linking genetic, environmental, and behavioral risk factors to MRI-derived phenotypes and provide reference values for clinical diagnostics.
Collapse
Affiliation(s)
- Sergios Gatidis
- From the Empirical Inference Department, Max-Planck Institute for Intelligent Systems
- Medical Image and Data Analysis Lab, Department of Radiology, University Hospital Tübingen, Tübingen, Germany
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Turkay Kart
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Marc Fischer
- Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
| | - Stefan Winzeck
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Ben Glocker
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Wenjia Bai
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald
| | - Carina Emmel
- Institute for Medical Informatics, Biometry, and Epidemiology, University Hospital of Essen, Essen
| | - Lena Friedrich
- Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg
| | - Hans-Ulrich Kauczor
- Clinic for Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg
| | - Thomas Keil
- Institute of Social Medicine, Epidemiology and Health Economics, Charité–Universitätsmedizin Berlin, Berlin
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg
- State Institute of Health, Bavarian Health and Food Safety Authority, Erlangen
| | - Thomas Kröncke
- Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg
| | - Philipp Mayer
- Clinic for Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Berlin
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg
- Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich
- German Diabetes Center (DZD e.V.—Partner site Munich), Neuherberg
| | - Tobias Pischon
- Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Molecular Epidemiology Research Group
- Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Biobank Technology Platform
- Berlin Institute of Health at Charité–Universitätsmedizin Berlin, Core Facility Biobank
- Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin
| | - Benedikt M. Schaarschmidt
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen
| | - Börge Schmidt
- Institute for Medical Informatics, Biometry, and Epidemiology, University Hospital of Essen, Essen
| | - Matthias B. Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke
- Institute of Nutritional Science, University of Potsdam, Nuthetal
| | - Lale Umutle
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald
| | - Thomas Küstner
- Medical Image and Data Analysis Lab, Department of Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Fabian Bamberg
- Department of Radiology, University Hospital Freiburg, Freiburg
| | - Bernhard Schölkopf
- From the Empirical Inference Department, Max-Planck Institute for Intelligent Systems
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
- Institute for AI and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| |
Collapse
|
5
|
Anush A, Rohini G, Nicola S, WalaaEldin EM, Eranga U. Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images. J Med Imaging (Bellingham) 2023; 10:024501. [PMID: 36950139 PMCID: PMC10026851 DOI: 10.1117/1.jmi.10.2.024501] [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: 05/18/2022] [Accepted: 02/22/2023] [Indexed: 03/24/2023] Open
Abstract
Purpose Accurate detection of small renal masses (SRM) is a fundamental step for automated classification of benign and malignant or indolent and aggressive renal tumors. Magnetic resonance image (MRI) may outperform computed tomography (CT) for SRM subtype differentiation due to improved tissue characterization, but is less explored compared to CT. The objective of this study is to autonomously detect SRM on contrast-enhanced magnetic resonance images (CE-MRI). Approach In this paper, we described a novel, fully automated methodology for accurate detection and localization of SRM on CE-MRI. We first determine the kidney boundaries using a U-Net convolutional neural network. We then search for SRM within the localized kidney regions using a mixture-of-experts ensemble model based on the U-Net architecture. Our dataset contained CE-MRI scans of 118 patients with different solid kidney tumor subtypes including renal cell carcinomas, oncocytomas, and fat-poor renal angiomyolipoma. We evaluated the proposed model on the entire CE-MRI dataset using 5-fold cross validation. Results The developed algorithm reported a Dice similarity coefficient of 91.20 ± 5.41 % (mean ± standard deviation) for kidney segmentation from 118 volumes consisting of 25,025 slices. Our proposed ensemble model for SRM detection yielded a recall and precision of 86.2% and 83.3% on the entire CE-MRI dataset, respectively. Conclusions We described a deep-learning-based method for fully automated SRM detection using CE-MR images, which has not been studied previously. The results are clinically important as SRM localization is a pre-step for fully automated diagnosis of SRM subtypes.
Collapse
Affiliation(s)
- Agarwal Anush
- University of Guelph, School of Engineering, Guelph, Ontario, Canada
| | - Gaikar Rohini
- University of Guelph, School of Engineering, Guelph, Ontario, Canada
| | - Schieda Nicola
- University of Ottawa, Department of Radiology, Ottawa, Ontario, Canada
| | | | - Ukwatta Eranga
- University of Guelph, School of Engineering, Guelph, Ontario, Canada
| |
Collapse
|
6
|
Basty N, Thanaj M, Cule M, Sorokin EP, Liu Y, Thomas EL, Bell JD, Whitcher B. Artifact-free fat-water separation in Dixon MRI using deep learning. JOURNAL OF BIG DATA 2023; 10:4. [PMID: 36686622 PMCID: PMC9835035 DOI: 10.1186/s40537-022-00677-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 12/25/2022] [Indexed: 06/17/2023]
Abstract
Chemical-shift encoded MRI (CSE-MRI) is a widely used technique for the study of body composition and metabolic disorders, where derived fat and water signals enable the quantification of adipose tissue and muscle. The UK Biobank is acquiring whole-body Dixon MRI (a specific implementation of CSE-MRI) for over 100,000 participants. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed by the scanner, making quantitative analysis challenging. The most common artifacts are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which is wasteful and may lead to unintended biases. Given the large number of whole-body Dixon MRI acquisitions in the UK Biobank, thousands of swaps are expected to be present in the fat and water volumes from image reconstruction performed on the scanner. If they go undetected, errors will propagate into processes such as organ segmentation, and dilute the results in population-based analyses. There is a clear need for a robust method to accurately separate fat and water volumes in big data collections like the UK Biobank. We formulate fat-water separation as a style transfer problem, where swap-free fat and water volumes are predicted from the acquired Dixon MRI data using a conditional generative adversarial network, and introduce a new loss function for the generator model. Our method is able to predict highly accurate fat and water volumes free from artifacts in the UK Biobank. We show that our model separates fat and water volumes using either single input (in-phase only) or dual input (in-phase and opposed-phase) data, with the latter producing superior results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-022-00677-1.
Collapse
Affiliation(s)
- Nicolas Basty
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Marjola Thanaj
- Research Centre for Optimal Health, University of Westminster, London, UK
| | | | | | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, USA
| | - E. Louise Thomas
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Jimmy D. Bell
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Brandon Whitcher
- Research Centre for Optimal Health, University of Westminster, London, UK
| |
Collapse
|
7
|
Anari PY, Lay N, Chaurasia A, Gopal N, Samimi S, Harmon S, Gautam R, Ma K, Firouzabadi FD, Turkbey E, Merino M, Jones EC, Ball MW, Linehan WM, Turkbey B, Malayeri AA. Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images. ARXIV 2023:arXiv:2301.02538v1. [PMID: 36789136 PMCID: PMC9928055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
We demonstrate automated segmentation of clear cell renal cell carcinomas (ccRCC), cysts, and surrounding normal kidney parenchyma in patients with von Hippel-Lindau (VHL) syndrome using convolutional neural networks (CNN) on Magnetic Resonance Imaging (MRI). We queried 115 VHL patients and 117 scans (3 patients have two separate scans) with 504 ccRCCs and 1171 cysts from 2015 to 2021. Lesions were manually segmented on T1 excretory phase, co-registered on all contrast-enhanced T1 sequences and used to train 2D and 3D U-Net. The U-Net performance was evaluated on 10 randomized splits of the cohort. The models were evaluated using the dice similarity coefficient (DSC). Our 2D U-Net achieved an average ccRCC lesion detection Area under the curve (AUC) of 0.88 and DSC scores of 0.78, 0.40, and 0.46 for segmentation of the kidney, cysts, and tumors, respectively. Our 3D U-Net achieved an average ccRCC lesion detection AUC of 0.79 and DSC scores of 0.67, 0.32, and 0.34 for kidney, cysts, and tumors, respectively. We demonstrated good detection and moderate segmentation results using U-Net for ccRCC on MRI. Automatic detection and segmentation of normal renal parenchyma, cysts, and masses may assist radiologists in quantifying the burden of disease in patients with VHL.
Collapse
Affiliation(s)
- Pouria Yazdian Anari
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Nathan Lay
- Artificial Intelligence Resource, National Institutes of Health, USA
| | - Aditi Chaurasia
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Nikhil Gopal
- Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA
| | - Safa Samimi
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Stephanie Harmon
- Artificial Intelligence Resource, National Institutes of Health, USA
| | - Rabindra Gautam
- Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA
| | - Kevin Ma
- Artificial Intelligence Resource, National Institutes of Health, USA
| | | | - Evrim Turkbey
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Maria Merino
- Pathology Department, National Cancer Institutes, National Institutes of Health, USA
| | - Elizabeth C. Jones
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Mark W. Ball
- Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA
| | - W. Marston Linehan
- Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA
| | - Baris Turkbey
- Artificial Intelligence Resource, National Institutes of Health, USA
| | - Ashkan A. Malayeri
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| |
Collapse
|
8
|
Fang L, Wang X. Multi-input Unet model based on the integrated block and the aggregation connection for MRI brain tumor segmentation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
9
|
Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies. Sci Rep 2022; 12:18733. [PMID: 36333523 PMCID: PMC9636393 DOI: 10.1038/s41598-022-23632-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 11/02/2022] [Indexed: 11/06/2022] Open
Abstract
Large epidemiological studies such as the UK Biobank (UKBB) or German National Cohort (NAKO) provide unprecedented health-related data of the general population aiming to better understand determinants of health and disease. As part of these studies, Magnetic Resonance Imaging (MRI) is performed in a subset of participants allowing for phenotypical and functional characterization of different organ systems. Due to the large amount of imaging data, automated image analysis is required, which can be performed using deep learning methods, e. g. for automated organ segmentation. In this paper we describe a computational pipeline for automated segmentation of abdominal organs on MRI data from 20,000 participants of UKBB and NAKO and provide results of the quality control process. We found that approx. 90% of data sets showed no relevant segmentation errors while relevant errors occurred in a varying proportion of data sets depending on the organ of interest. Image-derived features based on automated organ segmentations showed relevant deviations of varying degree in the presence of segmentation errors. These results show that large-scale, deep learning-based abdominal organ segmentation on MRI data is feasible with overall high accuracy, but visual quality control remains an important step ensuring the validity of down-stream analyses in large epidemiological imaging studies.
Collapse
|
10
|
Langner T, Martínez Mora A, Strand R, Ahlström H, Kullberg J. MIMIR: Deep Regression for Automated Analysis of UK Biobank MRI Scans. Radiol Artif Intell 2022; 4:e210178. [PMID: 35652115 PMCID: PMC9152682 DOI: 10.1148/ryai.210178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 02/25/2022] [Accepted: 03/23/2022] [Indexed: 11/11/2022]
Abstract
UK Biobank (UKB) has recruited more than 500 000 volunteers from the United Kingdom, collecting health-related information on genetics, lifestyle, blood biochemistry, and more. Ongoing medical imaging of 100 000 participants with 70 000 follow-up sessions will yield up to 170 000 MRI scans, enabling image analysis of body composition, organs, and muscle. This study presents an experimental inference engine for automated analysis of UKB neck-to-knee body 1.5-T MRI scans. This retrospective cross-validation study includes data from 38 916 participants (52% female; mean age, 64 years) to capture baseline characteristics, such as age, height, weight, and sex, as well as measurements of body composition, organ volumes, and abstract properties, such as grip strength, pulse rate, and type 2 diabetes status. Prediction intervals for each end point were generated based on uncertainty quantification. On a subsequent release of UKB data, the proposed method predicted 12 body composition metrics with a 3% median error and yielded mostly well-calibrated individual prediction intervals. The processing of MRI scans from 1000 participants required 10 minutes. The underlying method used convolutional neural networks for image-based mean-variance regression on two-dimensional representations of the MRI data. An implementation was made publicly available for fast and fully automated estimation of 72 different measurements from future releases of UKB image data. Keywords: MRI, Adipose Tissue, Obesity, Metabolic Disorders, Volume Analysis, Whole-Body Imaging, Quantification, Supervised Learning, Convolutional Neural Network (CNN) © RSNA, 2022.
Collapse
Affiliation(s)
- Taro Langner
- From the Departments of Surgical Sciences (T.L., A.M.M., R.S., H.A.,
J.K.) and Information Technology (R.S.), Uppsala University, Akademiska
sjukhuset, ingång 78, 1tr, 751 85 Uppsala, Sweden; and Antaros
Medical AB, Mölndal, Sweden (H.A., J.K.)
| | - Andrés Martínez Mora
- From the Departments of Surgical Sciences (T.L., A.M.M., R.S., H.A.,
J.K.) and Information Technology (R.S.), Uppsala University, Akademiska
sjukhuset, ingång 78, 1tr, 751 85 Uppsala, Sweden; and Antaros
Medical AB, Mölndal, Sweden (H.A., J.K.)
| | - Robin Strand
- From the Departments of Surgical Sciences (T.L., A.M.M., R.S., H.A.,
J.K.) and Information Technology (R.S.), Uppsala University, Akademiska
sjukhuset, ingång 78, 1tr, 751 85 Uppsala, Sweden; and Antaros
Medical AB, Mölndal, Sweden (H.A., J.K.)
| | - Håkan Ahlström
- From the Departments of Surgical Sciences (T.L., A.M.M., R.S., H.A.,
J.K.) and Information Technology (R.S.), Uppsala University, Akademiska
sjukhuset, ingång 78, 1tr, 751 85 Uppsala, Sweden; and Antaros
Medical AB, Mölndal, Sweden (H.A., J.K.)
| | - Joel Kullberg
- From the Departments of Surgical Sciences (T.L., A.M.M., R.S., H.A.,
J.K.) and Information Technology (R.S.), Uppsala University, Akademiska
sjukhuset, ingång 78, 1tr, 751 85 Uppsala, Sweden; and Antaros
Medical AB, Mölndal, Sweden (H.A., J.K.)
| |
Collapse
|
11
|
Klepaczko A, Majos M, Stefańczyk L, Ejkefjord E, Lundervold A. Whole kidney and renal cortex segmentation in contrast-enhanced MRI using a joint classification and segmentation convolutional neural network. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
|
12
|
Langner T, Gustafsson FK, Avelin B, Strand R, Ahlström H, Kullberg J. Uncertainty-aware body composition analysis with deep regression ensembles on UK Biobank MRI. Comput Med Imaging Graph 2021; 93:101994. [PMID: 34624770 DOI: 10.1016/j.compmedimag.2021.101994] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 09/06/2021] [Accepted: 09/07/2021] [Indexed: 11/25/2022]
Abstract
Along with rich health-related metadata, medical images have been acquired for over 40,000 male and female UK Biobank participants, aged 44-82, since 2014. Phenotypes derived from these images, such as measurements of body composition from MRI, can reveal new links between genetics, cardiovascular disease, and metabolic conditions. In this work, six measurements of body composition and adipose tissues were automatically estimated by image-based, deep regression with ResNet50 neural networks from neck-to-knee body MRI. Despite the potential for high speed and accuracy, these networks produce no output segmentations that could indicate the reliability of individual measurements. The presented experiments therefore examine uncertainty quantification with mean-variance regression and ensembling to estimate individual measurement errors and thereby identify potential outliers, anomalies, and other failure cases automatically. In 10-fold cross-validation on data of about 8500 subjects, mean-variance regression and ensembling showed complementary benefits, reducing the mean absolute error across all predictions by 12%. Both improved the calibration of uncertainties and their ability to identify high prediction errors. With intra-class correlation coefficients (ICC) above 0.97, all targets except the liver fat content yielded relative measurement errors below 5%. Testing on another 1000 subjects showed consistent performance, and the method was finally deployed for inference to 30,000 subjects with missing reference values. The results indicate that deep regression ensembles could ultimately provide automated, uncertainty-aware measurements of body composition for more than 120,000 UK Biobank neck-to-knee body MRI that are to be acquired within the coming years.
Collapse
Affiliation(s)
- Taro Langner
- Uppsala University, Department of Surgical Sciences, Uppsala, Sweden.
| | | | - Benny Avelin
- Uppsala University, Department of Mathematics, Uppsala, Sweden
| | - Robin Strand
- Uppsala University, Department of Information Technology, Uppsala, Sweden
| | - Håkan Ahlström
- Uppsala University, Department of Surgical Sciences, Uppsala, Sweden; Antaros Medical AB, BioVenture Hub, Mölndal, Sweden
| | - Joel Kullberg
- Uppsala University, Department of Surgical Sciences, Uppsala, Sweden; Antaros Medical AB, BioVenture Hub, Mölndal, Sweden
| |
Collapse
|
13
|
Gladytz T, Millward JM, Cantow K, Hummel L, Zhao K, Flemming B, Periquito JS, Pohlmann A, Waiczies S, Seeliger E, Niendorf T. Reliable kidney size determination by magnetic resonance imaging in pathophysiological settings. Acta Physiol (Oxf) 2021; 233:e13701. [PMID: 34089569 DOI: 10.1111/apha.13701] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 05/05/2021] [Accepted: 06/01/2021] [Indexed: 12/24/2022]
Abstract
AIM Kidney diseases constitute a major health challenge, which requires noninvasive imaging to complement conventional approaches to diagnosis and monitoring. Several renal pathologies are associated with changes in kidney size, offering an opportunity for magnetic resonance imaging (MRI) biomarkers of disease. This work uses dynamic MRI and an automated bean-shaped model (ABSM) for longitudinal quantification of pathophysiologically relevant changes in kidney size. METHODS A geometry-based ABSM was developed for kidney size measurements in rats using parametric MRI (T2 , T2 * mapping). The ABSM approach was applied to longitudinal renal size quantification using occlusion of the (a) suprarenal aorta or (b) the renal vein, (c) increase in renal pelvis and intratubular pressure and (d) injection of an X-ray contrast medium into the thoracic aorta to induce pathophysiologically relevant changes in kidney size. RESULTS The ABSM yielded renal size measurements with accuracy and precision equivalent to the manual segmentation, with >70-fold time savings. The automated method could detect a ~7% reduction (aortic occlusion) and a ~5%, a ~2% and a ~6% increase in kidney size (venous occlusion, pelvis and intratubular pressure increase and injection of X-ray contrast medium, respectively). These measurements were not affected by reduced image quality following administration of ferumoxytol. CONCLUSION Dynamic MRI in conjunction with renal segmentation using an ABSM supports longitudinal quantification of changes in kidney size in pathophysiologically relevant experimental setups mimicking realistic clinical scenarios. This can potentially be instrumental for developing MRI-based diagnostic tools for various kidney disorders and for gaining new insight into mechanisms of renal pathophysiology.
Collapse
Affiliation(s)
- Thomas Gladytz
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Jason M Millward
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Kathleen Cantow
- Institute of Physiology, Charité-Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Luis Hummel
- Institute of Physiology, Charité-Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Kaixuan Zhao
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Bert Flemming
- Institute of Physiology, Charité-Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Joāo S Periquito
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.,Institute of Physiology, Charité-Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Andreas Pohlmann
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Sonia Waiczies
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Erdmann Seeliger
- Institute of Physiology, Charité-Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.,Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| |
Collapse
|