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Jeon SK, Lee JM, Cho SJ, Byun YH, Jee JH, Kang M. Development and validation of multivariable quantitative ultrasound for diagnosing hepatic steatosis. Sci Rep 2023; 13:15235. [PMID: 37709827 PMCID: PMC10502048 DOI: 10.1038/s41598-023-42463-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 09/11/2023] [Indexed: 09/16/2023] Open
Abstract
This study developed and validated multivariable quantitative ultrasound (QUS) model for diagnosing hepatic steatosis. Retrospective secondary analysis of prospectively collected QUS data was performed. Participants underwent QUS examinations and magnetic resonance imaging proton density fat fraction (MRI-PDFF; reference standard). A multivariable regression model for estimating hepatic fat fraction was determined using two QUS parameters from one tertiary hospital (development set). Correlation between QUS-derived estimated fat fraction(USFF) and MRI-PDFF and diagnostic performance of USFF for hepatic steatosis (MRI-PDFF ≥ 5%) were assessed, and validated in an independent data set from the other health screening center(validation set). Development set included 173 participants with suspected NAFLD with 126 (72.8%) having hepatic steatosis; and validation set included 452 health screening participants with 237 (52.4%) having hepatic steatosis. USFF was correlated with MRI-PDFF (Pearson r = 0.799 and 0.824; development and validation set). The model demonstrated high diagnostic performance, with areas under the receiver operating characteristic curves of 0.943 and 0.924 for development and validation set, respectively. Using cutoff of 6.0% from development set, USFF showed sensitivity, specificity, positive predictive value, and negative predictive value of 87.8%, 78.6%, 81.9%, and 85.4% for diagnosing hepatic steatosis in validation set. In conclusion, multivariable QUS parameters-derived estimated fat fraction showed high diagnostic performance for detecting hepatic steatosis.
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Affiliation(s)
- Sun Kyung Jeon
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehangno, Jongno-gu, Seoul, 03080, Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehangno, Jongno-gu, Seoul, 03080, Korea.
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
| | - Soo Jin Cho
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-Gu, Seoul, 06351, Korea.
| | - Young-Hye Byun
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-Gu, Seoul, 06351, Korea
| | - Jae Hwan Jee
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-Gu, Seoul, 06351, Korea
| | - Mira Kang
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-Gu, Seoul, 06351, Korea
- Department of Digital Health, Samsung Advanced Institute of Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Korea
- Digital Innovation Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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Marini TJ, Castaneda B, Parker K, Baran TM, Romero S, Iyer R, Zhao YT, Hah Z, Park MH, Brennan G, Kan J, Meng S, Dozier A, O’Connell A. No sonographer, no radiologist: Assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans. PLOS Digit Health 2022; 1:e0000148. [PMID: 36812553 PMCID: PMC9931251 DOI: 10.1371/journal.pdig.0000148] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 10/21/2022] [Indexed: 05/12/2023]
Abstract
Breast ultrasound provides a first-line evaluation for breast masses, but the majority of the world lacks access to any form of diagnostic imaging. In this pilot study, we assessed the combination of artificial intelligence (Samsung S-Detect for Breast) with volume sweep imaging (VSI) ultrasound scans to evaluate the possibility of inexpensive, fully automated breast ultrasound acquisition and preliminary interpretation without an experienced sonographer or radiologist. This study was conducted using examinations from a curated data set from a previously published clinical study of breast VSI. Examinations in this data set were obtained by medical students without prior ultrasound experience who performed VSI using a portable Butterfly iQ ultrasound probe. Standard of care ultrasound exams were performed concurrently by an experienced sonographer using a high-end ultrasound machine. Expert-selected VSI images and standard of care images were input into S-Detect which output mass features and classification as "possibly benign" and "possibly malignant." Subsequent comparison of the S-Detect VSI report was made between 1) the standard of care ultrasound report by an expert radiologist, 2) the standard of care ultrasound S-Detect report, 3) the VSI report by an expert radiologist, and 4) the pathological diagnosis. There were 115 masses analyzed by S-Detect from the curated data set. There was substantial agreement of the S-Detect interpretation of VSI among cancers, cysts, fibroadenomas, and lipomas to the expert standard of care ultrasound report (Cohen's κ = 0.73 (0.57-0.9 95% CI), p<0.0001), the standard of care ultrasound S-Detect interpretation (Cohen's κ = 0.79 (0.65-0.94 95% CI), p<0.0001), the expert VSI ultrasound report (Cohen's κ = 0.73 (0.57-0.9 95% CI), p<0.0001), and the pathological diagnosis (Cohen's κ = 0.80 (0.64-0.95 95% CI), p<0.0001). All pathologically proven cancers (n = 20) were designated as "possibly malignant" by S-Detect with a sensitivity of 100% and specificity of 86%. Integration of artificial intelligence and VSI could allow both acquisition and interpretation of ultrasound images without a sonographer and radiologist. This approach holds potential for increasing access to ultrasound imaging and therefore improving outcomes related to breast cancer in low- and middle- income countries.
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Affiliation(s)
- Thomas J. Marini
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America
- * E-mail:
| | - Benjamin Castaneda
- Departamento de Ingeniería, Pontificia Universidad Católica del Perú, Lima, Peru
| | - Kevin Parker
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Timothy M. Baran
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Stefano Romero
- Departamento de Ingeniería, Pontificia Universidad Católica del Perú, Lima, Peru
| | - Radha Iyer
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Yu T. Zhao
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Zaegyoo Hah
- Samsung Medison Co., Ltd., Seoul, Republic of Korea
| | - Moon Ho Park
- Samsung Electronics Co., Ltd., Seoul, Republic of Korea
| | - Galen Brennan
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Jonah Kan
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Steven Meng
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Ann Dozier
- Department of Public Health, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Avice O’Connell
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America
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Park J, Lee JM, Lee G, Jeon SK, Joo I. Quantitative Evaluation of Hepatic Steatosis Using Advanced Imaging Techniques: Focusing on New Quantitative Ultrasound Techniques. Korean J Radiol 2022; 23:13-29. [PMID: 34983091 PMCID: PMC8743150 DOI: 10.3348/kjr.2021.0112] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 07/26/2021] [Accepted: 08/31/2021] [Indexed: 12/12/2022] Open
Abstract
Nonalcoholic fatty liver disease, characterized by excessive accumulation of fat in the liver, is the most common chronic liver disease worldwide. The current standard for the detection of hepatic steatosis is liver biopsy; however, it is limited by invasiveness and sampling errors. Accordingly, MR spectroscopy and proton density fat fraction obtained with MRI have been accepted as non-invasive modalities for quantifying hepatic steatosis. Recently, various quantitative ultrasonography techniques have been developed and validated for the quantification of hepatic steatosis. These techniques measure various acoustic parameters, including attenuation coefficient, backscatter coefficient and speckle statistics, speed of sound, and shear wave elastography metrics. In this article, we introduce several representative quantitative ultrasonography techniques and their diagnostic value for the detection of hepatic steatosis.
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Affiliation(s)
- Junghoan Park
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
| | - Gunwoo Lee
- Ultrasound R&D 2 Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seoul, Korea
| | - Sun Kyung Jeon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
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