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Li XS, Zhang QJ, Zhu J, Zhou QQ, Yu YS, Hu ZC, Xia ZY, Wei L, Yin XD, Zhang H. Assessment of kidney function in chronic kidney disease by combining diffusion tensor imaging and total kidney volume. Int Urol Nephrol 2021; 54:385-393. [PMID: 34024009 DOI: 10.1007/s11255-021-02886-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 05/08/2021] [Indexed: 11/24/2022]
Abstract
OBJECTIVE This study aimed to investigate the value and feasibility of combining fractional anisotropy (FA) values from diffusion tensor imaging (DTI) and total kidney volume (TKV) for the assessment of kidney function in chronic kidney disease (CKD). MATERIALS AND METHODS Fifty-one patients were included in this study. All MRI examinations were performed with a 3.0 T scanner. DTI was used to measure FA values, and TKV was obtained from DTI and T2-weighted imaging (T2WI). Patients were divided into three groups (mild, moderate, severe) according to eGFR, which was calculated with serum creatinine. Differences in the FA values of the cortex and medulla were analysed among the three groups, and the relationships of FA values, TKV, and the product of the FA values and TKV with eGFR were analysed. Receiver operating characteristic (ROC) curve analysis was used to compare the diagnostic efficiency of the FA values, TKV, and the product of the FA values and TKV for kidney function in different CKD stages. RESULTS Medullary FA values (m-FA), TKV, and the product of the m-FA values and TKV (m-FA-TKV) were significantly correlated with eGFR (r = 0.653, 0.685, and 0.797, respectively; all P < 0.001). ROC curve analysis showed that m-FA-TKV exhibited better diagnostic performance than m-FA values (P = 0.022). CONCLUSION m-FA-TKV obtained by DTI significantly improves the accuracy of kidney function assessment in CKD patients.
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Affiliation(s)
- Xue-Song Li
- Department of Radiology, The Affiliated Jiangning Hospital with Nanjing Medical University, No. 169, Hushan Road, Nanjing, 211100, Jiangsu Province, China
| | - Qing-Juan Zhang
- Department of Nephrology, The Affiliated Jiangning Hospital with Nanjing Medicine University, No. 169, Hushan Road, Nanjing, 211100, Jiangsu Province, China
| | - Jiang Zhu
- Department of Nephrology, The Affiliated Jiangning Hospital with Nanjing Medicine University, No. 169, Hushan Road, Nanjing, 211100, Jiangsu Province, China
| | - Qing-Qing Zhou
- Department of Radiology, The Affiliated Jiangning Hospital with Nanjing Medical University, No. 169, Hushan Road, Nanjing, 211100, Jiangsu Province, China
| | - Yu-Sheng Yu
- Department of Radiology, The Affiliated Jiangning Hospital with Nanjing Medical University, No. 169, Hushan Road, Nanjing, 211100, Jiangsu Province, China
| | - Zhang-Chun Hu
- Department of Radiology, The Affiliated Jiangning Hospital with Nanjing Medical University, No. 169, Hushan Road, Nanjing, 211100, Jiangsu Province, China
| | - Zi-Yi Xia
- Department of Radiology, The Affiliated Jiangning Hospital with Nanjing Medical University, No. 169, Hushan Road, Nanjing, 211100, Jiangsu Province, China
| | - Liang Wei
- Department of Radiology, The Affiliated Jiangning Hospital with Nanjing Medical University, No. 169, Hushan Road, Nanjing, 211100, Jiangsu Province, China
| | - Xin-Dao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No. 68, Changle Road, Nanjing, 210006, Jiangsu Province, China
| | - Hong Zhang
- Department of Radiology, The Affiliated Jiangning Hospital with Nanjing Medical University, No. 169, Hushan Road, Nanjing, 211100, Jiangsu Province, China.
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Alnazer I, Bourdon P, Urruty T, Falou O, Khalil M, Shahin A, Fernandez-Maloigne C. Recent advances in medical image processing for the evaluation of chronic kidney disease. Med Image Anal 2021; 69:101960. [PMID: 33517241 DOI: 10.1016/j.media.2021.101960] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 11/18/2020] [Accepted: 12/31/2020] [Indexed: 12/31/2022]
Abstract
Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (CT) and scintigraphy (PET, SPECT) offers the opportunity to non-invasively retrieve structural, functional and molecular information that could detect changes in renal tissue properties and functionality. Currently, the ability of artificial intelligence to turn conventional medical imaging into a full-automated diagnostic tool is widely investigated. In addition to the qualitative analysis performed on renal medical imaging, texture analysis was integrated with machine learning techniques as a quantification of renal tissue heterogeneity, providing a promising complementary tool in renal function decline prediction. Interestingly, deep learning holds the ability to be a novel approach of renal function diagnosis. This paper proposes a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function. First, we summarize the use of different medical imaging modalities to monitor CKD and then, we show the ability of Artificial Intelligence (AI) to guide renal function evaluation from segmentation to disease prediction, discussing how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction. The paper gives a summary about the role of AI in renal segmentation.
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Affiliation(s)
- Israa Alnazer
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France; AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon.
| | - Pascal Bourdon
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
| | - Thierry Urruty
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
| | - Omar Falou
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon; American University of Culture and Education, Koura, Lebanon; Lebanese University, Faculty of Science, Tripoli, Lebanon
| | - Mohamad Khalil
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon
| | - Ahmad Shahin
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon
| | - Christine Fernandez-Maloigne
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
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