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Jia W, Xia S, Jia X, Tang B, Cheng S, Nie M, Guan L, Duan Y, Zhang M, Chen X, Zhang H, Bai B, Jia H, Li N, Yuan C, Cai E, Dong Y, Zhang J, Jia Y, Liu J, Tang Z, Luo T, Zhang X, Zhan W, Zhu Y, Zhou J. Ultrasound Viscosity Imaging in Breast Lesions: A Multicenter Prospective Study. Acad Radiol 2024:S1076-6332(24)00159-4. [PMID: 38582684 DOI: 10.1016/j.acra.2024.03.017] [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: 11/27/2023] [Revised: 02/16/2024] [Accepted: 03/17/2024] [Indexed: 04/08/2024]
Abstract
RATIONALE AND OBJECTIVES To explore and validate the clinical value of ultrasound (US) viscosity imaging in differentiating breast lesions by combining with BI-RADS, and then comparing the diagnostic performances with BI-RADS alone. MATERIALS AND METHODS This multicenter, prospective study enrolled participants with breast lesions from June 2021 to November 2022. A development cohort (DC) and validation cohort (VC) were established. Using histological results as reference standard, the viscosity-related parameter with the highest area under the receiver operating curve (AUC) was selected as the optimal one. Then the original BI-RADS would upgrade or not based on the value of this parameter. Finally, the results were validated in the VC and total cohorts. In the DC, VC and total cohorts, all breast lesions were divided into the large lesion, small lesion and overall groups respectively. RESULTS A total of 639 participants (mean age, 46 years ± 14) with 639 breast lesions (372 benign and 267 malignant lesions) were finally enrolled in this study including 392 participants in the DC and 247 in the VC. In the DC, the optimal viscosity-related parameter in differentiating breast lesions was calculated to be A'-S2-Vmax, with the AUC of 0.88 (95% CI: 0.84, 0.91). Using > 9.97 Pa.s as the cutoff value, the BI-RADS was then modified. The AUC of modified BI-RADS significantly increased from 0.85 (95% CI: 0.81, 0.88) to 0.91 (95% CI: 0.87, 0.93), 0.85 (95% CI: 0.80, 0.89) to 0.90 (95% CI: 0.85, 0.93) and 0.85 (95% CI: 0.82, 0.87) to 0.90 (95% CI: 0.88, 0.92) in the DC, VC and total cohorts respectively (P < .05 for all). CONCLUSION The quantitative viscous parameters evaluated by US viscosity imaging contribute to breast cancer diagnosis when combined with BI-RADS.
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Affiliation(s)
- WanRu Jia
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - ShuJun Xia
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - XiaoHong Jia
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China
| | - BingHui Tang
- Department of Ultrasound, Nanchang People's Hospital, Nanchang, Jiangxi Province 330000, China
| | - ShuZhen Cheng
- Department of Ultrasound, Nanchang People's Hospital, Nanchang, Jiangxi Province 330000, China
| | - MeiYuan Nie
- Department of Ultrasound, Nanchang People's Hospital, Nanchang, Jiangxi Province 330000, China
| | - Ling Guan
- Department of Ultrasound, Gansu Provincial Cancer Hospital, Lanzhou, Gansu Province, China
| | - Ying Duan
- Department of Ultrasound, Gansu Provincial Cancer Hospital, Lanzhou, Gansu Province, China
| | - MengYan Zhang
- Department of Ultrasound, Gansu Provincial Cancer Hospital, Lanzhou, Gansu Province, China
| | - Xia Chen
- Department of Ultrasound, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, China
| | - Hui Zhang
- Department of Ultrasound, The First Hospital of Jiaxing, Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang Province, China
| | - BaoYan Bai
- Department of Ultrasound, Affiliated Hospital of Yan'an University, Yan'an, Shaanxi Province, China
| | - HaiYun Jia
- Department of Ultrasound, Affiliated Hospital of Yan'an University, Yan'an, Shaanxi Province, China
| | - Ning Li
- Department of Ultrasound, Yunnan Kungang Hospital, The Seventh Affiliated Hospital of Dali University, No.2 Ganghenan Road, Anning, Yunnan Province 650330, China
| | - CongCong Yuan
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China
| | - EnHeng Cai
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China
| | - YiJie Dong
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - JingWen Zhang
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China
| | - Yi Jia
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China
| | - Juan Liu
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China
| | - ZhenYun Tang
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China
| | - Ting Luo
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China
| | - XiaoXiao Zhang
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China
| | - WeiWei Zhan
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Ying Zhu
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - JianQiao Zhou
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin 2nd Road, 200025 Shanghai, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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Wang Y, Chen W, Wang Q. Segmental and transmural motion of the rat myocardium estimated using quantitative ultrasound with new strategies for infarct detection. Front Bioeng Biotechnol 2023; 11:1236108. [PMID: 37744251 PMCID: PMC10512837 DOI: 10.3389/fbioe.2023.1236108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction: The estimation of myocardial motion abnormalities has great potential for the early diagnosis of myocardial infarction (MI). This study aims to quantitatively analyze the segmental and transmural myocardial motion in MI rats by incorporating two novel strategies of algorithm parameter optimization and transmural motion index (TMI) calculation. Methods: Twenty-one rats were randomly divided into three groups (n = 7 per group): sham, MI, and ischemia-reperfusion (IR) groups. Ultrasound radio-frequency (RF) signals were acquired from each rat heart at 1 day and 28 days after animal model establishment; thus, a total of six datasets were represented as Sham1, Sham28, MI1, MI28, IR1, and IR28. The systolic cumulative displacement was calculated using our previously proposed vectorized normalized cross-correlation (VNCC) method. A semiautomatic regional and layer-specific myocardium segmentation framework was proposed for transmural and segmental myocardial motion estimation. Two novel strategies were proposed: the displacement-compensated cross-correlation coefficient (DCCCC) for algorithm parameter optimization and the transmural motion index (TMI) for quantitative estimation of the cross-wall transmural motion gradient. Results: The results showed that an overlap value of 80% used in VNCC guaranteed a more accurate displacement calculation. Compared to the Sham1 group, the systolic myocardial motion reductions were significantly detected (p < 0.05) in the middle anteroseptal (M-ANT-SEP), basal anteroseptal (B-ANT-SEP), apical lateral (A-LAT), middle inferolateral (M-INF-LAT), and basal inferolateral (B-INF-LAT) walls as well as a significant TMI drop (p < 0.05) in the M-ANT-SEP wall in the MI1 rats; significant motion reductions (p < 0.05) were also detected in the B-ANT-SEP and A-LAT walls in the IR1 group. The motion improvements (p < 0.05) were detected in the M-INF-LAT wall in the MI28 group and the apical septal (A-SEP) wall in the IR28 group compared to the MI1 and IR1 groups, respectively. Discussion: Our results show that the MI-induced reductions and reperfusion-induced recovery in systolic myocardial contractility could be successfully evaluated using our method, and most post-MI myocardial segments could recover systolic function to various extents in the remodeling phase. In conclusion, the ultrasound-based quantitative estimation framework for estimating segmental and transmural motion of the myocardium proposed in our study has great potential for non-invasive, novel, and early MI detection.
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Affiliation(s)
- Yinong Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, China
| | - Wufan Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, China
| | - Qing Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, China
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