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Zhu C, He X, Blumenfeld JD, Hu Z, Dev H, Sattar U, Bazojoo V, Sharbatdaran A, Aspal M, Romano D, Teichman K, Ng He HY, Wang Y, Soto Figueroa A, Weiss E, Prince AG, Chevalier JM, Shimonov D, Moghadam MC, Sabuncu M, Prince MR. A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression. Biomedicines 2024; 12:1133. [PMID: 38791095 PMCID: PMC11118119 DOI: 10.3390/biomedicines12051133] [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: 04/11/2024] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
Abdominal imaging of autosomal dominant polycystic kidney disease (ADPKD) has historically focused on detecting complications such as cyst rupture, cyst infection, obstructing renal calculi, and pyelonephritis; discriminating complex cysts from renal cell carcinoma; and identifying sources of abdominal pain. Many imaging features of ADPKD are incompletely evaluated or not deemed to be clinically significant, and because of this, treatment options are limited. However, total kidney volume (TKV) measurement has become important for assessing the risk of disease progression (i.e., Mayo Imaging Classification) and predicting tolvaptan treatment's efficacy. Deep learning for segmenting the kidneys has improved these measurements' speed, accuracy, and reproducibility. Deep learning models can also segment other organs and tissues, extracting additional biomarkers to characterize the extent to which extrarenal manifestations complicate ADPKD. In this concept paper, we demonstrate how deep learning may be applied to measure the TKV and how it can be extended to measure additional features of this disease.
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Affiliation(s)
- Chenglin Zhu
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Xinzi He
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USA
- Cornell Tech, Cornell University, Ithaca, NY 10044, USA
| | - Jon D. Blumenfeld
- The Rogosin Institute, New York, NY 10021, USA; (J.D.B.); (J.M.C.); (D.S.)
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Zhongxiu Hu
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Hreedi Dev
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Usama Sattar
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Vahid Bazojoo
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Arman Sharbatdaran
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Mohit Aspal
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Dominick Romano
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Kurt Teichman
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Hui Yi Ng He
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Yin Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Andrea Soto Figueroa
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Erin Weiss
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Anna G. Prince
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - James M. Chevalier
- The Rogosin Institute, New York, NY 10021, USA; (J.D.B.); (J.M.C.); (D.S.)
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Daniil Shimonov
- The Rogosin Institute, New York, NY 10021, USA; (J.D.B.); (J.M.C.); (D.S.)
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Mina C. Moghadam
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Mert Sabuncu
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
- Cornell Tech, Cornell University, Ithaca, NY 10044, USA
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Martin R. Prince
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
- Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA
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Klein T, Gladytz T, Millward JM, Cantow K, Hummel L, Seeliger E, Waiczies S, Lippert C, Niendorf T. Dynamic parametric MRI and deep learning: Unveiling renal pathophysiology through accurate kidney size quantification. NMR IN BIOMEDICINE 2024; 37:e5075. [PMID: 38043545 DOI: 10.1002/nbm.5075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/22/2023] [Accepted: 10/19/2023] [Indexed: 12/05/2023]
Abstract
Renal pathologies often manifest as alterations in kidney size, providing a valuable avenue for employing dynamic parametric MRI as a means to derive kidney size measurements for the diagnosis, treatment, and monitoring of renal disease. Furthermore, this approach holds significant potential in supporting MRI data-driven preclinical investigations into the intricate mechanisms underlying renal pathophysiology. The integration of deep learning algorithms is crucial in achieving rapid and precise segmentation of the kidney from temporally resolved parametric MRI, facilitating the use of kidney size as a meaningful (pre)clinical biomarker for renal disease. To explore this potential, we employed dynamic parametric T2 mapping of the kidney in rats in conjunction with a custom-tailored deep dilated U-Net (DDU-Net) architecture. The architecture was trained, validated, and tested on manually segmented ground truth kidney data, with benchmarking against an analytical segmentation model and a self-configuring no new U-Net. Subsequently, we applied our approach to in vivo longitudinal MRI data, incorporating interventions that emulate clinically relevant scenarios in rats. Our approach achieved high performance metrics, including a Dice coefficient of 0.98, coefficient of determination of 0.92, and a mean absolute percentage error of 1.1% compared with ground truth. The DDU-Net enabled automated and accurate quantification of acute changes in kidney size, such as aortic occlusion (-8% ± 1%), venous occlusion (5% ± 1%), furosemide administration (2% ± 1%), hypoxemia (-2% ± 1%), and contrast agent-induced acute kidney injury (11% ± 1%). This approach can potentially be instrumental for the development of dynamic parametric MRI-based tools for kidney disorders, offering unparalleled insights into renal pathophysiology.
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Affiliation(s)
- Tobias Klein
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Digital Health - Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
| | - 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 Translational Physiology, Charité - Universitätsmedizin, Berlin, Germany
| | - Luis Hummel
- Institute of Translational Physiology, Charité - Universitätsmedizin, Berlin, Germany
| | - Erdmann Seeliger
- Institute of Translational Physiology, Charité - Universitätsmedizin, Berlin, Germany
| | - Sonia Waiczies
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Christoph Lippert
- Digital Health - Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, 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
- Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine, Berlin, Germany
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Wigerinck S, Gregory AV, Smith BH, Iliuta IA, Hanna C, Chedid M, Kaidbay HDN, Senum SR, Shukoor S, Harris PC, Torres VE, Kline TL, Chebib FT. Evaluation of advanced imaging biomarkers at kidney failure in patients with ADPKD: a pilot study. Clin Kidney J 2023; 16:1691-1700. [PMID: 37779848 PMCID: PMC10539251 DOI: 10.1093/ckj/sfad114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Indexed: 10/03/2023] Open
Abstract
Background Autosomal dominant polycystic kidney disease (ADPKD) presents with variable disease severity and progression. Advanced imaging biomarkers may provide insights into cystic and non-cystic processes leading to kidney failure in different age groups. Methods This pilot study included 39 ADPKD patients with kidney failure, stratified into three age groups (<46, 46-56, >56 years old). Advanced imaging biomarkers were assessed using an automated instance cyst segmentation tool. The biomarkers were compared with an age- and sex-matched ADPKD cohort in early chronic kidney disease (CKD). Results Ht-total parenchymal volume correlated negatively with age at kidney failure. The median Ht-total parenchymal volume was significantly lower in patients older than 56 years. Cystic burden was significantly higher at time of kidney failure, especially in patients who reached it before age 46 years. The cyst index at kidney failure was comparable across age groups and Mayo Imaging Classes. Advanced imaging biomarkers showed higher correlation with Ht-total kidney volume in early CKD than at kidney failure. Cyst index and parenchymal index were relatively stable over 5 years prior to kidney failure, whereas Ht-total cyst volume and cyst parenchymal surface area increased significantly. Conclusion Age-related differences in advanced imaging biomarkers suggest variable pathophysiological mechanisms in ADPKD patients with kidney failure. Further studies are needed to validate the utility of these biomarkers in predicting disease progression and guiding treatment strategies.
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Affiliation(s)
- Stijn Wigerinck
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
- Faculty of Medicine, Catholic University of Leuven, Leuven, Belgium
| | | | - Byron H Smith
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Ioan-Andrei Iliuta
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL, USA
| | - Christian Hanna
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
- Division of Pediatric Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Maroun Chedid
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | | | - Sarah R Senum
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA
| | - Shebaz Shukoor
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Peter C Harris
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA
| | - Vicente E Torres
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | | | - Fouad T Chebib
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL, USA
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Caroli A, Kline TL. Abdominal Imaging in ADPKD: Beyond Total Kidney Volume. J Clin Med 2023; 12:5133. [PMID: 37568535 PMCID: PMC10420262 DOI: 10.3390/jcm12155133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 08/02/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
In the context of autosomal dominant polycystic kidney disease (ADPKD), measurement of the total kidney volume (TKV) is crucial. It acts as a marker for tracking disease progression, and evaluating the effectiveness of treatment strategies. The TKV has also been recognized as an enrichment biomarker and a possible surrogate endpoint in clinical trials. Several imaging modalities and methods are available to calculate the TKV, and the choice depends on the purpose of use. Technological advancements have made it possible to accurately assess the cyst burden, which can be crucial to assessing the disease state and helping to identify rapid progressors. Moreover, the development of automated algorithms has increased the efficiency of total kidney and cyst volume measurements. Beyond these measurements, the quantification and characterization of non-cystic kidney tissue shows potential for stratifying ADPKD patients early on, monitoring disease progression, and possibly predicting renal function loss. A broad spectrum of radiological imaging techniques are available to characterize the kidney tissue, showing promise when it comes to non-invasively picking up the early signs of ADPKD progression. Radiomics have been used to extract textural features from ADPKD images, providing valuable information about the heterogeneity of the cystic and non-cystic components. This review provides an overview of ADPKD imaging biomarkers, focusing on the quantification methods, potential, and necessary steps toward a successful translation to clinical practice.
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Affiliation(s)
- Anna Caroli
- Bioengineering Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 24020 Ranica, BG, Italy
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Potretzke TA, Korfiatis P, Blezek DJ, Edwards ME, Klug JR, Cook CJ, Gregory AV, Harris PC, Chebib FT, Hogan MC, Torres VE, Bolan CW, Sandrasegaran K, Kawashima A, Collins JD, Takahashi N, Hartman RP, Williamson EE, King BF, Callstrom MR, Erickson BJ, Kline TL. Clinical Implementation of an Artificial Intelligence Algorithm for Magnetic Resonance-Derived Measurement of Total Kidney Volume. Mayo Clin Proc 2023; 98:689-700. [PMID: 36931980 PMCID: PMC10159957 DOI: 10.1016/j.mayocp.2022.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 12/09/2022] [Accepted: 12/29/2022] [Indexed: 03/18/2023]
Abstract
OBJECTIVE To evaluate the performance of an internally developed and previously validated artificial intelligence (AI) algorithm for magnetic resonance (MR)-derived total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) when implemented in clinical practice. PATIENTS AND METHODS The study included adult patients with ADPKD seen by a nephrologist at our institution between November 2019 and January 2021 and undergoing an MR imaging examination as part of standard clinical care. Thirty-three nephrologists ordered MR imaging, requesting AI-based TKV calculation for 170 cases in these 161 unique patients. We tracked implementation and performance of the algorithm over 1 year. A radiologist and a radiology technologist reviewed all cases (N=170) for quality and accuracy. Manual editing of algorithm output occurred at radiology or radiology technologist discretion. Performance was assessed by comparing AI-based and manually edited segmentations via measures of similarity and dissimilarity to ensure expected performance. We analyzed ADPKD severity class assignment of algorithm-derived vs manually edited TKV to assess impact. RESULTS Clinical implementation was successful. Artificial intelligence algorithm-based segmentation showed high levels of agreement and was noninferior to interobserver variability and other methods for determining TKV. Of manually edited cases (n=84), the AI-algorithm TKV output showed a small mean volume difference of -3.3%. Agreement for disease class between AI-based and manually edited segmentation was high (five cases differed). CONCLUSION Performance of an AI algorithm in real-life clinical practice can be preserved if there is careful development and validation and if the implementation environment closely matches the development conditions.
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Affiliation(s)
| | | | | | | | - Jason R Klug
- Department of Radiology and Mayo Clinic, Rochester, MN, USA
| | - Cole J Cook
- Department of Radiology and Mayo Clinic, Rochester, MN, USA
| | | | - Peter C Harris
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Fouad T Chebib
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Marie C Hogan
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Vicente E Torres
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | | | | | | | - Bernard F King
- Department of Radiology and Mayo Clinic, Rochester, MN, USA
| | | | | | - Timothy L Kline
- Department of Radiology and Mayo Clinic, Rochester, MN, USA; Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA.
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Devlin L, Dhondurao Sudhindar P, Sayer JA. Renal ciliopathies: promising drug targets and prospects for clinical trials. Expert Opin Ther Targets 2023; 27:325-346. [PMID: 37243567 DOI: 10.1080/14728222.2023.2218616] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/12/2023] [Accepted: 05/23/2023] [Indexed: 05/29/2023]
Abstract
INTRODUCTION Renal ciliopathies represent a collection of genetic disorders characterized by deficiencies in the biogenesis, maintenance, or functioning of the ciliary complex. These disorders, which encompass autosomal dominant polycystic kidney disease (ADPKD), autosomal recessive polycystic kidney disease (ARPKD), and nephronophthisis (NPHP), typically result in cystic kidney disease, renal fibrosis, and a gradual deterioration of kidney function, culminating in kidney failure. AREAS COVERED Here we review the advances in basic science and clinical research into renal ciliopathies which have yielded promising small compounds and drug targets, within both preclinical studies and clinical trials. EXPERT OPINION Tolvaptan is currently the sole approved treatment option available for ADPKD patients, while no approved treatment alternatives exist for ARPKD or NPHP patients. Clinical trials are presently underway to evaluate additional medications in ADPKD and ARPKD patients. Based on preclinical models, other potential therapeutic targets for ADPKD, ARPKD, and NPHP look promising. These include molecules targeting fluid transport, cellular metabolism, ciliary signaling and cell-cycle regulation. There is a real and urgent clinical need for translational research to bring novel treatments to clinical use for all forms of renal ciliopathies to reduce kidney disease progression and prevent kidney failure.
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Affiliation(s)
- Laura Devlin
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
| | - Praveen Dhondurao Sudhindar
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
| | - John A Sayer
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
- Renal Services, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
- NIHR Newcastle Biomedical Research Centre, Newcastle Upon Tyne, UK
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Beaumont NJ, Holmes HL, Gregory AV, Edwards ME, Rojas JD, Gessner RC, Dayton PA, Kline TL, Romero MF, Czernuszewicz TJ. Assessing Polycystic Kidney Disease in Rodents: Comparison of Robotic 3D Ultrasound and Magnetic Resonance Imaging. ACTA ACUST UNITED AC 2020; 1:1126-1136. [PMID: 33521650 DOI: 10.34067/kid.0003912020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Polycystic kidney disease (PKD) is an inherited disorder characterized by renal cyst formation and enlargement of the kidney. PKD severity can be staged noninvasively by measuring total kidney volume (TKV), a promising biomarker that has recently received regulatory qualification. In preclinical mouse models, where the disease is studied and potential therapeutics are evaluated, the most popular noninvasive method of measuring TKV is magnetic resonance imaging (MRI). Although MRI provides excellent 3D resolution and contrast, these systems are expensive to operate, have long acquisition times, and, consequently, are not heavily used in preclinical PKD research. In this study, a new imaging instrument, based on robotic ultrasound (US), was evaluated as a complementary approach for assessing PKD in rodent models. The objective was to determine the extent to which TKV measurements on the robotic US scanner correlated with both in vivo and ex vivo reference standards (MRI and Vernier calipers, respectively). A cross-sectional study design was implemented that included both PKD-affected mice and healthy wild types, spanning sex and age for a wide range of kidney volumes. It was found that US-derived TKV measurements and kidney lengths were strongly associated with both in vivo MRI and ex vivo Vernier caliper measurements (R 2=0.94 and 0.90, respectively). In addition to measuring TKV, renal vascular density was assessed using acoustic angiography (AA), a novel contrast-enhanced US methodology. AA image intensity, indicative of volumetric vascularity, was seen to have a strong negative correlation with TKV (R 2=0.82), suggesting impaired renal vascular function in mice with larger kidneys. These studies demonstrate that robotic US can provide a rapid and accurate approach for noninvasively evaluating PKD in rodent models.
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Affiliation(s)
| | - Heather L Holmes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota
| | | | | | | | | | - Paul A Dayton
- Joint Department of Biomedical Engineering, The University of North Carolina and North Carolina State University, Chapel Hill, North Carolina
| | - Timothy L Kline
- Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota.,Radiology, Mayo Clinic, Rochester, Minnesota
| | - Michael F Romero
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota.,Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Tomasz J Czernuszewicz
- SonoVol, Inc., Durham, North Carolina.,Joint Department of Biomedical Engineering, The University of North Carolina and North Carolina State University, Chapel Hill, North Carolina
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8
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Symptom relief and quality of life after combined partial hepatectomy and cyst fenestration in highly symptomatic polycystic liver disease. Surgery 2020; 168:25-32. [PMID: 32402542 DOI: 10.1016/j.surg.2020.02.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 02/14/2020] [Accepted: 02/17/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND Polycystic liver disease can cause severe symptomatic hepatomegaly. Combined partial hepatectomy and cyst fenestration can be performed to reduce liver volume and symptom burden. We aimed to assess change in symptom relief and quality of life 6 months after partial hepatectomy and cyst fenestration in polycystic liver disease patients. METHOD We established a prospective cohort between 2014 and 2018 at a referral center in the United States. Patients who underwent partial hepatectomy and cyst fenestration for volume-related symptoms were included. Primary outcome was change in polycystic liver disease-related symptoms, measured with Polycystic Liver Disease Questionnaire. Secondary outcomes were change in liver volume (computed tomography/ magnetic resonance imaging) and change in quality of life, measured with the 12-Item Short Form Survey and the EuroQoL Visual Analogue Scale. Questionnaire scores range from 0 to 100 and were assessed before and 6 months after partial hepatectomy and cyst fenestration. Surgical complications were scored according to Clavien-Dindo (grade 1 to 5). RESULTS We included 18 patients (mean age 52 years, 82% female). Partial hepatectomy and cyst fenestration reduced median liver volume (4,917 to 2,120 mL). Symptoms, measured with Polycystic Liver Disease Questionnaire, decreased (76.9 to 34.8 points; P < .001) 6 months after surgery; 15/16 symptoms declined after treatment, with the most impact seen on early satiety and dyspnea. Quality of life also improved after surgery: median physical and mental component scales of the 12-Item Short Form Survey and EuroQoL Visual Analog Scale increased (24.9 to 45.7, P = .004; 40.5 to 55.4, P = .02; and 40.0 to 72.5, P = .003). Major complications (grade 4) occurred in 2 patients. There was no procedure-related mortality. CONCLUSION Partial hepatectomy and cyst fenestration substantially improves symptom burden and quality of life in highly symptomatic polycystic liver disease patients.
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van Gastel MDA, Edwards ME, Torres VE, Erickson BJ, Gansevoort RT, Kline TL. Automatic Measurement of Kidney and Liver Volumes from MR Images of Patients Affected by Autosomal Dominant Polycystic Kidney Disease. J Am Soc Nephrol 2019; 30:1514-1522. [PMID: 31270136 DOI: 10.1681/asn.2018090902] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 04/10/2019] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND The formation and growth of cysts in kidneys, and often liver, in autosomal dominant polycystic kidney disease (ADPKD) cause progressive increases in total kidney volume (TKV) and liver volume (TLV). Laborious and time-consuming manual tracing of kidneys and liver is the current gold standard. We developed a fully automated segmentation method for TKV and TLV measurement that uses a deep learning network optimized to perform semantic segmentation of kidneys and liver. METHODS We used 80% of a set of 440 abdominal magnetic resonance images (T2-weighted HASTE coronal sequences) from patients with ADPKD to train the network and the remaining 20% for validation. Both kidneys and liver were also segmented manually. To evaluate the method's performance, we used an additional test set of images from 100 patients, 45 of whom were also involved in longitudinal analyses. RESULTS TKV and TLV measured by the automated approach correlated highly with manually traced TKV and TLV (intraclass correlation coefficients, 0.998 and 0.996, respectively), with low bias and high precision (<0.1%±2.7% for TKV and -1.6%±3.1% for TLV); this was comparable with inter-reader variability of manual tracing (<0.1%±3.5% for TKV and -1.5%±4.8% for TLV). For longitudinal analysis, bias and precision were <0.1%±3.2% for TKV and 1.4%±2.9% for TLV growth. CONCLUSIONS These findings demonstrate a fully automated segmentation method that measures TKV, TLV, and changes in these parameters as accurately as manual tracing. This technique may facilitate future studies in which automated and reproducible TKV and TLV measurements are needed to assess disease severity, disease progression, and treatment response.
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Affiliation(s)
- Maatje D A van Gastel
- Division of Nephrology and Hypertension, Department of Internal Medicine.,Department of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands and
| | - Marie E Edwards
- Division of Nephrology and Hypertension, Department of Internal Medicine
| | - Vicente E Torres
- Division of Nephrology and Hypertension, Department of Internal Medicine
| | | | - Ron T Gansevoort
- Department of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands and
| | - Timothy L Kline
- Division of Nephrology and Hypertension, Department of Internal Medicine; .,Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Chebib FT, Perrone RD, Chapman AB, Dahl NK, Harris PC, Mrug M, Mustafa RA, Rastogi A, Watnick T, Yu ASL, Torres VE. A Practical Guide for Treatment of Rapidly Progressive ADPKD with Tolvaptan. J Am Soc Nephrol 2018; 29:2458-2470. [PMID: 30228150 PMCID: PMC6171265 DOI: 10.1681/asn.2018060590] [Citation(s) in RCA: 142] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
In the past, the treatment of autosomal dominant polycystic kidney disease (ADPKD) has been limited to the management of its symptoms and complications. Recently, the US Food and Drug Administration (FDA) approved tolvaptan as the first drug treatment to slow kidney function decline in adults at risk of rapidly progressing ADPKD. Full prescribing information approved by the FDA provides helpful guidelines but does not address practical questions that are being raised by nephrologists, internists, and general practitioners taking care of patients with ADPKD, and by the patients themselves. In this review, we provide practical guidance and discuss steps that require consideration before and after prescribing tolvaptan to patients with ADPKD to ensure that this treatment is implemented safely and effectively. These steps include confirmation of diagnosis; identification of rapidly progressive disease; implementation of basic renal protective measures; counseling of patients on potential benefits and harms; exclusions to use; education of patients on aquaresis and its expected consequences; initiation, titration, and optimization of tolvaptan treatment; prevention of aquaresis-related complications; evaluation and management of liver enzyme elevations; and monitoring of treatment efficacy. Our recommendations are made on the basis of published evidence and our collective experiences during the randomized, clinical trials and open-label extension studies of tolvaptan in ADPKD.
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Affiliation(s)
- Fouad T Chebib
- Division of Nephrology and Hypertension, Mayo Clinic College of Medicine, Rochester, Minnesota;
| | - Ronald D Perrone
- Division of Nephrology, Department of Medicine, Tufts Medical Center, Boston, Massachusetts
| | - Arlene B Chapman
- Section of Nephrology, University of Chicago School of Medicine, Chicago, Illinois
| | - Neera K Dahl
- Section of Nephrology, Yale University School of Medicine, New Haven, Connecticut
| | - Peter C Harris
- Division of Nephrology and Hypertension, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Michal Mrug
- Division of Nephrology, Department of Veterans Affairs Medical Center and University of Alabama, Birmingham, Alabama
| | - Reem A Mustafa
- Division of Nephrology and Hypertension and the Kidney Institute, University of Kansas Medical Center, Kansas City, Kansas
| | - Anjay Rastogi
- Division of Nephrology, Department of Medicine, University of California, Los Angeles, Los Angeles, California; and
| | - Terry Watnick
- Division of Nephrology, University of Maryland School of Medicine, Baltimore, Maryland
| | - Alan S L Yu
- Division of Nephrology and Hypertension and the Kidney Institute, University of Kansas Medical Center, Kansas City, Kansas
| | - Vicente E Torres
- Division of Nephrology and Hypertension, Mayo Clinic College of Medicine, Rochester, Minnesota;
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