Analysis of clinical value of elastography combined with automatic breast full volume imaging in differential diagnosis of benign and malignant breast nodules of BI-RADS category 4_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

CHEN Zhenglei , ZHANG Zhen , DU Guilin , WANG Liping , YONG Xinyuan ,  ZHANG Yuhua

Department of Ultrasound Medicine, Zhengzhou Third People’s Hospital, Zhengzhou 450000, P. R. China;

Corresponding?author：

ZHANG Yuhua, Email: zzssy_csk@163.com

Keywords：

automated breast full volume imaging; elastography; breast imaging reporting and data system; breast nodule; differential diagnosis

DOI：

10.7507/1007-9424.202507108

Video：

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Objective To analyze the clinical value of elastography combined with automatic breast full volume imaging in distinguishing benign and malignant breast nodules of breast imaging reporting and data system (BI-RADS) category 4. Methods This retrospective study included patients with breast nodules of BI-RADS category 4 who confirmed by routine ultrasound in Zhengzhou Third People’s Hospital from January 2018 to December 2023. To compare the clinical efficacy of conventional conventional color doppler ultrasound (CDUS), coronal automated breast volume scanner (ABVS), ultrasound elastography (UE), coronal automatic breast volume imaging combined with UE in differential diagnosis of benign and malignant breast nodules of BI-RADS category 4. Results A total of 280 cases with 301 lesions were enrolled in this study, among which 195 lesions were pathologically diagnosed as benign and 106 as malignant. The Kappa values for the consistency between the diagnostic results of benign and malignant breast nodules of BI-RADS category 4 by CDUS, ABVS, UE, and the combination of coronal ABVS and UE with the pathological results were 0.702, 0.625, 0.717 and 0.978, respectively. The sensitivity of coronal ABVS combined with UE was 99.06% (105/106), the specificity was 98.97% (193/195), and the accuracy was 99.00% (298/301); The sensitivity of CDUS was 76.42% (81/106), the specificity was 92.31% (180/195), and the accuracy was 86.71% (261/301); the sensitivity of UE was 88.68% (94/106), the specificity was 77.95% (152/195), and the accuracy was 81.73% (246/301); the sensitivity of coronal ABVS was 87.74% (93/106), the specificity was 86.71% (168/195), and the accuracy was 86.71% (261/301). Receiver operating characteristic curve analysis showed that the area under the curve of the combination of coronal ABVS and UE was 0.990, which had superior diagnostic efficacy than CDUS (Z=–6.572, P<0.001), UE (Z=–7.525, P<0.001) and coronal ABVS (Z=–6.167, P<0.001). Conclusion The combination of coronal ABVS and UE can effectively improve the differential diagnosis of benign and malignant breast nodules of BI-RADS category 4 and reduce the risk of clinical overdiagnosis.

Citation： CHEN Zhenglei, ZHANG Zhen, DU Guilin, WANG Liping, YONG Xinyuan, ZHANG Yuhua. Analysis of clinical value of elastography combined with automatic breast full volume imaging in differential diagnosis of benign and malignant breast nodules of BI-RADS category 4. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2026, 33(3): 373-378. doi: 10.7507/1007-9424.202507108 Copy

1.	Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2021. CA Cancer J Clin, 2021, 71(1): 7-33.
2.	劉夢涵, 周匯明, 肖際東. 自動乳腺全容積掃描影像組學聯合臨床和超聲特征列線圖鑒別良、惡性乳腺導管內病變. 中國醫學影像技術, 2024, 40(3): 366-371.
3.	Zhang X, Chen J, Zhou Y, et al. Diagnostic value of an automated breast volume scanner compared with a hand-held ultrasound: a meta-analysis. Gland Surg, 2019, 8(6): 698-711.
4.	Hendriks GAGM, Chen C, Mann R, et al. Automated 3-D ultrasound elastography of the breast: an in vivo validation study. Ultrasound Med Biol, 2024, 50(3): 358-363.
5.	Jiang X, Chen C, Yao J, et al. A nomogram for diagnosis of BI-RADS 4 breast nodules based on three-dimensional volume ultrasound. BMC Med Imaging, 2025, 25(1): 48. doi: 10.1186/s12880-025-01580-w.
6.	朱慶莉, 姜玉新. 乳腺影像報告與數據系統指南 (第5版) 超聲內容更新介紹. 中華醫學超聲雜志 (電子版), 2016, 13(1): 5-7.
7.	陳泉, 胡萍, 何燕, 等. ABVS冠狀面聯合彈性成像技術對乳腺BI-RADS-US 4類病灶的診斷價值. 現代醫學, 2023, 51(12): 1709-1714.
8.	Lee SH, Chang JM, Cho N, et al. Practice guideline for the performance of breast ultrasound elastography. Ultrasonography, 2014, 33(1): 3-10.
9.	中國超聲醫學工程學會儀器工程開發專業委員會, 中國抗癌協會腫瘤影像專委會, 中華醫學會超聲醫學分會淺表組織與血管學組. 自動乳腺容積超聲技術專家共識 (2022版). 中國超聲醫學雜志, 2022, 38(3): 241-247.
10.	張一丹, 徐超麗, 張麗娟, 等. 彈性成像技術聯合自動乳腺全容積成像鑒別診斷乳腺影像報告與數據系統4類病灶良惡性的價值. 中華醫學超聲雜志 (電子版), 2017, 14(12): 903-908.
11.	Tozaki M, Isobe S, Yamaguchi M, et al. Optimal scanning technique to cover the whole breast using an automated breast volume scanner. Jpn J Radiol, 2010, 28(4): 325-328.
12.	Luo WQ, Huang QX, Huang XW, et al. Predicting breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: a nomogram combining radiomics and BI-RADS. Sci Rep, 2019, 9(1): 11921. doi: 10.1038/s41598-019-48488-4.
13.	Ren T, Li X, Xiang Y, et al. The diagnostic significance of the BI-RADS classification combined with automated breast volume scanner and shear wave elastography for breast lesions. J Ultrasound Med, 2023, 42(7): 1459-1469.
14.	Li JM, Shao YH, Sun XM, et al. Ultrasonic features of automated breast volume scanner (ABVS) and handheld ultrasound (HHUS) combined with molecular biomarkers in predicting axillary lymph node metastasis of clinical T1-T2 breast cancer. Quant Imaging Med Surg, 2024, 14(2): 1359-1368.
15.	Sherchan A, Liang JT, Sherchan B, et al. Comparative analysis of automated breast volume scanner (ABVS) combined with conventional hand-held ultrasound and mammography in female breast cancer detection. Ann Med Surg (Lond), 2023, 86(1): 159-165.
16.	Yan L, Jing L, Lu Q, et al. Automated breast volume scanner is more valuable than hand-held ultrasound in diagnosis of small breast cancer: an analysis of 725 patients with 912 lesions evaluations. Ultrasound Q, 2024, 40(1): 66-73.
17.	Hendriks GA, Holl?nder B, Menssen J, et al. Automated 3D ultrasound elastography of the breast: a phantom validation study. Phys Med Biol, 2016, 61(7): 2665-2679.
18.	Sinha D, Sharma S, Kundaragi NG, et al. Added value of strain elastography in the characterisation of breast lesions: a prospective study. Ultrasound, 2020, 28(3): 164-173.
19.	Daniaux M, Auer T, De Zordo T, et al. Strain elastography of breast and prostata cancer: similarities and differences. Rofo, 2016, 188(3): 253-258.
20.	Carlsen JF, Ewertsen C, L?nn L, et al. Strain elastography ultrasound: an overview with emphasis on breast cancer diagnosis. Diagnostics (Basel), 2013, 3(1): 117-125.
21.	Ma Q, Wang J, Xu D, et al. Automatic breast volume scanner and B-ultrasound-based radiomics nomogram for clinician management of BI-RADS 4A lesions. Acad Radiol, 2023, 30(8): 1628-1637.
22.	Labcharoenwongs P, Vonganansup S, Chunhapran O, et al. An automatic breast tumor detection and classification including automatic tumor volume estimation using deep learning technique. Asian Pac J Cancer Prev, 2023, 24(3): 1081-1088.
23.	Zhou J, Zhang Y, Shi S. Ultrasound elastography: advances and challenges in early detection of breast cancer. Front Oncol, 2025, 15: 1589142. doi: 10.3389/fonc.2025.1589142.
24.	Yao Z, Luo T, Dong Y, et al. Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis. Nat Commun, 2023, 14(1): 788. doi: 10.1038/s41467-023-36102-1.
25.	Ansari MY, Qaraqe M, Righetti R, et al. Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound. Front Oncol, 2023, 13: 1282536. doi: 10.3389/fonc.2023.1282536.

1. Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2021. CA Cancer J Clin, 2021, 71(1): 7-33.
2. 劉夢涵, 周匯明, 肖際東. 自動乳腺全容積掃描影像組學聯合臨床和超聲特征列線圖鑒別良、惡性乳腺導管內病變. 中國醫學影像技術, 2024, 40(3): 366-371.
3. Zhang X, Chen J, Zhou Y, et al. Diagnostic value of an automated breast volume scanner compared with a hand-held ultrasound: a meta-analysis. Gland Surg, 2019, 8(6): 698-711.
4. Hendriks GAGM, Chen C, Mann R, et al. Automated 3-D ultrasound elastography of the breast: an in vivo validation study. Ultrasound Med Biol, 2024, 50(3): 358-363.
5. Jiang X, Chen C, Yao J, et al. A nomogram for diagnosis of BI-RADS 4 breast nodules based on three-dimensional volume ultrasound. BMC Med Imaging, 2025, 25(1): 48. doi: 10.1186/s12880-025-01580-w.
6. 朱慶莉, 姜玉新. 乳腺影像報告與數據系統指南 (第5版) 超聲內容更新介紹. 中華醫學超聲雜志 (電子版), 2016, 13(1): 5-7.
7. 陳泉, 胡萍, 何燕, 等. ABVS冠狀面聯合彈性成像技術對乳腺BI-RADS-US 4類病灶的診斷價值. 現代醫學, 2023, 51(12): 1709-1714.
8. Lee SH, Chang JM, Cho N, et al. Practice guideline for the performance of breast ultrasound elastography. Ultrasonography, 2014, 33(1): 3-10.
9. 中國超聲醫學工程學會儀器工程開發專業委員會, 中國抗癌協會腫瘤影像專委會, 中華醫學會超聲醫學分會淺表組織與血管學組. 自動乳腺容積超聲技術專家共識 (2022版). 中國超聲醫學雜志, 2022, 38(3): 241-247.
10. 張一丹, 徐超麗, 張麗娟, 等. 彈性成像技術聯合自動乳腺全容積成像鑒別診斷乳腺影像報告與數據系統4類病灶良惡性的價值. 中華醫學超聲雜志 (電子版), 2017, 14(12): 903-908.
11. Tozaki M, Isobe S, Yamaguchi M, et al. Optimal scanning technique to cover the whole breast using an automated breast volume scanner. Jpn J Radiol, 2010, 28(4): 325-328.
12. Luo WQ, Huang QX, Huang XW, et al. Predicting breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: a nomogram combining radiomics and BI-RADS. Sci Rep, 2019, 9(1): 11921. doi: 10.1038/s41598-019-48488-4.
13. Ren T, Li X, Xiang Y, et al. The diagnostic significance of the BI-RADS classification combined with automated breast volume scanner and shear wave elastography for breast lesions. J Ultrasound Med, 2023, 42(7): 1459-1469.
14. Li JM, Shao YH, Sun XM, et al. Ultrasonic features of automated breast volume scanner (ABVS) and handheld ultrasound (HHUS) combined with molecular biomarkers in predicting axillary lymph node metastasis of clinical T1-T2 breast cancer. Quant Imaging Med Surg, 2024, 14(2): 1359-1368.
15. Sherchan A, Liang JT, Sherchan B, et al. Comparative analysis of automated breast volume scanner (ABVS) combined with conventional hand-held ultrasound and mammography in female breast cancer detection. Ann Med Surg (Lond), 2023, 86(1): 159-165.
16. Yan L, Jing L, Lu Q, et al. Automated breast volume scanner is more valuable than hand-held ultrasound in diagnosis of small breast cancer: an analysis of 725 patients with 912 lesions evaluations. Ultrasound Q, 2024, 40(1): 66-73.
17. Hendriks GA, Holl?nder B, Menssen J, et al. Automated 3D ultrasound elastography of the breast: a phantom validation study. Phys Med Biol, 2016, 61(7): 2665-2679.
18. Sinha D, Sharma S, Kundaragi NG, et al. Added value of strain elastography in the characterisation of breast lesions: a prospective study. Ultrasound, 2020, 28(3): 164-173.
19. Daniaux M, Auer T, De Zordo T, et al. Strain elastography of breast and prostata cancer: similarities and differences. Rofo, 2016, 188(3): 253-258.
20. Carlsen JF, Ewertsen C, L?nn L, et al. Strain elastography ultrasound: an overview with emphasis on breast cancer diagnosis. Diagnostics (Basel), 2013, 3(1): 117-125.
21. Ma Q, Wang J, Xu D, et al. Automatic breast volume scanner and B-ultrasound-based radiomics nomogram for clinician management of BI-RADS 4A lesions. Acad Radiol, 2023, 30(8): 1628-1637.
22. Labcharoenwongs P, Vonganansup S, Chunhapran O, et al. An automatic breast tumor detection and classification including automatic tumor volume estimation using deep learning technique. Asian Pac J Cancer Prev, 2023, 24(3): 1081-1088.
23. Zhou J, Zhang Y, Shi S. Ultrasound elastography: advances and challenges in early detection of breast cancer. Front Oncol, 2025, 15: 1589142. doi: 10.3389/fonc.2025.1589142.
24. Yao Z, Luo T, Dong Y, et al. Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis. Nat Commun, 2023, 14(1): 788. doi: 10.1038/s41467-023-36102-1.
25. Ansari MY, Qaraqe M, Righetti R, et al. Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound. Front Oncol, 2023, 13: 1282536. doi: 10.3389/fonc.2023.1282536.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Analysis of clinical value of elastography combined with automatic breast full volume imaging in differential diagnosis of benign and malignant breast nodules of BI-RADS category 4

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