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He P, Chen W, Bai MY, Li J, Wang QQ, Fan LH, Zheng J, Liu CT, Zhang XR, Yuan XR, Song PJ, Cui LG. Application of computer-aided diagnosis to predict malignancy in BI-RADS 3 breast lesions. Heliyon 2024; 10:e24560. [PMID: 38304808 PMCID: PMC10831749 DOI: 10.1016/j.heliyon.2024.e24560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 02/03/2024] Open
Abstract
Purpose To evaluate the ability of computer-aided diagnosis (CAD) system (S-Detect) to identify malignancy in ultrasound (US) -detected BI-RADS 3 breast lesions. Materials and methods 148 patients with 148 breast lesions categorized as BI-RADS 3 were included in the study between January 2021 and September 2022. The malignancy rate, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated. Results In this study, 143 breast lesions were found to be benign, and 5 breast lesions were malignant (malignancy rate, 3.4 %, 95 % confidence interval (CI): 0.5-6.3). The malignancy rate rose significantly to 18.2 % (4/22, 95 % CI: 2.1-34.3) in the high-risk group with a "possibly malignant" CAD result (p = 0.017). With a "possibly benign" CAD result, the malignancy rate decreased to 0.8 % (1/126, 95 % CI: 0-2.2) in the low-risk group (p = 0.297). The AUC, sensitivity, specificity, accuracy, PPV, and NPV of the CAD system in BI-RADS 3 breast lesions were 0.837 (95 % CI: 77.7-89.6), 80.0 % (95 % CI: 73.6-86.4), 87.4 % (95 % CI: 82.0-92.7), 87.2 % (95 % CI: 81.8-92.6), 18.2 % (95 % CI: 2.1-34.3) and 99.2 % (95 % CI: 97.8-100.0), respectively. Conclusions CAD system (S-Detect) enables radiologists to distinguish a high-risk group and a low-risk group among US-detected BI-RADS 3 breast lesions, so that patients in the low-risk group can receive follow-up without anxiety, while those in the high-risk group with a significantly increased malignancy rate should actively receive biopsy to avoid delayed diagnosis of breast cancer.
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Affiliation(s)
- Ping He
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Beijing, 100191, China
| | - Wen Chen
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Beijing, 100191, China
| | - Ming-Yu Bai
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Beijing, 100191, China
| | - Jun Li
- Department of Ultrasound, The First Affiliated Hospital of Medical College of Shihezi University, 107 North Second Rd., Shihezi, 832008, Xinjiang, China
| | - Qing-Qing Wang
- Department of Breast Ultrasonography, Center for Diagnosis and Treatment of Breast Diseases, Yili Maternity and Child Health Hospital, Sichuan Road, Economic Cooperation Zone, Yili Kazakh Autonomous Prefecture, Xinjiang Uyghur Autonomous Region, China
| | - Li-Hong Fan
- Department of Ultrasound, Jinzhong First People's Hospital, 689 South Huitong Rd. Yuci District 030600, Jinzhong City, Shanxi Province, China
| | - Jian Zheng
- Ultrasound Department of the Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of Shenzhen, Shenzhen, 518172, China
| | - Chun-Tao Liu
- Department of Ultrasound, Liaocheng Dongchangfu District Maternal and Child Care Service Center, 129 Zhenxing West Rd., Liaocheng, 252000, Shandong, China
| | - Xiao-Rong Zhang
- Department of Ultrasound, Beijing HaiDian Hospital, 29 Zhongguanchun Rd., Beijing, 100080, China
| | - Xi-Rong Yuan
- Department of Ultrasound, The Second People's Hospital of Zhangqiu District, Jinan, Shandong, Ji Nan Zhang Qiu, 250200, China
| | - Peng-Jie Song
- Department of Ultrasound, Port Hospital of Hebei Port Group Co. LTD, 57 Dongshan Street, Haigang District, Qinhuangdao City, Hebei Province, China
| | - Li-Gang Cui
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Beijing, 100191, China
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Reig B, Kim E, Chhor CM, Moy L, Lewin AA, Heacock L. Problem-solving Breast MRI. Radiographics 2023; 43:e230026. [PMID: 37733618 DOI: 10.1148/rg.230026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Breast MRI has high sensitivity and negative predictive value, making it well suited to problem solving when other imaging modalities or physical examinations yield results that are inconclusive for the presence of breast cancer. Indications for problem-solving MRI include equivocal or uncertain imaging findings at mammography and/or US; suspicious nipple discharge or skin changes suspected to represent an abnormality when conventional imaging results are negative for cancer; lesions categorized as Breast Imaging Reporting and Data System 4, which are not amenable to biopsy; and discordant radiologic-pathologic findings after biopsy. MRI should not precede or replace careful diagnostic workup with mammography and US and should not be used when a biopsy can be safely performed. The role of MRI in characterizing calcifications is controversial, and management of calcifications should depend on their mammographic appearance because ductal carcinoma in situ may not appear enhancing on MR images. In addition, ductal carcinoma in situ detected solely with MRI is not associated with a higher likelihood of an upgrade to invasive cancer compared with ductal carcinoma in situ detected with other modalities. MRI for triage of high-risk lesions is a subject of ongoing investigation, with a possible future role for MRI in decreasing excisional biopsies. The accuracy of MRI is likely to increase with the use of advanced techniques such as deep learning, which will likely expand the indications for problem-solving MRI. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
- Beatriu Reig
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Eric Kim
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Chloe M Chhor
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Linda Moy
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Alana A Lewin
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Laura Heacock
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
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