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Wang Y, Yue J, Xing Y, Ju Y, Gao X, Shu R, Wang Z, Liu B, Xiao Y, Zhang G, Li H, Wang T, Guan X, Song Z, Song H. Assessing Diagnostic Performance Using the Transverse Plane Plus Coronal Plane Compared With the Transverse Plane Alone in Screening Automated Breast Ultrasound. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:1013-1024. [PMID: 38323467 DOI: 10.1002/jum.16428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 01/21/2024] [Indexed: 02/08/2024]
Abstract
OBJECTIVES The coronal plane is the unique display mode of automated breast (AB) ultrasound (US), which has valuable features of showing the entire breast anatomy and providing additional diagnostic value for breast lesions. However, whether adding the coronal plane could improve the diagnostic performance in screening breast cancer remains uncertain. This study aimed to evaluate the value of adding the coronal plane in interpretation for AB US screening. METHODS In this retrospective study, AB US images from 644 women (396 in the no-finding group, 143 with benign lesions, and 105 with malignant lesions) aged 40-70 years were collected between January 2016 and October 2020. Four novice radiologists (with 1-5 years of experience with breast US) and four experienced radiologists (with >5 years of experience with breast US) were assigned to read all AB US images in the transverse plane plus coronal plane (T + C planes) and transverse plane (T plane) alone in separate reading sessions. Diagnostic performance, lesion conspicuity, and reading time were compared using analysis of variance. RESULTS The mean reading time of all radiologists was significantly shorter in the T + C planes reading mode than in the T plane alone (115 ± 32 vs 128 ± 31 s, respectively; P < .05), and cancers had a higher conspicuity (odds ratio, 1.76; 95% confidence interval [CI], 1.00-3.08; P = .04). No significant differences were noted in the two reading modes (T + C planes vs T plane) in the sensitivity (82% [95% CI, 74-89%] vs 81% [95% CI, 74-88%], respectively; P = .68) and specificity (68% [95% CI, 62-75%] vs 70% [95% CI, 64-75%], respectively; P = .39) when Breast Imaging-Reporting and Data System (BI-RADS) 3 was set as the threshold. There were also no significant differences in the two reading modes (T + C planes vs T plane) in the sensitivity (70% [95% CI, 64-76%] vs 69% [95% CI, 63-75%], respectively; P = .39) and specificity (91% [95% CI, 87-96%] vs 91% [95% CI, 88-95%], respectively; P = .90) when BI-RADS 4 was set as the threshold. In addition, the mean areas under the receiver operating characteristic curves of all radiologists in the two reading modes (T + C planes vs T plane) were not significantly different (0.84 [95% CI, 0.79-0.89] vs 0.83 [95% CI, 0.78-0.89], respectively; P = .61). CONCLUSIONS Adding a coronal plane in the AB US screening setting saved the reading time and improved the conspicuity of breast cancers but not the diagnostic performance.
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Affiliation(s)
- Yin Wang
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, China
- Department of Ultrasound, Xi'an Daxing Hospital, Xi'an, China
| | - Jinzhuo Yue
- Department of Ultrasound, Xi'an Daxing Hospital, Xi'an, China
| | - Yangfeng Xing
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yan Ju
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Xican Gao
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Rui Shu
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhenfang Wang
- Department of Ultrasound, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Bo Liu
- Department of Ultrasound, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Yao Xiao
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Ge Zhang
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Hui Li
- Department of Ultrasound, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Tian Wang
- Department of Ultrasound, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Xiangping Guan
- Department of Ultrasound, Shaanxi Provincial People's Hospital, Xi'an, China
| | | | - Hongping Song
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, China
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den Dekker BM, Broeders MJM, Meeuwis C, Setz-Pels W, Venmans A, van Gils CH, Pijnappel RM. Diagnostic accuracy of supplemental three-dimensional breast ultrasound in the work-up of BI-RADS 0 screening recalls. Insights Imaging 2024; 15:131. [PMID: 38816526 PMCID: PMC11139835 DOI: 10.1186/s13244-024-01714-8] [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: 12/21/2023] [Accepted: 05/04/2024] [Indexed: 06/01/2024] Open
Abstract
OBJECTIVE To evaluate the diagnostic accuracy of supplemental 3D automated breast ultrasound (ABUS) in the diagnostic work-up of BI-RADS 0 recalls. We hypothesized that 3D ABUS may reduce the benign biopsy rate. MATERIALS AND METHODS In this prospective multicenter diagnostic study, screening participants recalled after a BI-RADS 0 result underwent bilateral 3D ABUS supplemental to usual care: digital breast tomosynthesis (DBT) and targeted hand-held ultrasound (HHUS). Sensitivity, specificity, positive predictive value, and negative predictive value of 3D ABUS, and DBT plus HHUS, were calculated. New 3D ABUS findings and changes of management (biopsy or additional imaging) were recorded. RESULTS A total of 501 women (median age 55 years, IQR [51-64]) with 525 BI-RADS 0 lesions were included between April 2018 and March 2020. Cancer was diagnosed in 45 patients. 3D ABUS sensitivity was 72.1% (95% CI [57.2-83.4%]), specificity 84.4% (95% CI [80.8-87.4%]), PPV 29.2% (95% CI [21.4-38.5%]), and NPV 97.1% 95.0-98.4%). Sensitivity of DBT plus HHUS was 100% (95% CI [90.2-100%]), specificity 71.4% (95% CI [67.2-75.2%]), PPV 23.8% (95% CI [18.1-30.5%]) and NPV 100% (95% CI [98.7-100%]). Twelve out of 43 (27.9%) malignancies in BI-RADS 0 lesions were missed on 3D ABUS, despite being detected on DBT and/or HHUS. Supplemental 3D ABUS resulted in the detection of 57 new lesions and six extra biopsy procedures, all were benign. CONCLUSION 3D ABUS in the diagnostic work-up of BI-RADS 0 recalls may miss over a quarter of cancers detected with HHUS and/or DBT and should not be used to omit biopsy. Supplemental 3D ABUS increases the benign biopsy rate. TRIAL REGISTRATION Dutch Trial Register, available via https://www.onderzoekmetmensen.nl/en/trial/29659 CRITICAL RELEVANCE STATEMENT: Supplemental 3D automated breast ultrasound in the work-up of BI-RADS 0 recalls may miss over a quarter of cancers detected with other methods and should not be used to omit biopsy; ABUS findings did increase benign biopsy rate. KEY POINTS Automated breast ultrasound (ABUS) may miss over 25% of cancers detectable by alternative methods. Don't rely solely on 3D ABUS to assess indication for biopsy. New findings with supplemental 3D ABUS increase the benign biopsy rate.
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Affiliation(s)
- Bianca M den Dekker
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Mireille J M Broeders
- Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands
- Dutch Expert Centre for Screening, Nijmegen, The Netherlands
| | - Carla Meeuwis
- Department of Radiology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Wikke Setz-Pels
- Department of Radiology, Catharina Hospital, Eindhoven, The Netherlands
| | - Alexander Venmans
- Department of Radiology, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
| | - Carla H van Gils
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Ruud M Pijnappel
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Dutch Expert Centre for Screening, Nijmegen, The Netherlands
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Yang Y, Zhong Y, Li J, Feng J, Gong C, Yu Y, Hu Y, Gu R, Wang H, Liu F, Mei J, Jiang X, Wang J, Yao Q, Wu W, Liu Q, Yao H. Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts: a diagnostic study. Int J Surg 2024; 110:2604-2613. [PMID: 38348891 DOI: 10.1097/js9.0000000000001186] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/29/2024] [Indexed: 05/16/2024]
Abstract
OBJECTIVES The authors aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data System (BI-RADS) US 4A in diagnostic patients with dense breasts. METHODS A total of 992 patients were randomly allocated into the training cohort and the test cohort at a proportion of 4:1. Another, 218 patients were enrolled to form a prospective validation cohort. The DL model was developed by incorporating both US and MG images. The predictive performance of the combined DL model for malignancy was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The combined DL model was then compared to a clinical nomogram model and to the DL model trained using US image only and to that trained MG image only. RESULTS The combined DL model showed satisfactory diagnostic performance for predicting malignancy in breast lesions, with an AUC of 0.940 (95% CI: 0.874-1.000) in the test cohort, and an AUC of 0.906 (95% CI: 0.817-0.995) in the validation cohort, which was significantly higher than the clinical nomogram model, and the DL model for US or MG alone ( P <0.05). CONCLUSIONS The study developed an objective DL model combining both US and MG imaging features, which was proven to be more accurate for predicting malignancy in the BI-RADS US 4A breast lesions of patients with dense breasts. This model may then be used to more accurately guide clinicians' choices about whether performing biopsies in breast cancer diagnosis.
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Affiliation(s)
| | - Ying Zhong
- Department of Medical Oncology, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou
| | - Junwei Li
- Department of Medical Oncology, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou
| | - Jiahao Feng
- Cellsvision (Guangzhou) Medical Technology Inc., People's Republic of China
| | | | - Yunfang Yu
- Department of Medical Oncology, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou
| | | | | | | | | | | | | | - Jin Wang
- Cellsvision (Guangzhou) Medical Technology Inc., People's Republic of China
| | - Qinyue Yao
- Cellsvision (Guangzhou) Medical Technology Inc., People's Republic of China
| | | | | | - Herui Yao
- Breast Tumor Center
- Department of Medical Oncology, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou
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Iacob R, Iacob ER, Stoicescu ER, Ghenciu DM, Cocolea DM, Constantinescu A, Ghenciu LA, Manolescu DL. Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review. Bioengineering (Basel) 2024; 11:262. [PMID: 38534536 DOI: 10.3390/bioengineering11030262] [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: 02/19/2024] [Revised: 03/04/2024] [Accepted: 03/06/2024] [Indexed: 03/28/2024] Open
Abstract
Breast cancer, affecting both genders, but mostly females, exhibits shifting demographic patterns, with an increasing incidence in younger age groups. Early identification through mammography, clinical examinations, and breast self-exams enhances treatment efficacy, but challenges persist in low- and medium-income countries due to limited imaging resources. This review assesses the feasibility of employing breast ultrasound as the primary breast cancer screening method, particularly in resource-constrained regions. Following the PRISMA guidelines, this study examines 52 publications from the last five years. Breast ultrasound, distinct from mammography, offers advantages like radiation-free imaging, suitability for repeated screenings, and preference for younger populations. Real-time imaging and dense breast tissue evaluation enhance sensitivity, accessibility, and cost-effectiveness. However, limitations include reduced specificity, operator dependence, and challenges in detecting microcalcifications. Automatic breast ultrasound (ABUS) addresses some issues but faces constraints like potential inaccuracies and limited microcalcification detection. The analysis underscores the need for a comprehensive approach to breast cancer screening, emphasizing international collaboration and addressing limitations, especially in resource-constrained settings. Despite advancements, notably with ABUS, the primary goal is to contribute insights for optimizing breast cancer screening globally, improving outcomes, and mitigating the impact of this debilitating disease.
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Affiliation(s)
- Roxana Iacob
- Department of Anatomy and Embriology, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
- Doctoral School, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
- Faculty of Mechanics, Field of Applied Engineering Sciences, Specialization Statistical Methods and Techniques in Health and Clinical Research, 'Politehnica' University Timișoara, Mihai Viteazul Boulevard No. 1, 300222 Timisoara, Romania
| | - Emil Radu Iacob
- Department of Pediatric Surgery, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
| | - Emil Robert Stoicescu
- Doctoral School, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
- Faculty of Mechanics, Field of Applied Engineering Sciences, Specialization Statistical Methods and Techniques in Health and Clinical Research, 'Politehnica' University Timișoara, Mihai Viteazul Boulevard No. 1, 300222 Timisoara, Romania
- Department of Radiology and Medical Imaging, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
- Research Center for Pharmaco-Toxicological Evaluations, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
| | - Delius Mario Ghenciu
- Doctoral School, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
| | - Daiana Marina Cocolea
- Doctoral School, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
| | - Amalia Constantinescu
- Department of Radiology and Medical Imaging, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
| | - Laura Andreea Ghenciu
- Discipline of Pathophysiology, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
| | - Diana Luminita Manolescu
- Department of Radiology and Medical Imaging, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
- Center for Research and Innovation in Precision Medicine of Respiratory Diseases (CRIPMRD), 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
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Udayakumar D, Madhuranthakam AJ, Doğan BE. Magnetic Resonance Perfusion Imaging for Breast Cancer. Magn Reson Imaging Clin N Am 2024; 32:135-150. [PMID: 38007276 DOI: 10.1016/j.mric.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
Breast cancer is the most frequently diagnosed cancer among women worldwide, carrying a significant socioeconomic burden. Breast cancer is a heterogeneous disease with 4 major subtypes identified. Each subtype has unique prognostic factors, risks, treatment responses, and survival rates. Advances in targeted therapies have considerably improved the 5-year survival rates for primary breast cancer patients largely due to widespread screening programs that enable early detection and timely treatment. Imaging techniques are indispensable in diagnosing and managing breast cancer. While mammography is the primary screening tool, MRI plays a significant role when mammography results are inconclusive or in patients with dense breast tissue. MRI has become standard in breast cancer imaging, providing detailed anatomic and functional data, including tumor perfusion and cellularity. A key characteristic of breast tumors is angiogenesis, a biological process that promotes tumor development and growth. Increased angiogenesis in tumors generally indicates poor prognosis and increased risk of metastasis. Dynamic contrast-enhanced (DCE) MRI measures tumor perfusion and serves as an in vivo metric for angiogenesis. DCE-MRI has become the cornerstone of breast MRI, boasting a high negative-predictive value of 89% to 99%, although its specificity can vary. This review presents a thorough overview of magnetic resonance (MR) perfusion imaging in breast cancer, focusing on the role of DCE-MRI in clinical applications and exploring emerging MR perfusion imaging techniques.
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Affiliation(s)
- Durga Udayakumar
- Department of Radiology, Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Ananth J Madhuranthakam
- Department of Radiology, Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Başak E Doğan
- Department of Radiology, Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX 75390, USA
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Rahmat K, Ab Mumin N, Ng WL, Mohd Taib NA, Chan WY, Ramli Hamid MT. Automated Breast Ultrasound Provides Comparable Diagnostic Performance in Opportunistic Screening and Diagnostic Assessment. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:112-118. [PMID: 37839984 DOI: 10.1016/j.ultrasmedbio.2023.09.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/10/2023] [Accepted: 09/14/2023] [Indexed: 10/17/2023]
Abstract
OBJECTIVE The aim of the work described here was to assess the performance of automated breast ultrasound (ABUS) as an adjunct to digital breast tomosynthesis (DBT) in the screening and diagnostic setting. METHODS This cross-sectional study of women who underwent DBT and ABUS from December 2019 to March 2022 included opportunistic and targeted screening cases, as well as symptomatic women. Breast density, Breast Imaging Reporting and Data System categories and histopathology reports were collected and compared. The PPV3 (proportion of examinations with abnormal findings that resulted in a tissue diagnosis of cancer), biopsy rate (percentage of biopsies performed) and cancer detection yield (number of malignancies found by the diagnostic test given to the study sample) were calculated. RESULTS A total of 1089 ABUS examinations were performed (age range: 29-85 y, mean: 51.9 y). Among these were 909 screening (83.5%) and 180 diagnostic (16.5%) examinations. A total of 579 biopsies were performed on 407 patients, with a biopsy rate of 53.2%. There were 100 (9.2%) malignant lesions, 30 (5.2%) atypical/B3 lesions and 414 (71.5%) benign cases. In 9 cases (0.08%), ABUS alone detected malignancies, and in 19 cases (1.7%), DBT alone detected malignancies. The PPV3 in the screening group was 14.6%. CONCLUSION ABUS is useful as an adjunct to DBT in the opportunistic screening and diagnostic setting.
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Affiliation(s)
- Kartini Rahmat
- Department of Biomedical Imaging, Universiti Malaya Research Imaging Centre, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Nazimah Ab Mumin
- Department of Biomedical Imaging, Universiti Malaya Research Imaging Centre, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia; Department of Radiology, Faculty of Medicine, Universiti Teknologi MARA, Selangor, Malaysia.
| | - Wei Lin Ng
- Department of Biomedical Imaging, Universiti Malaya Research Imaging Centre, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Nur Aishah Mohd Taib
- Department of Surgery, Faculty of Medicine, University Malaya Cancer Research Institute, University Malaya, Kuala Lumpur, Malaysia
| | - Wai Yee Chan
- Imaging Department, Gleneagles Kuala Lumpur, Jalan Ampang, Kuala Lumpur, Malaysia
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Oh K, Lee SE, Kim EK. 3-D breast nodule detection on automated breast ultrasound using faster region-based convolutional neural networks and U-Net. Sci Rep 2023; 13:22625. [PMID: 38114666 PMCID: PMC10730541 DOI: 10.1038/s41598-023-49794-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/12/2023] [Indexed: 12/21/2023] Open
Abstract
Mammography is currently the most commonly used modality for breast cancer screening. However, its sensitivity is relatively low in women with dense breasts. Dense breast tissues show a relatively high rate of interval cancers and are at high risk for developing breast cancer. As a supplemental screening tool, ultrasonography is a widely adopted imaging modality to standard mammography, especially for dense breasts. Lately, automated breast ultrasound imaging has gained attention due to its advantages over hand-held ultrasound imaging. However, automated breast ultrasound imaging requires considerable time and effort for reading because of the lengthy data. Hence, developing a computer-aided nodule detection system for automated breast ultrasound is invaluable and impactful practically. This study proposes a three-dimensional breast nodule detection system based on a simple two-dimensional deep-learning model exploiting automated breast ultrasound. Additionally, we provide several postprocessing steps to reduce false positives. In our experiments using the in-house automated breast ultrasound datasets, a sensitivity of [Formula: see text] with 8.6 false positives is achieved on unseen test data at best.
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Affiliation(s)
- Kangrok Oh
- Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Si Eun Lee
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin, Gyeonggi-do, 16995, Republic of Korea
| | - Eun-Kyung Kim
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin, Gyeonggi-do, 16995, Republic of Korea.
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Zhang J, Wu J, Zhou XS, Shi F, Shen D. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Semin Cancer Biol 2023; 96:11-25. [PMID: 37704183 DOI: 10.1016/j.semcancer.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023]
Abstract
Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.
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Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
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Ren W, Yan H, Zhao X, Jia M, Zhang S, Zhang J, Li Z, Ming L, Zhang Y, Li H, He L, Li X, Cheng X, Yue L, Zhou W, Qiao Y, Zhao F. Integration of Handheld Ultrasound or Automated Breast Ultrasound among Women with Negative Mammographic Screening Findings: A Multi-center Population-based Study in China. Acad Radiol 2023; 30 Suppl 2:S114-S126. [PMID: 37003874 DOI: 10.1016/j.acra.2023.02.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 04/03/2023]
Abstract
RATIONALE AND OBJECTIVES This study assessed the role of second-look automated breast ultrasound (ABUS) adjunct to mammography (MAM) versus MAM alone in asymptomatic women and compared it with supplementing handheld ultrasound (HHUS). MATERIALS AND METHODS Women aged 45 to 64 underwent HHUS, ABUS, and MAM among six hospitals in China from 2018 to 2022. We compared the screening performance of three strategies (MAM alone, MAM plus HHUS, and MAM plus ABUS) stratified by age groups and breast density. McNemar's test was used to assess differences in the cancer detection rate (CDR), the false-positive biopsy rate, sensitivity, and specificity of different strategies. RESULTS Of 19,171 women analyzed (mean [SD] age, 51.54 [4.61] years), 72 cases of breast cancer (3.76 per 1000) were detected. The detection rates for both HHUS and ABUS combined with MAM were statistically higher than those for MAM alone (all p < 0.001). There was no significant difference in cancer yields between the two integration strategies. The increase in CRD of the integrated strategies was higher in women aged 45-54 years with denser breasts compared with MAM alone (all p < 0.0167). In addition, the false-positive biopsy rate of MAM plus ABUS was lower than that of MAM plus HHUS (p = 0.025). Moreover, the retraction in ABUS was more frequent in cases detected among MAM-negative results. CONCLUSION Integrated ABUS or HHUS into MAM provided similar CDRs that were significantly higher than those for MAM alone in younger women (45-54 years) with denser breasts. ABUS has the potential to avoid unnecessary biopsies and provides specific image features to distinguish malignant tumors from HHUS.
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Affiliation(s)
- Wenhui Ren
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huijiao Yan
- Center for Global Health, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xuelian Zhao
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mengmeng Jia
- Center for Global Health, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Shaokai Zhang
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, Henan, China
| | - Junpeng Zhang
- Department of Breast Surgery, Xinmi Maternal and Child Health Care Hospital, Xinmi, Henan, China
| | - Zhifang Li
- Department of Preventive Medicine, Changzhi Medical College, Changzhi, Shanxi, China
| | - Lingling Ming
- Department of Breast Surgery, Zezhou Maternal and Child Health Care Hospital, Zezhou, Shanxi, China
| | - Yongdong Zhang
- Department of Ultrasound, Jungar Banner Maternal and Child Care Service Centre, Jungar, Inner Mongolia, China
| | - Huibing Li
- Department of Women Health, Chongzhou Maternal & Child Health Care Hospital, Chongzhou, Sichuan, China
| | - Lichun He
- Physical Examination Center, Mianyang Maternal & Child Health Care Hospital, Mianyang Children's Hospital, Mianyang, Sichuan, China
| | - Xiaofeng Li
- School of Public Health, Dalian Medical University, Dalian, Liaoning, China
| | - Xia Cheng
- Department of Women Health, Dalian Women and Children's Medical Group, Dalian, Liaoning, China
| | - Lu Yue
- Center for Global Health, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Wenjing Zhou
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Youlin Qiao
- Center for Global Health, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Fanghui Zhao
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Du W, Zhang L, Suh E, Lin D, Marcus C, Ozkan L, Ahuja A, Fernandez S, Shuvo II, Sadat D, Liu W, Li F, Chandrakasan AP, Ozmen T, Dagdeviren C. Conformable ultrasound breast patch for deep tissue scanning and imaging. SCIENCE ADVANCES 2023; 9:eadh5325. [PMID: 37506210 PMCID: PMC10382022 DOI: 10.1126/sciadv.adh5325] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023]
Abstract
Ultrasound is widely used for tissue imaging such as breast cancer diagnosis; however, fundamental challenges limit its integration with wearable technologies, namely, imaging over large-area curvilinear organs. We introduced a wearable, conformable ultrasound breast patch (cUSBr-Patch) that enables standardized and reproducible image acquisition over the entire breast with less reliance on operator training and applied transducer compression. A nature-inspired honeycomb-shaped patch combined with a phased array is guided by an easy-to-operate tracker that provides for large-area, deep scanning, and multiangle breast imaging capability. The in vitro studies and clinical trials reveal that the array using a piezoelectric crystal [Yb/Bi-Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3] (Yb/Bi-PIN-PMN-PT) exhibits a sufficient contrast resolution (~3 dB) and axial/lateral resolutions of 0.25/1.0 mm at 30 mm depth, allowing the observation of small cysts (~0.3 cm) in the breast. This research develops a first-of-its-kind ultrasound technology for breast tissue scanning and imaging that offers a noninvasive method for tracking real-time dynamic changes of soft tissue.
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Affiliation(s)
- Wenya Du
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Lin Zhang
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Emma Suh
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Dabin Lin
- School of Opto-electronical Engineering, Xi’an Technological University, Xi’an 710021, China
| | - Colin Marcus
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Lara Ozkan
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Avani Ahuja
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sara Fernandez
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | | | - David Sadat
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Weiguo Liu
- School of Opto-electronical Engineering, Xi’an Technological University, Xi’an 710021, China
| | - Fei Li
- Electronic Materials Research Laboratory, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Anantha P. Chandrakasan
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tolga Ozmen
- Division of Surgical Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Canan Dagdeviren
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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11
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Liu P, Zhao Y, Rong DD, Li KF, Wang YJ, Zhao J, Kang H. Diagnostic value of preoperative examination for evaluating margin status in breast cancer. World J Clin Cases 2023; 11:4852-4864. [PMID: 37583993 PMCID: PMC10424046 DOI: 10.12998/wjcc.v11.i20.4852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/08/2023] [Accepted: 06/21/2023] [Indexed: 07/11/2023] Open
Abstract
BACKGROUND A positive resection margin is a major risk factor for local breast cancer recurrence after breast-conserving surgery (BCS). Preoperative imaging examinations are frequently employed to assess the surgical margin. AIM To investigate the role and value of preoperative imaging examinations [magnetic resonance imaging (MRI), molybdenum target, and ultrasound] in evaluating margins for BCS. METHODS A retrospective study was conducted on 323 breast cancer patients who met the criteria for BCS and consented to the procedure from January 2014 to July 2021. The study gathered preoperative imaging data (MRI, ultrasound, and molybdenum target examination) and intraoperative and postoperative pathological information. Based on their BCS outcomes, patients were categorized into positive and negative margin groups. Subsequently, the patients were randomly split into a training set (226 patients, approximately 70%) and a validation set (97 patients, approximately 30%). The imaging and pathological information was analyzed and summarized using R software. Non-conditional logistic regression and LASSO regression were conducted in the validation set to identify factors that might influence the failure of BCS. A column chart was generated and applied to the validation set to examine the relationship between pathological margin range and prognosis. This study aims to identify the risk factors associated with failure in BCS. RESULTS The multivariate non-conditional logistic regression analysis demonstrated that various factors raise the risk of positive margins following BCS. These factors comprise non-mass enhancement (NME) on dynamic contrast-enhanced MRI, multiple focal vascular signs around the lesion on MRI, tumor size exceeding 2 cm, type III time-signal intensity curve, indistinct margins on molybdenum target examination, unclear margins on ultrasound examination, and estrogen receptor (ER) positivity in immunohistochemistry. LASSO regression was additionally employed in this study to identify four predictive factors for the model: ER, molybdenum target tumor type (MT Xmd Shape), maximum intensity projection imaging feature, and lesion type on MRI. The model constructed with these predictive factors exhibited strong consistency with the real-world scenario in both the training set and validation set. Particularly, the outcomes of the column chart model accurately predicted the likelihood of positive margins in BCS. CONCLUSION The proposed column chart model effectively predicts the success of BCS for breast cancer. The model utilizes preoperative ultrasound, molybdenum target, MRI, and core needle biopsy pathology evaluation results, all of which align with the real-world scenario. Hence, our model can offer dependable guidance for clinical decision-making concerning BCS.
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Affiliation(s)
- Peng Liu
- Department of General Surgery, Center for Thyroid and Breast Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- Department of General Surgery, Beijing Fengtai Hospital, Beijing 100071, China
| | - Ye Zhao
- Department of General Surgery, Center for Thyroid and Breast Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Dong-Dong Rong
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Kai-Fu Li
- Department of General Surgery, Center for Thyroid and Breast Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Ya-Jun Wang
- Department of General Surgery, Center for Thyroid and Breast Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Jing Zhao
- Department of General Surgery, Center for Thyroid and Breast Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Hua Kang
- Department of General Surgery, Center for Thyroid and Breast Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
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12
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Liu P, Zhao Y, Rong DD, Li KF, Wang YJ, Zhao J, Kang H. Diagnostic value of preoperative examination for evaluating margin status in breast cancer. World J Clin Cases 2023; 11:4848-4860. [DOI: 10.12998/wjcc.v11.i20.4848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/08/2023] [Accepted: 06/21/2023] [Indexed: 07/06/2023] Open
Abstract
BACKGROUND A positive resection margin is a major risk factor for local breast cancer recurrence after breast-conserving surgery (BCS). Preoperative imaging examinations are frequently employed to assess the surgical margin.
AIM To investigate the role and value of preoperative imaging examinations [magnetic resonance imaging (MRI), molybdenum target, and ultrasound] in evaluating margins for BCS.
METHODS A retrospective study was conducted on 323 breast cancer patients who met the criteria for BCS and consented to the procedure from January 2014 to July 2021. The study gathered preoperative imaging data (MRI, ultrasound, and molybdenum target examination) and intraoperative and postoperative pathological information. Based on their BCS outcomes, patients were categorized into positive and negative margin groups. Subsequently, the patients were randomly split into a training set (226 patients, approximately 70%) and a validation set (97 patients, approximately 30%). The imaging and pathological information was analyzed and summarized using R software. Non-conditional logistic regression and LASSO regression were conducted in the validation set to identify factors that might influence the failure of BCS. A column chart was generated and applied to the validation set to examine the relationship between pathological margin range and prognosis. This study aims to identify the risk factors associated with failure in BCS.
RESULTS The multivariate non-conditional logistic regression analysis demonstrated that various factors raise the risk of positive margins following BCS. These factors comprise non-mass enhancement (NME) on dynamic contrast-enhanced MRI, multiple focal vascular signs around the lesion on MRI, tumor size exceeding 2 cm, type III time-signal intensity curve, indistinct margins on molybdenum target examination, unclear margins on ultrasound examination, and estrogen receptor (ER) positivity in immunohistochemistry. LASSO regression was additionally employed in this study to identify four predictive factors for the model: ER, molybdenum target tumor type (MT Xmd Shape), maximum intensity projection imaging feature, and lesion type on MRI. The model constructed with these predictive factors exhibited strong consistency with the real-world scenario in both the training set and validation set. Particularly, the outcomes of the column chart model accurately predicted the likelihood of positive margins in BCS.
CONCLUSION The proposed column chart model effectively predicts the success of BCS for breast cancer. The model utilizes preoperative ultrasound, molybdenum target, MRI, and core needle biopsy pathology evaluation results, all of which align with the real-world scenario. Hence, our model can offer dependable guidance for clinical decision-making concerning BCS.
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Affiliation(s)
- Peng Liu
- Department of General Surgery, Center for Thyroid and Breast Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- Department of General Surgery, Beijing Fengtai Hospital, Beijing 100071, China
| | - Ye Zhao
- Department of General Surgery, Center for Thyroid and Breast Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Dong-Dong Rong
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Kai-Fu Li
- Department of General Surgery, Center for Thyroid and Breast Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Ya-Jun Wang
- Department of General Surgery, Center for Thyroid and Breast Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Jing Zhao
- Department of General Surgery, Center for Thyroid and Breast Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Hua Kang
- Department of General Surgery, Center for Thyroid and Breast Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
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Xie Y, Chen Y, Wang Q, Li B, Shang H, Jing H. Early Prediction of Response to Neoadjuvant Chemotherapy Using Quantitative Parameters on Automated Breast Ultrasound Combined with Contrast-Enhanced Ultrasound in Breast Cancer. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:1638-1646. [PMID: 37100671 DOI: 10.1016/j.ultrasmedbio.2023.03.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 03/15/2023] [Accepted: 03/23/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVE This prospective study was aimed at evaluating the role of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in the early prediction of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. METHODS Forty-three patients with pathologically confirmed invasive breast cancer treated with NAC were included. The standard for evaluation of response to NAC was based on surgery within 21 d of completing treatment. The patients were classified as having a pathological complete response (pCR) and a non-pCR. All patients underwent CEUS and ABUS 1 wk before receiving NAC and after two treatment cycles. The rising time (RT), time to peak (TTP), peak intensity (PI), wash-in slope (WIS) and wash-in area under the curve (Wi-AUC) were measured on the CEUS images before and after NAC. The maximum tumor diameters in the coronal and sagittal planes were measured on ABUS, and the tumor volume (V) was calculated. The difference (∆) in each parameter between the two treatment time points was compared. Binary logistic regression analysis was used to identify the predictive value of each parameter. RESULTS ∆V, ∆TTP and ∆PI were independent predictors of pCR. The CEUS-ABUS model achieved the highest AUC (0.950), followed by those based on CEUS (0.918) and ABUS (0.891) alone. CONCLUSION The CEUS-ABUS model could be used clinically to optimize the treatment of patients with breast cancer.
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Affiliation(s)
- Yongwei Xie
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yu Chen
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
| | - Qiucheng Wang
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
| | - Bo Li
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
| | - Haitao Shang
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
| | - Hui Jing
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China.
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14
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Saharkhiz N, Kamimura HAS, Konofagou EE. An Efficient and Multi-Focal Focused Ultrasound Technique for Harmonic Motion Imaging. IEEE Trans Biomed Eng 2023; 70:1150-1161. [PMID: 36191094 PMCID: PMC10067540 DOI: 10.1109/tbme.2022.3211465] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Harmonic motion imaging (HMI) is an ultrasound-based elasticity imaging technique that utilizes oscillatory acoustic radiation force to estimate the mechanical properties of tissues, as well as monitor high-intensity focused ultrasound (HIFU) treatment. Conventionally, in HMI, a focused ultrasound (FUS) transducer generates oscillatory tissue displacements, and an imaging transducer acquires channel data for displacement estimation, with each transducer being driven with a separate system. The fixed position of the FUS focal spot requires mechanical translation of the transducers, which can be a time-consuming and challenging procedure. In this study, we developed and characterized a new HMI system with a multi-element FUS transducer with the capability of electronic focal steering of ±5 mm and ±2 mm from the geometric focus in the axial and lateral directions, respectively. A pulse sequence was developed to drive both the FUS and imaging transducers using a single ultrasound data acquisition (DAQ) system. The setup was validated on a tissue-mimicking phantom with embedded inclusions. Integrating beam steering with the mechanical translation of the transducers resulted in a consistent high contrast-to-noise ratio (CNR) for the inclusions with Young's moduli of 22 and 44 kPa within a 5-kPa background while the data acquisition speed is increased by 4.5-5.2-fold compared to the case when only mechanical movements were applied. The feasibility of simultaneous generation of multiple foci and tracking the induced displacements is demonstrated in phantoms for applications where imaging or treatment of a larger region is needed. Moreover, preliminary feasibility is shown in a human subject with a breast tumor, where the mean HMI displacement within the tumor was about 4 times lower than that within perilesional tissues. The proposed HMI system facilitates data acquisition in terms of flexibility and speed and can be potentially used in the clinic for breast cancer imaging and treatment.
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15
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Dan Q, Zheng T, Liu L, Sun D, Chen Y. Ultrasound for Breast Cancer Screening in Resource-Limited Settings: Current Practice and Future Directions. Cancers (Basel) 2023; 15:cancers15072112. [PMID: 37046773 PMCID: PMC10093585 DOI: 10.3390/cancers15072112] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/09/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023] Open
Abstract
Breast cancer (BC) is the most prevalent cancer among women globally. Cancer screening can reduce mortality and improve women’s health. In developed countries, mammography (MAM) has been primarily utilized for population-based BC screening for several decades. However, it is usually unavailable in low-resource settings due to the lack of equipment, personnel, and time necessary to conduct and interpret the examinations. Ultrasound (US) with high detection sensitivity for women of younger ages and with dense breasts has become a supplement to MAM for breast examination. Some guidelines suggest using US as the primary screening tool in certain settings where MAM is unavailable and infeasible, but global recommendations have not yet reached a unanimous consensus. With the development of smart devices and artificial intelligence (AI) in medical imaging, clinical applications and preclinical studies have shown the potential of US combined with AI in BC screening. Nevertheless, there are few comprehensive reviews focused on the role of US in screening BC in underserved conditions, especially in technological, economical, and global perspectives. This work presents the benefits, limitations, advances, and future directions of BC screening with technology-assisted and resource-appropriate strategies, which may be helpful to implement screening initiatives in resource-limited countries.
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Affiliation(s)
- Qing Dan
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen 518036, China
| | - Tingting Zheng
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen 518036, China
| | - Li Liu
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen 518036, China
| | - Desheng Sun
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen 518036, China
| | - Yun Chen
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen 518036, China
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16
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The Potential of Adding Mammography to Handheld Ultrasound or Automated Breast Ultrasound to Reduce Unnecessary Biopsies in BI-RADS Ultrasound Category 4a: A Multicenter Hospital-Based Study in China. Curr Oncol 2023; 30:3301-3314. [PMID: 36975464 PMCID: PMC10047589 DOI: 10.3390/curroncol30030251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/04/2023] [Accepted: 03/09/2023] [Indexed: 03/14/2023] Open
Abstract
The appropriate management strategies for BI-RADS category 4a lesions among handheld ultrasound (HHUS) remain a matter of debate. We aimed to explore the role of automated breast ultrasound (ABUS) or the second-look mammography (MAM) adjunct to ultrasound (US) of 4a masses to reduce unnecessary biopsies. Women aged 30 to 69 underwent HHUS and ABUS from 2016 to 2017 at five high-level hospitals in China, with those aged 40 or older also accepting MAM. Logistic regression analysis assessed image variables correlated with false-positive lesions in US category 4a. Unnecessary biopsies, invasive cancer (IC) yields, and diagnostic performance among different biopsy thresholds were compared. A total of 1946 women (44.9 ± 9.8 years) were eligible for analysis. The false-positive rate of category 4a in ABUS was almost 65.81% (77/117), which was similar to HHUS (67.55%; 127/188). Orientation, architectural distortion, and duct change were independent factors associated with the false-positive lesions in 4a of HHUS, whereas postmenopausal, calcification, and architectural distortion were significant features of ABUS (all p < 0.05). For HHUS, both unnecessary biopsy rate and IC yields were significantly reduced when changing biopsy thresholds by adding MAM for US 4a in the total population (scenario #1:BI-RADS 3, 4, and 5; scenario #2: BI-RADS 4 and 5) compared with the current scenario (all p < 0.05). Notably, scenario #1 reduced false-positive biopsies without affecting IC yields when compared to the current scenario for ABUS (p < 0.001; p = 0.125). The higher unnecessary biopsy rate of category 4a by ABUS was similar to HHUS. However, the second-look MAM adjunct to ABUS has the potential to safely reduce false-positive biopsies compared with HHUS.
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Diagnostic Performance of Prototype Handheld Ultrasound According to the Fifth Edition of BI-RADS for Breast Ultrasound Compared with Automated Breast Ultrasound among Females with Positive Lumps. Diagnostics (Basel) 2023; 13:diagnostics13061065. [PMID: 36980373 PMCID: PMC10047253 DOI: 10.3390/diagnostics13061065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 03/16/2023] Open
Abstract
(1) Objective: To evaluate the diagnostic performance of prototype handheld ultrasound compared to automated breast ultrasound, according to the fifth edition of BI-RADS categorization, among females with positive lumps. (2) Methods: A total of 1004 lesions in 162 participants who underwent both prototype handheld ultrasound and automated breast ultrasound were included. Two radiologists and a sonographer independently evaluated the sonographic features of each lesion according to the fifth BI-RADS edition. The kappa coefficient (κ) was calculated for each BI-RADS descriptor and final assessment category. The cross-tabulation was performed to see whether there were differences between the ABUS and prototype HHUS results. Specificity and sensitivity were evaluated and compared using the McNamar test. (3) Results: ABUS and prototype HHUS observers found the same number of breast lesions in the 324 breasts of the 162 respondents. There was no significant difference in the mean lesion size, with a maximum mean length dimension of 0.48 ± 0.33 cm. The assessment of the lesion’s shape, orientation, margin, echo pattern, posterior acoustic features, and calcification was obtained with good to excellent agreements between ABUS and prototype HHUS observers (κ = 0.70–1.0). There was absolutely no significant difference between ABUS and prototype HHUS in assessment of lesion except for lesion orientation p = 0.00. Diagnostic accuracy (99.8% and 97.7–98.9%), sensitivity (99.5% and 98.0–99.0%), specificity (99.8% and 99.6–99.8%), positive predictive value (98.1% and 90.3–96.2%), negative predictive value (90.0% and 84.4–88.7%), and areas under the curve (0.98 and 0.83–0.92; p < 0.05) were not significantly different between ABUS and prototype HHUS observers. (4) Conclusion: According to the fifth BI-RADS edition, automated breast ultrasound is not statistically significantly different from prototype handheld ultrasound with regard to interobserver variability and diagnostic performance.
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Catalano O, Fusco R, De Muzio F, Simonetti I, Palumbo P, Bruno F, Borgheresi A, Agostini A, Gabelloni M, Varelli C, Barile A, Giovagnoni A, Gandolfo N, Miele V, Granata V. Recent Advances in Ultrasound Breast Imaging: From Industry to Clinical Practice. Diagnostics (Basel) 2023; 13:diagnostics13050980. [PMID: 36900124 PMCID: PMC10000574 DOI: 10.3390/diagnostics13050980] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Breast ultrasound (US) has undergone dramatic technological improvement through recent decades, moving from a low spatial resolution, grayscale-limited technique to a highly performing, multiparametric modality. In this review, we first focus on the spectrum of technical tools that have become commercially available, including new microvasculature imaging modalities, high-frequency transducers, extended field-of-view scanning, elastography, contrast-enhanced US, MicroPure, 3D US, automated US, S-Detect, nomograms, images fusion, and virtual navigation. In the subsequent section, we discuss the broadened current application of US in breast clinical scenarios, distinguishing among primary US, complementary US, and second-look US. Finally, we mention the still ongoing limitations and the challenging aspects of breast US.
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Affiliation(s)
- Orlando Catalano
- Department of Radiology, Istituto Diagnostico Varelli, 80126 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Correspondence:
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Igino Simonetti
- Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli”, 80131 Naples, Italy
| | - Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Federico Bruno
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| | - Michela Gabelloni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, 56126 Pisa, Italy
| | - Carlo Varelli
- Department of Radiology, Istituto Diagnostico Varelli, 80126 Naples, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, 67100 L’Aquila, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Vincenza Granata
- Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli”, 80131 Naples, Italy
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Tittmann J, Csanádi M, Ágh T, Széles G, Vokó Z, Kallai Á. Development of a breast cancer screening protocol to use automated breast ultrasound in a local setting. Front Public Health 2023; 10:1071317. [PMID: 36684917 PMCID: PMC9846565 DOI: 10.3389/fpubh.2022.1071317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 12/12/2022] [Indexed: 01/05/2023] Open
Abstract
Introduction The sensitivity of mammography screening is lower in women with dense breast. Increasing the efficacy of breast cancer screening have received special attention recently. The automated breast ultrasound (ABUS) shows promising results to complement mammography. Our aim was to expand the existing breast cancer screening protocol with ABUS within a Hungarian pilot project. Methods First, we developed a protocol for the screening process focusing on integrating ABUS to the current practice. Consensus among clinical experts was achieved considering information from the literature and the actual opportunities of the hospital. Then we developed a protocol for evaluation that ensures systematic data collection and monitoring of screening with mammography and ABUS. We identified indicators based on international standards and adapted them to local setting. We considered their feasibility from the data source and timeframe perspective. The protocol was developed in a partnership of researchers, clinicians and hospital managers. Results The process of screening activity was described in a detailed flowchart. Human and technological resource requirements and communication activities were defined. We listed 23 monitoring indicators to evaluate the screening program and checked the feasibility to calculate these indicators based on local data collection and other sources. Partnership between researchers experienced in planning and evaluating screening programs, interested clinicians, and hospital managers resulted in a locally implementable, evidence-based screening protocol. Discussion The experience and knowledge gained on the implementation of the ABUS technology could generate real-world data to support the decision on using the technology at national level.
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Affiliation(s)
- Judit Tittmann
- Semmelweis University, Center for Health Technology Assessment, Budapest, Hungary
| | | | - Tamás Ágh
- Syreon Research Institute, Budapest, Hungary
| | | | - Zoltán Vokó
- Semmelweis University, Center for Health Technology Assessment, Budapest, Hungary,Syreon Research Institute, Budapest, Hungary,*Correspondence: Zoltán Vokó ✉
| | - Árpád Kallai
- Csongrád-Csanád Regional Health Center, Hódmezovásárhely, Hungary
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20
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Zhang J, Cai L, Pan X, Chen L, Chen M, Yan D, Liu J, Luo L. Comparison and risk factors analysis of multiple breast cancer screening methods in the evaluation of breast non-mass-like lesions. BMC Med Imaging 2022; 22:202. [PMID: 36404330 PMCID: PMC9677910 DOI: 10.1186/s12880-022-00921-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 10/26/2022] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVE To compare multiple breast cancer screening methods for evaluating breast non-mass-like lesions (NMLs), and investigate new best screening method for breast non-mass-like lesions and the value of the lexicon of ACR BI-RADS in NML evaluation. METHODS This retrospective study examined 253 patients aged 24-68 years who were diagnosed with breast NMLs and described the lexicon of ACR BI-RADS from April 2017 to December 2019. All lesions were evaluated by HHUS, MG, and ABUS to determine BI-RADS category, and underwent pathological examination within six months or at least 2 years of follow-up. The sensitivity, specificity, accuracy, positive predictive values (PPV), and negative predictive values (NPV) of MG, HHUS and ABUS in the prediction of malignancy were compared. Independent risk factors for malignancy were assessed using non-conditional logistic regression. RESULTS HHUS, MG and ABUS findings significantly differed between benign and malignant breast NML, including internal echo, hyperechoic spot, peripheral blood flow, internal blood flow, catheter change, peripheral change, coronal features of ABUS, and structural distortion, asymmetry, and calcification in MG. ABUS is superior to MG and HHUS in sensitivity, specificity, PPV, NPV, as well as in evaluating the necessity of biopsy and accuracy in identifying malignancy. MG was superior to HHUS in specificity, PPV, and accuracy in evaluating the need for biopsy. CONCLUSIONS ABUS was superior to HHUS and MG in evaluating the need for biopsy in breast NMLs. Compared to each other, HHUS and MG had their own relative advantages. Internal blood flow, calcification, and coronal plane feature was independent risk factors in NMLs Management, and different screening methods had their own advantages in NML management. The lexicon of ACR BI-RADS could be used not only in the evaluation of mass lesions, but also in the evaluation of NML.
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Affiliation(s)
- Jianxing Zhang
- grid.258164.c0000 0004 1790 3548Department of Medical Imaging Center, The First Affiliated Hospital, Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangzhou, 510630 Guangdong Province China ,grid.411866.c0000 0000 8848 7685Department of Ultrasound, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, No. 111, Dade Road, Yuexiu District, Guangzhou, 510120 Guangdong Province China
| | - Lishan Cai
- grid.411866.c0000 0000 8848 7685Department of Ultrasound, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, No. 16, Jichang Road, Baiyun District, Guangzhou, 510403 Guangdong Province China
| | - Xiyang Pan
- grid.411866.c0000 0000 8848 7685Department of Ultrasound, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, No. 111, Dade Road, Yuexiu District, Guangzhou, 510120 Guangdong Province China
| | - Ling Chen
- grid.411866.c0000 0000 8848 7685Department of Ultrasound, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, No. 111, Dade Road, Yuexiu District, Guangzhou, 510120 Guangdong Province China
| | - Miao Chen
- grid.411866.c0000 0000 8848 7685Department of Ultrasound, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, No. 111, Dade Road, Yuexiu District, Guangzhou, 510120 Guangdong Province China
| | - Dan Yan
- grid.411866.c0000 0000 8848 7685Department of Ultrasound, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, No. 111, Dade Road, Yuexiu District, Guangzhou, 510120 Guangdong Province China
| | - Jia Liu
- grid.411866.c0000 0000 8848 7685Department of Ultrasound, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, No. 111, Dade Road, Yuexiu District, Guangzhou, 510120 Guangdong Province China
| | - Liangping Luo
- grid.258164.c0000 0004 1790 3548Department of Medical Imaging Center, The First Affiliated Hospital, Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangzhou, 510630 Guangdong Province China
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21
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Wang S, Liu H, Yang T, Huang M, Zheng B, Wu T, Han L, Zhang Y, Ren J. Machine learning based on automated breast volume scanner ( ABVS) radiomics for differential diagnosis of benign and malignant BI‐RADS 4 lesions. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022; 32:1577-1587. [DOI: 10.1002/ima.22724] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 02/25/2022] [Indexed: 09/11/2023]
Abstract
AbstractBI‐RADS category 4 represents possibly malignant lesions and biopsy is recommended to distinguish benign and malignant. However, studies revealed that up to 67%–78% of BI‐RADS 4 lesions proved to be benign, but received unnecessary biopsies, which may cause unnecessary anxiety and discomfort to patients and increase the burden on the healthcare system. In this prospective study, machine learning (ML) based on the emerging breast ultrasound technology‐automated breast volume scanner (ABVS) was constructed to distinguish benign and malignant BI‐RADS 4 lesions and compared with different experienced radiologists. A total of 223 pathologically confirmed BI‐RADS 4 lesions were recruited and divided into training and testing cohorts. Radiomics features were extracted from axial, sagittal, and coronal ABVS images for each lesion. Seven feature selection methods and 13 ML algorithms were used to construct different ML pipelines, of which the DNN‐RFE (combination of recursive feature elimination and deep neural networks) had the best performance in both training and testing cohorts. The AUC value of the DNN‐RFE was significantly higher than less experienced radiologist at Delong's test (0.954 vs. 0.776, p = 0.004). Additionally, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the DNN‐RFE were 88.9%, 83.3%, 95.2%, 83.3%, and 95.2%, which also significantly better than less experienced radiologist at McNemar's test (p = 0.043). Therefore, ML based on ABVS radiomics may be a potential method to non‐invasively distinguish benign and malignant BI‐RADS 4 lesions.
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Affiliation(s)
- Shi‐jie Wang
- Department of Medical Ultrasonics The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Hua‐qing Liu
- Artificial Intelligence Innovation Center Research Institute of Tsinghua Guangzhou China
| | - Tao Yang
- Department of Ultrasound The Affiliated Hospital of Southwest Medical University Sichuan China
| | - Ming‐quan Huang
- Department of Breast Surgery The Affiliated Hospital of Southwest Medical University Sichuan China
| | - Bo‐wen Zheng
- Department of Medical Ultrasonics The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Tao Wu
- Department of Medical Ultrasonics The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Lan‐qing Han
- Artificial Intelligence Innovation Center Research Institute of Tsinghua Guangzhou China
| | - Yong Zhang
- Department of Nuclear Medicine The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Jie Ren
- Department of Medical Ultrasonics The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
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22
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Park CKS, Xing S, Papernick S, Orlando N, Knull E, Toit CD, Bax JS, Gardi L, Barker K, Tessier D, Fenster A. Spatially tracked whole-breast three-dimensional ultrasound system toward point-of-care breast cancer screening in high-risk women with dense breasts. Med Phys 2022; 49:3944-3962. [PMID: 35319105 DOI: 10.1002/mp.15632] [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: 11/25/2021] [Revised: 03/16/2022] [Accepted: 03/17/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Mammographic screening has reduced mortality in women through the early detection of breast cancer. However, the sensitivity for breast cancer detection is significantly reduced in women with dense breasts, in addition to being an independent risk factor. Ultrasound (US) has been proven effective in detecting small, early-stage, and invasive cancers in women with dense breasts. PURPOSE To develop an alternative, versatile, and cost-effective spatially tracked three-dimensional (3D) US system for whole-breast imaging. This paper describes the design, development, and validation of the spatially tracked 3DUS system, including its components for spatial tracking, multi-image registration and fusion, feasibility for whole-breast 3DUS imaging and multi-planar visualization in tissue-mimicking phantoms, and a proof-of-concept healthy volunteer study. METHODS The spatially tracked 3DUS system contains (a) a six-axis manipulator and counterbalanced stabilizer, (b) an in-house quick-release 3DUS scanner, adaptable to any commercially available US system, and removable, allowing for handheld 3DUS acquisition and two-dimensional US imaging, and (c) custom software for 3D tracking, 3DUS reconstruction, visualization, and spatial-based multi-image registration and fusion of 3DUS images for whole-breast imaging. Spatial tracking of the 3D position and orientation of the system and its joints (J1-6 ) were evaluated in a clinically accessible workspace for bedside point-of-care (POC) imaging. Multi-image registration and fusion of acquired 3DUS images were assessed with a quadrants-based protocol in tissue-mimicking phantoms and the target registration error (TRE) was quantified. Whole-breast 3DUS imaging and multi-planar visualization were evaluated with a tissue-mimicking breast phantom. Feasibility for spatially tracked whole-breast 3DUS imaging was assessed in a proof-of-concept healthy male and female volunteer study. RESULTS Mean tracking errors were 0.87 ± 0.52, 0.70 ± 0.46, 0.53 ± 0.48, 0.34 ± 0.32, 0.43 ± 0.28, and 0.78 ± 0.54 mm for joints J1-6 , respectively. Lookup table (LUT) corrections minimized the error in joints J1 , J2 , and J5 . Compound motions exercising all joints simultaneously resulted in a mean tracking error of 1.08 ± 0.88 mm (N = 20) within the overall workspace for bedside 3DUS imaging. Multi-image registration and fusion of two acquired 3DUS images resulted in a mean TRE of 1.28 ± 0.10 mm. Whole-breast 3DUS imaging and multi-planar visualization in axial, sagittal, and coronal views were demonstrated with the tissue-mimicking breast phantom. The feasibility of the whole-breast 3DUS approach was demonstrated in healthy male and female volunteers. In the male volunteer, the high-resolution whole-breast 3DUS acquisition protocol was optimized without the added complexities of curvature and tissue deformations. With small post-acquisition corrections for motion, whole-breast 3DUS imaging was performed on the healthy female volunteer showing relevant anatomical structures and details. CONCLUSIONS Our spatially tracked 3DUS system shows potential utility as an alternative, accurate, and feasible whole-breast approach with the capability for bedside POC imaging. Future work is focused on reducing misregistration errors due to motion and tissue deformations, to develop a robust spatially tracked whole-breast 3DUS acquisition protocol, then exploring its clinical utility for screening high-risk women with dense breasts.
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Affiliation(s)
- Claire Keun Sun Park
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Shuwei Xing
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada.,School of Biomedical Engineering, Faculty of Engineering, Western University, London, Ontario, Canada
| | - Samuel Papernick
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Nathan Orlando
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Eric Knull
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada.,School of Biomedical Engineering, Faculty of Engineering, Western University, London, Ontario, Canada
| | - Carla Du Toit
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada.,School of Kinesiology, Faculty of Health Sciences, Western University, London, Ontario, Canada
| | - Jeffrey Scott Bax
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Lori Gardi
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Kevin Barker
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - David Tessier
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Aaron Fenster
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada.,School of Biomedical Engineering, Faculty of Engineering, Western University, London, Ontario, Canada.,Division of Imaging Sciences, Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
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23
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Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions. Diagnostics (Basel) 2022; 12:diagnostics12010172. [PMID: 35054339 PMCID: PMC8774686 DOI: 10.3390/diagnostics12010172] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/07/2022] [Accepted: 01/10/2022] [Indexed: 01/22/2023] Open
Abstract
Improving the assessment of breast imaging reporting and data system (BI-RADS) 4 lesions and reducing unnecessary biopsies are urgent clinical issues. In this prospective study, a radiomic nomogram based on the automated breast volume scanner (ABVS) was constructed to identify benign and malignant BI-RADS 4 lesions and evaluate its value in reducing unnecessary biopsies. A total of 223 histologically confirmed BI-RADS 4 lesions were enrolled and assigned to the training and validation cohorts. A radiomic score was generated from the axial, sagittal, and coronal ABVS images. Combining the radiomic score and clinical-ultrasound factors, a radiomic nomogram was developed by multivariate logistic regression analysis. The nomogram integrating the radiomic score, lesion size, and BI-RADS 4 subcategories showed good discrimination between malignant and benign BI-RADS 4 lesions in the training (AUC, 0.959) and validation (AUC, 0.925) cohorts. Moreover, 42.5% of unnecessary biopsies would be reduced by using the nomogram, but nine (4%) malignant BI-RADS 4 lesions were unfortunately missed, of which 4A (77.8%) and small-sized (<10 mm) lesions (66.7%) accounted for the majority. The ABVS radiomics nomogram may be a potential tool to reduce unnecessary biopsies of BI-RADS 4 lesions, but its ability to detect small BI-RADS 4A lesions needs to be improved.
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24
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Park KW, Ko EY, Park S, Han BK, Ko ES, Choi JS, Kwon MR. Reproducibility of Automated Breast Ultrasonography and Handheld Ultrasonography for Breast Lesion Size Measurement. Ultrasound Q 2022; 38:13-17. [PMID: 35001027 DOI: 10.1097/ruq.0000000000000568] [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: 11/25/2022]
Abstract
ABSTRACT The purpose of our study was to evaluate the reproducibility of size measurement of breast lesions using automated breast ultrasonography (ABUS) compared with that with handheld ultrasonography (HHUS). Three breast radiologists performed HHUS and measured the lesions size in 2 different phantoms: lesions with various shape, size, and same stiffness (phantom 1) and lesions with same shape, size, and various stiffness (phantom 2). After 1 month, the same radiologists measured the lesion size of the same breast phantoms in the images obtained using ABUS. We evaluated interobserver variability between 3 radiologists in ABUS and HHUS, and intraobserver variability of radiologists between ABUS and HHUS. Intraclass correlation coefficient (ICC) was used in statistical analysis. The measured size of lesions on HHUS was slightly larger than that on ABUS in both phantom 1 and 2, although not statistically significant (P = 0.314, P = 0.858). There were no significant differences in size measurements between the radiologists' measurements and the reference size in phantom 2 (P = 0.862). The ICCs for the interobserver agreement between the 3 radiologists were 0.98 to 0.99 on ABUS and 0.99 to 1.00 on HHUS, respectively. The ICCs for the intraobserver agreement between ABUS and HHUS were 0.97 to 0.97 in phantom 1 and 0.98 to 0.99 in phantom 2. In conclusion, ABUS showed excellent interobserver and intraobserver agreement with HHUS in measuring size of the lesions, regardless of shape, size, and stiffness. Therefore, ABUS mixed with HHUS can be used reliably in following up breast lesions size.
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Affiliation(s)
- Ko Woon Park
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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25
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Chowdary J, Yogarajah P, Chaurasia P, Guruviah V. A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images. ULTRASONIC IMAGING 2022; 44:3-12. [PMID: 35128997 PMCID: PMC8902030 DOI: 10.1177/01617346221075769] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Breast cancer is one of the most fatal diseases leading to the death of several women across the world. But early diagnosis of breast cancer can help to reduce the mortality rate. So an efficient multi-task learning approach is proposed in this work for the automatic segmentation and classification of breast tumors from ultrasound images. The proposed learning approach consists of an encoder, decoder, and bridge blocks for segmentation and a dense branch for the classification of tumors. For efficient classification, multi-scale features from different levels of the network are used. Experimental results show that the proposed approach is able to enhance the accuracy and recall of segmentation by 1.08%, 4.13%, and classification by 1.16%, 2.34%, respectively than the methods available in the literature.
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Affiliation(s)
| | - Pratheepan Yogarajah
- University of Ulster, Londonderry, UK
- Pratheepan Yogarajah, University of Ulster, Northland Road, Magee Campus, Londonderry, Northern Ireland BT48 7JL, UK.
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26
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Does automated breast ultrasound (ABUS) add to breast tomosynthesis (DBT) in assessment of lesions in dense breasts? THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [DOI: 10.1186/s43055-021-00556-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
As mammography has its known limitations in dense breast, additional imaging is usually needed. We aimed to evaluate the role of automated breast ultrasound in addition to tomosynthesis in detection and diagnosis of breast lesions in dense breasts. Seventy patients with dense breasts subjected to full-field digital mammography (FFDM) including digital breast tomosynthesis (DBT) and automated breast ultrasound (ABUS). Both studies were evaluated by two experienced radiologists to assess breast composition, mass characterization, asymmetry, calcification, axillary lymphadenopathy, extent of disease (EOD), skin thickening, retraction, architectural distortion, and BIRADS classification. All breast masses were interpreted as above described and then correlated with final pathological diagnosis.
Results
Study included 70 females presenting with different types of breast lesions. Eighty-two masses were detected: 53 benign (n = 53/82), 29 malignant (n = 29/82). Histopathology of the masses was reached by core biopsy (n = 30), FNAC (n = 14), and excisional biopsy (n = 11). The rest of the masses (n = 27/82) were confirmed by their characteristic sonographic appearances; 20 cases of multiple bilateral anechoic simple cysts, 7 typical fibroadenomas showed stationary course on follow-up. As regards the final BIRADS score given for both modalities, tomosynthesis showed accuracy of 93.1% in characterization of malignant masses with accuracy of 94.3% in benign masses, on the other hand automated ultrasound showed 100% accuracy in characterization of malignant masses with 98.1% accuracy in benign masses.
Conclusion
Adding ABUS to tomosynthesis has proven a valuable imaging tool for characterization of breast lesions in dense breasts both as screening and diagnostic tool. They proved to be more sensitive and specific than digital mammography alone in showing tissue overlap, tumor characterization, lesion margins, extent, and multiplicity of malignant lesions.
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Nicosia L, Addante F, Bozzini AC, Latronico A, Montesano M, Meneghetti L, Tettamanzi F, Frassoni S, Bagnardi V, De Santis R, Pesapane F, Fodor CI, Mastropasqua MG, Cassano E. Evaluation of computer-aided diagnosis in breast ultrasonography: Improvement in diagnostic performance of inexperienced radiologists. Clin Imaging 2021; 82:150-155. [PMID: 34826773 DOI: 10.1016/j.clinimag.2021.11.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/11/2021] [Accepted: 11/07/2021] [Indexed: 11/15/2022]
Abstract
PURPOSE To evaluate if a computer-aided diagnosis (CAD) system on ultrasound (US) can improve the diagnostic performance of inexperienced radiologists. METHODS We collected ultrasound images of 256 breast lesions taken between March and May 2020. We asked two experienced and two inexperienced radiologists to retrospectively review the US features of each breast lesion according to the Breast Imaging Reporting and Data System (BI-RADS) categories. A CAD examination with S-Detect™ software (Samsung Healthcare, Seoul, South Korea) was conducted retrospectively by another uninvolved radiologist blinded to the BIRADS values previously attributed to the lesions. Diagnostic performances of experienced and inexperienced radiologists and CAD were compared and the inter-observer agreement among radiologists was calculated. RESULTS The diagnostic performance of the experienced group in terms of sensitivity was significantly higher than CAD (p < 0.001). Conversely, the diagnostic performance of inexperienced group in terms of both sensitivity and specificity was significantly lower than CAD (p < 0.001). We obtained an excellent agreement in the evaluation of the lesions among the two expert radiologists (Kappa coefficient: 88.7%), and among the two non-expert radiologists (Kappa coefficient: 84.9%). CONCLUSION The US CAD system is a useful additional tool to improve the diagnostic performance of the inexperienced radiologists, eventually reducing the number of unnecessary biopsies. Moreover, it is a valid second opinion in case of experienced radiologists.
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Affiliation(s)
- Luca Nicosia
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy.
| | - Francesca Addante
- Department of Emergency and Organ Transplantation, Section of Anatomic Pathology, School of Medicine, University "Aldo Moro", 70124 Bari, Italy
| | - Anna Carla Bozzini
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy
| | - Antuono Latronico
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy
| | - Marta Montesano
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy
| | - Lorenza Meneghetti
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy
| | - Francesca Tettamanzi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy
| | - Samuele Frassoni
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milan, Italy
| | - Vincenzo Bagnardi
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milan, Italy
| | - Rossella De Santis
- Postgraduate School in Radiology, University of Milan, 20122 Milan, Italy
| | - Filippo Pesapane
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy
| | - Cristiana Iuliana Fodor
- Division of Radiation Oncology, IEO European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Mauro Giuseppe Mastropasqua
- Department of Emergency and Organ Transplantation, Section of Anatomic Pathology, School of Medicine, University "Aldo Moro", 70124 Bari, Italy
| | - Enrico Cassano
- Division of Breast imaging IEO; European institute of Oncology, IRCCS, Via Ripamonti 435, Milan Italy
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Zhang J, Cai L, Chen L, Pang X, Chen M, Yan D, Liu J, Luo L. Re-evaluation of high-risk breast mammography lesions by target ultrasound and ABUS of breast non-mass-like lesions. BMC Med Imaging 2021; 21:156. [PMID: 34702200 PMCID: PMC8549220 DOI: 10.1186/s12880-021-00665-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 09/06/2021] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE The purpose of this study was to reevaluate the high-risk breast non-mass-like lesions (NMLs) in mammography (MG) by target ultrasound (US) and Automated breast ultrasonography (ABUS), and to analyze the correlation between different imaging findings and the factors influencing the classification of lesions. METHODS A total of 161 patients with 166 breast lesions were recruited in this retrospectively study. All cases were diagnosed as BI-RADS 4 or 5 by MG and as NML on ultrasound. While all NMLs underwent mammography, target US and ABUS before breast surgery or biopsy in the consistent position of breast. The imaging and pathological features of all cases were collected. All lesions were classified according to the lexion of ACR BI-RADS®. RESULTS There were significant differences between benign and malignant breast NML in all the features of target US and ABUS. US, especially ABUS, was superior to MG in determining the malignant breast NML. There was a significant difference in the detection rate of calcification between MG and Target US (P < 0.001), and there was a significant difference in the detection rate of structural distortion between ABUS and MG (P < 0.001). CONCLUSIONS Target US, especially ABUS, can significantly improve the sensitivity, specificity and accuracy of the diagnosis of high-risk NMLs in MG. The features of Target US and ABUS such as blood supply, hyperechogenicity, ductal changes, peripheral changes and coronal features could be employed to predict benign and malignant lesions. The coronal features of ABUS were more sensitive than those of Target HHUS in showing structural abnormalities. Target US was less effective than MG in local micro-calcification.
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Affiliation(s)
- Jianxing Zhang
- Department of Medical Imaging Center, Jinan University First Affiliated Hospital, No. 613, Huangpu Road West, Tianhe District, Guangzhou, 510630, Guangdong Province, China.
- Department of Ultrasound, 2Nd Clinical Hospital of Guangzhou Chinese Traditional Medicine College: Guangdong Provincial Hospital of Traditional Chinese Medicine, No. 111, Dade Road, Yuexiu District, Guangzhou, 510120, Guangdong Province, China.
| | - Lishang Cai
- Department of Ultrasound, Guangzhou University of Traditional Chinese Medicine First Affiliated Hospital, No. 16, Jichang Road, Baiyun District, Guangzhou, 510403, Guangdong Province, China
| | - Ling Chen
- Department of Ultrasound, 2Nd Clinical Hospital of Guangzhou Chinese Traditional Medicine College: Guangdong Provincial Hospital of Traditional Chinese Medicine, No. 111, Dade Road, Yuexiu District, Guangzhou, 510120, Guangdong Province, China
| | - Xiyan Pang
- Department of Ultrasound, 2Nd Clinical Hospital of Guangzhou Chinese Traditional Medicine College: Guangdong Provincial Hospital of Traditional Chinese Medicine, No. 111, Dade Road, Yuexiu District, Guangzhou, 510120, Guangdong Province, China
| | - Miao Chen
- Department of Ultrasound, 2Nd Clinical Hospital of Guangzhou Chinese Traditional Medicine College: Guangdong Provincial Hospital of Traditional Chinese Medicine, No. 111, Dade Road, Yuexiu District, Guangzhou, 510120, Guangdong Province, China
| | - Dan Yan
- Department of Ultrasound, 2Nd Clinical Hospital of Guangzhou Chinese Traditional Medicine College: Guangdong Provincial Hospital of Traditional Chinese Medicine, No. 111, Dade Road, Yuexiu District, Guangzhou, 510120, Guangdong Province, China
| | - Jia Liu
- Department of Ultrasound, 2Nd Clinical Hospital of Guangzhou Chinese Traditional Medicine College: Guangdong Provincial Hospital of Traditional Chinese Medicine, No. 111, Dade Road, Yuexiu District, Guangzhou, 510120, Guangdong Province, China
| | - Liangping Luo
- Department of Medical Imaging Center, Jinan University First Affiliated Hospital, No. 613, Huangpu Road West, Tianhe District, Guangzhou, 510630, Guangdong Province, China.
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Coppola F, Faggioni L, Gabelloni M, De Vietro F, Mendola V, Cattabriga A, Cocozza MA, Vara G, Piccinino A, Lo Monaco S, Pastore LV, Mottola M, Malavasi S, Bevilacqua A, Neri E, Golfieri R. Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging. Front Psychol 2021; 12:710982. [PMID: 34650476 PMCID: PMC8505993 DOI: 10.3389/fpsyg.2021.710982] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/02/2021] [Indexed: 12/22/2022] Open
Abstract
Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.
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Affiliation(s)
- Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Fabrizio De Vietro
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Vincenzo Mendola
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Arrigo Cattabriga
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Maria Adriana Cocozza
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Giulio Vara
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Alberto Piccinino
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Silvia Lo Monaco
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Luigi Vincenzo Pastore
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Margherita Mottola
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Silvia Malavasi
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Alessandro Bevilacqua
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Emanuele Neri
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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Gatta G, Cappabianca S, La Forgia D, Massafra R, Fanizzi A, Cuccurullo V, Brunese L, Tagliafico A, Grassi R. Second-Generation 3D Automated Breast Ultrasonography (Prone ABUS) for Dense Breast Cancer Screening Integrated to Mammography: Effectiveness, Performance and Detection Rates. J Pers Med 2021; 11:jpm11090875. [PMID: 34575652 PMCID: PMC8468126 DOI: 10.3390/jpm11090875] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/24/2021] [Accepted: 08/29/2021] [Indexed: 12/22/2022] Open
Abstract
In our study, we added a three-dimensional automated breast ultrasound (3D ABUS) to mammography to evaluate the performance and cancer detection rate of mammography alone or with the addition of 3D prone ABUS in women with dense breasts. Our prospective observational study was based on the screening of 1165 asymptomatic women with dense breasts who selected independent of risk factors. The results evaluated include the cancers detected between June 2017 and February 2019, and all surveys were subjected to a double reading. Mammography detected four cancers, while mammography combined with a prone Sofia system (3D ABUS) doubled the detection rate, with eight instances of cancer being found. The diagnostic yield difference was 3.4 per 1000. Mammography alone was subjected to a recall rate of 14.5 for 1000 women, while mammography combined with 3D prone ABUS resulted in a recall rate of 26.6 per 1000 women. We also observed an additional 12.1 recalls per 1000 women screened. Integrating full-field digital mammography (FFDM) with 3D prone ABUS in women with high breast density increases and improves breast cancer detection rates in a significant manner, including small and invasive cancers, and it has a tolerable impact on recall rate. Moreover, 3D prone ABUS performance results are comparable with the performance results of the supine 3D ABUS system.
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Affiliation(s)
- Gianluca Gatta
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (S.C.); (V.C.); (R.G.)
| | - Salvatore Cappabianca
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (S.C.); (V.C.); (R.G.)
| | - Daniele La Forgia
- IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (R.M.); (A.F.)
- Correspondence: ; Tel.: +39-80-5555111
| | - Raffaella Massafra
- IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (R.M.); (A.F.)
| | - Annarita Fanizzi
- IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (R.M.); (A.F.)
| | - Vincenzo Cuccurullo
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (S.C.); (V.C.); (R.G.)
| | - Luca Brunese
- Dipartimento di Medicina e Scienze della Salute “Vincenzo Tiberio”—Università degli Studi del Molise, 86100 Campobasso, Italy;
| | | | - Roberto Grassi
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (S.C.); (V.C.); (R.G.)
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Pros and Cons for Automated Breast Ultrasound (ABUS): A Narrative Review. J Pers Med 2021; 11:jpm11080703. [PMID: 34442347 PMCID: PMC8400952 DOI: 10.3390/jpm11080703] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/21/2021] [Accepted: 07/22/2021] [Indexed: 12/15/2022] Open
Abstract
Automated breast ultrasound (ABUS) is an ultrasound technique that tends to be increasingly used as a supplementary technique in the evaluation of patients with dense glandular breasts. Patients with dense breasts have an increased risk of developing breast cancer compared to patients with fatty breasts. Furthermore, for this group of patients, mammography has a low sensitivity in detecting breast cancers, especially if it is not associated with architectural distortion or calcifications. ABUS is a standardized examination with many advantages in both screening and diagnostic settings: it increases the detection rate of breast cancer, improves the workflow, and reduces the examination time. On the other hand, like any imaging technique, ABUS has disadvantages and even some limitations. Many disadvantages can be diminished by additional attention and training. Disadvantages regarding image acquisition are the inability to assess the axilla, the vascularization, and the elasticity of a lesion, while concerning the interpretation, the disadvantages are the artifacts due to poor positioning, lack of contact, motion or lesion related. This article reviews and discusses the indications, the advantages, and disadvantages of the method and also the sources of error in the ABUS examination.
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Zhang P, Ma Z, Zhang Y, Chen X, Wang G. Improved Inception V3 method and its effect on radiologists' performance of tumor classification with automated breast ultrasound system. Gland Surg 2021; 10:2232-2245. [PMID: 34422594 PMCID: PMC8340346 DOI: 10.21037/gs-21-328] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/17/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND The automated breast ultrasound system (ABUS) is recognized as a valuable detection tool in addition to mammography. The purpose of this study was to propose a novel computer-aided diagnosis (CAD) system by extracting the textural features from ABUS images and to investigate the efficiency of using this CAD for breast cancer detection. METHODS This retrospective study involved 149 breast nodules [maximum diameter: mean size 18.89 mm, standard deviation (SD) 10.238, and range 5-59 mm] in 135. We assigned 3 novice readers (<3 years of experience and 3 experienced readers (≥10 years of experience to review the imaging data and stratify the 149 breast nodules as either malignant or benign. The Improved Inception V3 (II3) method was developed and used as an assistant tool to help the 6 readers to re-interpret the images. RESULTS Our method (II3) achieved an accuracy of 88.6% for the final result. The 3 novice readers had an average accuracy of 71.37%±4.067% while the 3 experienced readers was 83.03%±3.371% on the first-reading. With the help of II3 on the second-reading, the average accuracy of the novice readers increased to 84.13%±1.662% and the experienced readers increased to 89.50%±0.346%.The areas under the curve (AUCs) were similar compared with linear algorithms. The mean AUC of the novice readers was improved from 0.7751 (without II3) to 0.8232 (with II3). The mean AUC of the experienced readers was improved from 0.8939 (without II3) to 0.9211 (with II3). The mean AUC for all readers improved in both the second-reading mode (from 0.8345 to 0.8722, P=0.0081<0.05). CONCLUSIONS With the help of the II3, the diagnostic accuracy of the two groups were both improved, and II3 was more helpful for novice readers than for experienced readers. Our results showed that II3 is valuable in the differentiation of benign and malignant breast nodules and it also improves the experience and skill of some novice radiologists. The II3 cannot completely replace the influence of experience in the diagnostic process and will retain an auxiliary role in the clinic at present.
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Affiliation(s)
- Panpan Zhang
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Zhejiang University, Linhai, China
| | - Zhaosheng Ma
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Zhejiang University, Linhai, China
| | - Yingtao Zhang
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiaodan Chen
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Gang Wang
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Zhejiang University, Linhai, China
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A Review of Breast Imaging for Timely Diagnosis of Disease. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18115509. [PMID: 34063854 PMCID: PMC8196652 DOI: 10.3390/ijerph18115509] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/11/2021] [Accepted: 05/12/2021] [Indexed: 12/20/2022]
Abstract
Breast cancer (BC) is the cancer with the highest incidence in women in the world. In this last period, the COVID-19 pandemic has caused in many cases a drastic reduction of routine breast imaging activity due to the combination of various factors. The survival of BC is directly proportional to the earliness of diagnosis, and especially during this period, it is at least fundamental to remember that a diagnostic delay of even just three months could affect BC outcomes. In this article we will review the state of the art of breast imaging, starting from morphological imaging, i.e., mammography, tomosynthesis, ultrasound and magnetic resonance imaging and contrast-enhanced mammography, and their most recent evolutions; and ending with functional images, i.e., magnetic resonance imaging and contrast enhanced mammography.
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Philadelpho F, Calas MJG, Carneiro GDAC, Silveira IC, Vaz ABR, Nogueira AMC, Bergmann A, Lopes FPPL. Comparison of Automated Breast Ultrasound and Hand-Held Breast Ultrasound in the Screening of Dense Breasts. REVISTA BRASILEIRA DE GINECOLOGIA E OBSTETRÍCIA 2021; 43:190-199. [PMID: 33860502 PMCID: PMC10183872 DOI: 10.1055/s-0040-1722156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
Abstract
OBJECTIVE To compare hand-held breast ultrasound (HHBUS) and automated breast ultrasound (ABUS) as screening tool for cancer. METHODS A cross-sectional study in patients with mammographically dense breasts was conducted, and both HHBUS and ABUS were performed. Hand-held breast ultrasound was acquired by radiologists and ABUS by mammography technicians and analyzed by breast radiologists. We evaluated the Breast Imaging Reporting and Data System (BI-RADS) classification of the exam and of the lesion, as well as the amount of time required to perform and read each exam. The statistical analysis employed was measures of central tendency and dispersion, frequencies, Student t test, and a univariate logistic regression, through the odds ratio and its respective 95% confidence interval, and with p < 0.05 considered of statistical significance. RESULTS A total of 440 patients were evaluated. Regarding lesions, HHBUS detected 15 (7.7%) BI-RADS 2, 175 (89.3%) BI-RADS 3, and 6 (3%) BI-RADS 4, with 3 being confirmed by biopsy as invasive ductal carcinomas (IDCs), and 3 false-positives. Automated breast ultrasound identified 12 (12.9%) BI-RADS 2, 75 (80.7%) BI-RADS 3, and 6 (6.4%) BI-RADS 4, including 3 lesions detected by HHBUS and confirmed as IDCs, in addition to 1 invasive lobular carcinoma and 2 high-risk lesions not detected by HHBUS. The amount of time required for the radiologist to read the ABUS was statistically inferior compared with the time required to read the HHBUS (p < 0.001). The overall concordance was 80.9%. A total of 219 lesions were detected, from those 70 lesions by both methods, 126 only by HHBUS (84.9% not suspicious by ABUS) and 23 only by ABUS. CONCLUSION Compared with HHBUS, ABUS allowed adequate sonographic study in supplemental screening for breast cancer in heterogeneously dense and extremely dense breasts.
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Affiliation(s)
- Fernanda Philadelpho
- Radiology Department, Diagnósticos da América (DASA), Barra da Tijuca, RJ, Brazil
| | | | | | | | | | | | - Anke Bergmann
- Radiology Department, Diagnósticos da América (DASA), Barra da Tijuca, RJ, Brazil.,Clinical Epidemiology Program, Instituto Nacional de Cancer (INCA), Rio de Janeiro, RJ, Brazil
| | - Flávia Paiva Proença Lobo Lopes
- Radiology Department, Diagnósticos da América (DASA), Barra da Tijuca, RJ, Brazil.,Radiology Department, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
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De Cataldo C, Bruno F, Palumbo P, Di Sibio A, Arrigoni F, Clemente A, Bafile A, Gravina GL, Cappabianca S, Barile A, Splendiani A, Masciocchi C, Di Cesare E. Apparent diffusion coefficient magnetic resonance imaging (ADC-MRI) in the axillary breast cancer lymph node metastasis detection: a narrative review. Gland Surg 2021; 9:2225-2234. [PMID: 33447575 DOI: 10.21037/gs-20-546] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The presence of axillary lymph nodes metastases in breast cancer is the most significant prognostic factor, with a great impact on morbidity, disease-related survival and management of oncological therapies; for this reason, adequate imaging evaluation is strictly necessary. Physical examination is not enough sensitive to assess breast cancer nodal status; axillary ultrasonography (US) is commonly used to detect suspected or occult nodal metastasis, providing exclusively morphological evaluation, with low sensitivity and positive predictive value. Currently, sentinel lymph node biopsy (SLNB) and/or axillary dissection are the milestone for the diagnostic assessment of axillary lymph node metastases, although its related morbidity. The impact of magnetic resonance imaging (MRI) in the detection of nodal metastases has been widely investigated, as it continues to represent the most promising imaging modality in the breast cancer management. In particular, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values represent additional reliable non-contrast sequences, able to improve the diagnostic accuracy of breast cancer MRI evaluation. Several studies largely demonstrated the usefulness of implementing DWI/ADC MRI in the characterization of breast lesions. Herein, in the light of our clinical experience, we perform a review of the literature regarding the diagnostic performance and accuracy of ADC value as potential pre-operative tool to define metastatic involvement of nodal structures in breast cancer patients. For the purpose of this review, PubMed, Web of Science, and SCOPUS electronic databases were searched with different combinations of "axillary lymph node", "breast cancer", "MRI/ADC", "breast MRI" keywords. All original articles, reviews and metanalyses were included.
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Affiliation(s)
- Camilla De Cataldo
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Federico Bruno
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Pierpaolo Palumbo
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | | | - Francesco Arrigoni
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Alfredo Clemente
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | | | - Giovanni Luca Gravina
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Antonio Barile
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Alessandra Splendiani
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Carlo Masciocchi
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Ernesto Di Cesare
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
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Wanru JMD, Jingwen, ZMD, Yijie DMD, Ying ZMD, Xiaohong JMD, Weiwei ZMD, Jianqiao ZMD. Characterization of Breast Lesions: Comparison between Three-dimensional Ultrasound and Automated Volume Breast Ultrasound. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY 2021. [DOI: 10.37015/audt.2021.210007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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37
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Spear GG, Mendelson EB. Automated breast ultrasound: Supplemental screening for average-risk women with dense breasts. Clin Imaging 2020; 76:15-25. [PMID: 33548888 DOI: 10.1016/j.clinimag.2020.12.007] [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: 06/24/2020] [Revised: 11/24/2020] [Accepted: 12/17/2020] [Indexed: 11/25/2022]
Abstract
OBJECTIVE We review ultrasound (US) options for supplemental breast cancer screening of average risk women with dense breasts. CONCLUSION Performance data of physician-performed handheld US (HHUS), technologist-performed HHUS, and automated breast ultrasound (AUS) indicate that all are appropriate for adjunctive screening. Volumetric 3D acquisitions, reduced operator dependence, protocol standardization, reliable comparison with previous studies, independence of performance and interpretation, and whole breast depiction on coronal view may favor selection of AUS. Important considerations are workflow adjustments for physicians and staff.
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Affiliation(s)
- Georgia Giakoumis Spear
- NorthShore University HealthSystem, The University of Chicago Pritzker School of Medicine, United States of America.
| | - Ellen B Mendelson
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
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Zhou Y, Chen H, Li Y, Liu Q, Xu X, Wang S, Yap PT, Shen D. Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images. Med Image Anal 2020; 70:101918. [PMID: 33676100 DOI: 10.1016/j.media.2020.101918] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 11/22/2020] [Accepted: 11/23/2020] [Indexed: 12/12/2022]
Abstract
Tumor classification and segmentation are two important tasks for computer-aided diagnosis (CAD) using 3D automated breast ultrasound (ABUS) images. However, they are challenging due to the significant shape variation of breast tumors and the fuzzy nature of ultrasound images (e.g., low contrast and signal to noise ratio). Considering the correlation between tumor classification and segmentation, we argue that learning these two tasks jointly is able to improve the outcomes of both tasks. In this paper, we propose a novel multi-task learning framework for joint segmentation and classification of tumors in ABUS images. The proposed framework consists of two sub-networks: an encoder-decoder network for segmentation and a light-weight multi-scale network for classification. To account for the fuzzy boundaries of tumors in ABUS images, our framework uses an iterative training strategy to refine feature maps with the help of probability maps obtained from previous iterations. Experimental results based on a clinical dataset of 170 3D ABUS volumes collected from 107 patients indicate that the proposed multi-task framework improves tumor segmentation and classification over the single-task learning counterparts.
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Affiliation(s)
- Yue Zhou
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China; Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Houjin Chen
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
| | - Yanfeng Li
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Qin Liu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Xuanang Xu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Shu Wang
- Peking University People's Hospital, Beijing 100044, China
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, 27599, USA.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea.
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De Giorgis S, Brunetti N, Zawaideh J, Rossi F, Calabrese M, Tagliafico AS. Influence of Breast Density on Patient's Compliance during Ultrasound Examination: Conventional Handheld Breast Ultrasound Compared to Automated Breast Ultrasound. J Med Ultrasound 2020; 28:230-234. [PMID: 33659162 PMCID: PMC7869737 DOI: 10.4103/jmu.jmu_13_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 02/10/2020] [Accepted: 02/15/2020] [Indexed: 11/20/2022] Open
Abstract
Background: Our aim was to study the influence of breast density on patient's compliance during conventional handheld breast ultrasound (US) or automated breast US (ABUS), which could be used as adjunct screening modalities. Methods: Between January 2019 and June 2019, 221 patients (mean age: 53; age range: 24–89 years) underwent both US and ABUS. All participants had independently interpreted US and ABUS regarding patient compliance. The diagnostic experience with US or ABUS was described with a modified testing morbidity index (TMI). The scale ranged from 0 (worst possible experience) to 5 (acceptable experience). Standard statistics was used to compare the data of US and data of ABUS. Breast density was recorded with the Breast Imaging Reporting and Data System (BI-RADS) score. Results: The mean TMI score was 4.6 ± 0.5 for US and 4.3 ± 0.8 for ABUS. The overall difference between patients' experience on US and ABUS was statistically significant with P < 0.0001. The difference between patients' experience on US and ABUS in women with BI-RADS C and D for breast density was statistically significant with P < 0.02 in favor of US (4.7 ± 0.4) versus 4.5 ± 0.6 for ABUS. Patients' experience with breast density B was better for US (4.7 ± 0.4) versus 4.3 ± 0.6 for ABUS with P < 0.01. Pain or discomfort occurred during testing, especially in patients >40 years. Conclusion: Patient age (>40 years) is a significant predictor of decreased compliance to ABUS. Compliance of ABUS resulted lower that of US independently for breast density.
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Affiliation(s)
- Sara De Giorgis
- Department of Health Sciences, (DISSAL) - Radiology Section, University of Genova, Genova, Italy
| | - Nicole Brunetti
- Department of Health Sciences, (DISSAL) - Radiology Section, University of Genova, Genova, Italy
| | - Jeries Zawaideh
- Department of Health Sciences, (DISSAL) - Radiology Section, University of Genova, Genova, Italy
| | - Federica Rossi
- Department of Health Sciences, (DISSAL) - Radiology Section, University of Genova, Genova, Italy
| | | | - Alberto Stefano Tagliafico
- Department of Health Sciences, (DISSAL) - Radiology Section, University of Genova, Genova, Italy.,IRCCS-Ospedale Policlinico San Martino, Genova, Italy
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40
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Saharkhiz N, Ha R, Taback B, Li XJ, Weber R, Nabavizadeh A, Lee SA, Hibshoosh H, Gatti V, Kamimura HAS, Konofagou EE. Harmonic motion imaging of human breast masses: an in vivo clinical feasibility. Sci Rep 2020; 10:15254. [PMID: 32943648 PMCID: PMC7498461 DOI: 10.1038/s41598-020-71960-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 08/07/2020] [Indexed: 12/14/2022] Open
Abstract
Non-invasive diagnosis of breast cancer is still challenging due to the low specificity of the imaging modalities that calls for unnecessary biopsies. The diagnostic accuracy can be improved by assessing the breast tissue mechanical properties associated with pathological changes. Harmonic motion imaging (HMI) is an elasticity imaging technique that uses acoustic radiation force to evaluate the localized mechanical properties of the underlying tissue. Herein, we studied the in vivo feasibility of a clinical HMI system to differentiate breast tumors based on their relative HMI displacements, in human subjects. We performed HMI scans in 10 female subjects with breast masses: five benign and five malignant masses. Results revealed that both benign and malignant masses were stiffer than the surrounding tissues. However, malignant tumors underwent lower mean HMI displacement (1.1 ± 0.5 µm) compared to benign tumors (3.6 ± 1.5 µm) and the adjacent non-cancerous tissue (6.4 ± 2.5 µm), which allowed to differentiate between tumor types. Additionally, the excised breast specimens of the same patients (n = 5) were imaged post-surgically, where there was an excellent agreement between the in vivo and ex vivo findings, confirmed with histology. Higher displacement contrast between cancerous and non-cancerous tissue was found ex vivo, potentially due to the lower nonlinearity in the elastic properties of ex vivo tissue. This preliminary study lays the foundation for the potential complementary application of HMI in clinical practice in conjunction with the B-mode to classify suspicious breast masses.
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Affiliation(s)
- Niloufar Saharkhiz
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Richard Ha
- Department of Radiology, New-York-Presbyterian/Columbia University Medical Center, New York, NY, USA
| | - Bret Taback
- Department of Surgery, New-York-Presbyterian/Columbia University Medical Center, New York, NY, USA
| | - Xiaoyue Judy Li
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Rachel Weber
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Alireza Nabavizadeh
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Stephen A Lee
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Hanina Hibshoosh
- Department of Pathology and Cell Biology, New-York-Presbyterian/Columbia University Medical Center, New York, NY, USA
| | - Vittorio Gatti
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Hermes A S Kamimura
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Elisa E Konofagou
- Department of Biomedical Engineering, Columbia University, New York, NY, USA. .,Department of Radiology, New-York-Presbyterian/Columbia University Medical Center, New York, NY, USA.
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Wang F, Liu X, Yuan N, Qian B, Ruan L, Yin C, Jin C. Study on automatic detection and classification of breast nodule using deep convolutional neural network system. J Thorac Dis 2020; 12:4690-4701. [PMID: 33145042 PMCID: PMC7578508 DOI: 10.21037/jtd-19-3013] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Backgrounds Conventional ultrasound manual scanning and artificial diagnosis approaches in breast are considered to be operator-dependence, slight slow and error-prone. In this study, we used Automated Breast Ultrasound (ABUS) machine for the scanning, and deep convolutional neural network (CNN) technology, a kind of Deep Learning (DL) algorithm, for the detection and classification of breast nodules, aiming to achieve the automatic and accurate diagnosis of breast nodules. Methods Two hundred and ninety-three lesions from 194 patients with definite pathological diagnosis results (117 benign and 176 malignancy) were recruited as case group. Another 70 patients without breast diseases were enrolled as control group. All the breast scans were carried out by an ABUS machine and then randomly divided into training set, verification set and test set, with a proportion of 7:1:2. In the training set, we constructed a detection model by a three-dimensionally U-shaped convolutional neural network (3D U-Net) architecture for the purpose of segment the nodules from background breast images. Processes such as residual block, attention connections, and hard mining were used to optimize the model while strategies of random cropping, flipping and rotation for data augmentation. In the test phase, the current model was compared with those in previously reported studies. In the verification set, the detection effectiveness of detection model was evaluated. In the classification phase, multiple convolutional layers and fully-connected layers were applied to set up a classification model, aiming to identify whether the nodule was malignancy. Results Our detection model yielded a sensitivity of 91% and 1.92 false positive subjects per automatically scanned imaging. The classification model achieved a sensitivity of 87.0%, a specificity of 88.0% and an accuracy of 87.5%. Conclusions Deep CNN combined with ABUS maybe a promising tool for easy detection and accurate diagnosis of breast nodule.
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Affiliation(s)
- Feiqian Wang
- Department of Ultrasound, The First Affiliated Hospital of Xi'an Jiaotong University, China
| | - Xiaotong Liu
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, China.,School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Na Yuan
- Department of Ultrasound, The First Affiliated Hospital of Xi'an Jiaotong University, China
| | - Buyue Qian
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, China.,School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Litao Ruan
- Department of Ultrasound, The First Affiliated Hospital of Xi'an Jiaotong University, China
| | - Changchang Yin
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, China.,School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Ciping Jin
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, China.,School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
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Lin X, Jia M, Zhou X, Bao L, Chen Y, Liu P, Feng R, Zhang X, Zhu L, Wang H, Zhu Y, Tang G, Feng W, Li A, Qiao Y. The diagnostic performance of automated versus handheld breast ultrasound and mammography in symptomatic outpatient women: a multicenter, cross-sectional study in China. Eur Radiol 2020; 31:947-957. [PMID: 32852589 DOI: 10.1007/s00330-020-07197-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/04/2020] [Accepted: 08/14/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES The purpose of this study was to evaluate the diagnostic performance of automated breast ultrasound (ABUS) for breast cancer by comparing it to handheld ultrasound (HHUS) and mammography (MG). METHODS A multicenter cross-sectional study was conducted between February 2016 and March 2017 in five tertiary hospitals in China, and 1922 women aged 30-69 years old were recruited. Women aged 30-39 years (group A) underwent ABUS and HHUS, and women aged 40-69 (group B) underwent additional MG. Images were interpreted using the Breast Imaging Reporting and Data System (BI-RADS). All BI-RADS 4 and 5 cases were confirmed pathologically. Sensitivities and specificities of all modalities were compared. RESULTS There were 83 cancers in 677 women in group A and 321 cancers in 1245 women in group B. In the whole study population, the sensitivities of ABUS and HHUS were 92.8% (375/404) and 96.3% (389/404), and the specificities were 93.0% (1411/1518) and 89.6% (1360/1518), respectively. ABUS had a significantly higher specificity to HHUS (p < 0.01), while HHUS had higher sensitivity (p = 0.01). In group B, the sensitivities of ABUS, HHUS, and MG were 93.5% (300/321), 96.6% (310/321), and 87.9% (282/321). The specificities were 93.0% (859/924), 89.9% (831/924), and 91.6% (846/924). ABUS had significantly higher sensitivity (p = 0.02) and comparable specificity compared with MG (p = 0.14). CONCLUSION ABUS increased sensitivity and had similar specificity compared with mammography in the diagnosis of breast cancer. Additionally, ABUS has comparable performance to HHUS in women aged 30-69 years old. ABUS or HHUS is a suitable modality for breast cancer diagnosis. KEY POINTS • In breast cancer diagnosis settings, automated breast ultrasound has a higher cancer detection rate, sensitivity, and specificity than mammography, especially in women with dense breasts. • Compared with handheld ultrasound, automated breast ultrasound has higher specificity, lower sensitivity, and comparable diagnostic performance. • Automated breast ultrasound is a suitable modality for breast cancer diagnosis, and may have a potential indication for its further use in the breast cancer early detection.
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Affiliation(s)
- Xi Lin
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Mengmeng Jia
- Department of Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiang Zhou
- Department of Interventional Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lingyun Bao
- Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
| | - Yaqing Chen
- Department of Ultrasound, Xin Hua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Peifang Liu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Ruimei Feng
- Department of Cancer Prevention Research, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Xi Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Luoxi Zhu
- Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
| | - Hui Wang
- Department of Ultrasound, Xin Hua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Ying Zhu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Guoxue Tang
- Department of Ultrasound, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Wenqi Feng
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Anhua Li
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China.
| | - Youlin Qiao
- Department of Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China. .,School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
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The value of coronal view as a stand-alone assessment in women undergoing automated breast ultrasound. Radiol Med 2020; 126:206-213. [PMID: 32676876 DOI: 10.1007/s11547-020-01250-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 07/02/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Aim of the study was to evaluate the value of automated breast ultrasound (AUS) in women with dense breast, in terms of reading times, diagnostic performance and interobserver agreement. The assessment of coronal images alone versus the complete multiplanar (MPR) views was evaluated. METHODS Between August and October 2017, consecutive patients with dense breast that were referred to our Institute, for post-mammography ultrasound assessment, pre-operative assessment or follow-up of known benign lesions, were invited to undergo an additional study with AUS. Three radiologists, (5, 15 and 25 years of experience in breast imaging), reviewed the exams twice: first assessing reconstructed coronal images alone, second the complete MPR views. Reading times, diagnostic performance and interobserver agreement were assessed. RESULTS One hundred eighty-eight women were included, for a total of 67 breast lesions, 25 (37%) malignant and 42 (63%) benign. Compared to MPR, coronal view was associated with: lower reading times, respectively, for the three readers: 83 ± 37, 84 ± 43 and 76 ± 30 versus 163 ± 109, 131 ± 57, 151 ± 42 s (p < 0.035); lower sensitivity: 44.8%, 62.1%, 55.2% versus 69.0% (p = 0.059), 65.5% (p = 0.063), 72.4% (p = 0.076), respectively; better specificity: 94.1%, 93.7%, 94.2% versus 89.5% (p = 0.093), 87.4% (p = 0.002), 91.6% (p = 0.383), respectively. Agreement between the most and the least experienced reader was fair to moderate for categorical variables and significant for continuous ones. CONCLUSION The coronal view allows significantly lower reading times, a valuable feature in the screening setting, but its diagnostic performance makes the complete multiplanar assessment mandatory.
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Depretto C, Liguori A, Primolevo A, Di Cosimo S, Cartia F, Ferranti C, Scaperrotta GP. Automated breast ultrasound compared to hand-held ultrasound in surveillance after breast-conserving surgery. TUMORI JOURNAL 2020; 107:132-138. [PMID: 32552398 DOI: 10.1177/0300891620930278] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PURPOSE To investigate the agreement between automated breast ultrasound (ABUS) and hand-held ultrasound (HHUS) in surveillance of women with a history of breast cancer in terms of recurrences or new ipsilateral or contralateral breast cancer. METHODS The institutional review board approved this retrospective study and informed consent was waived. From April to June 2016, women with dense breasts undergoing annual surveillance with mammography and HHUS after breast-conserving surgery were offered supplemental ABUS (Invenia). HHUS was performed by a breast radiologist and ABUS by a trained technician. Images were reviewed by 2 breast radiologists. A per-patient BI-RADS category was independently assigned in all cases and categories were dichotomized into negative (1, 2, 3) and positive (4, 5). Cohen κ, McNemar, and Wilcoxon statistics were used. Final pathology was used as reference standard for malignant lesions. RESULTS A total of 154 women (mean age 62±11 years) were enrolled. Time from surgery was a mean of 8±6 years. Cancer prevalence was 4/154 (2.6%). Interreader agreement for ABUS was 1. Intermethod interreader agreement for HHUS and ABUS was substantial for BI-RADS categories (κ = 0.785) and for dichotomic assessment (κ = 0.794). There was no difference in dichotomic assignment between 2 readers (p = 0.5) but a significant difference in assigning BI-RADS categories (p < 0.05). CONCLUSIONS A substantial agreement resulted between HHUS and ABUS in surveillance of women with a previous history of breast cancer. In particular, ABUS recognized all cancers detected by HHUS and could play a role in first-level surveillance of women at intermediate risk.
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Affiliation(s)
| | | | | | - Serena Di Cosimo
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milano, Italy
| | - Francesco Cartia
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milano, Italy
| | - Claudio Ferranti
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milano, Italy
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Mussetto I, Gristina L, Schiaffino S, Tosto S, Raviola E, Calabrese M. Breast ultrasound: automated or hand-held? Exploring patients' experience and preference. Eur Radiol Exp 2020; 4:12. [PMID: 32040784 PMCID: PMC7010878 DOI: 10.1186/s41747-019-0136-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 11/20/2019] [Indexed: 11/29/2022] Open
Abstract
Background Our aim was to compare women’s experience with automated breast ultrasound (ABUS) versus breast hand-held ultrasound (HHUS) and to evaluate their acceptance rate. Methods After ethical approval, from October 2017 to March 2018, 79 consecutive patients were enrolled in this prospective study. On the same day, patients underwent HHUS followed by ABUS. Each patient’s experience was assessed using the modified testing morbidities index (TMI) (the lower the score, the better is the experience). Nine items were assessed for both techniques: seven directly related to the examination technique (pain or discomfort immediately before (preparation), during and after testing, fear or anxiety immediately before (preparation) and during testing, physical and mental function after testing) and two indirectly related to the examination technique (embarrassment during testing and overall satisfaction). Finally, we asked patients to choose between the two techniques for a potential next breast examination. Wilcoxon signed ranks test was used. Results The median TMI score for the seven items was found to be significantly better for HHUS (8, interquartile range [IQR] 7–11) compared to ABUS (9, IQR 8–12) (p = 0.003). The item ‘pain/discomfort during the test’ (p < 0.001) was significantly higher for ABUS compared to HHUS. Instead, the item ‘fear/anxiety before the test’ was higher for HHUS (p = 0.001). Overall, 40.5% of the patients chose HHUS, 29.1% chose ABUS, and 30.4% were unable to choose. Conclusions ABUS and HHUS exams were well tolerated and accepted. However, HHUS was perceived to be less painful than ABUS.
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Affiliation(s)
- Ilaria Mussetto
- School of Radiology, University of Genoa, Department of Health Sciences DISSAL, Via Antonio Pastore 1, 16132, Genoa, Italy.
| | - Licia Gristina
- Diagnostic Senology, IRCCS - Policlinico San Martino, Largo Rosanna Benzi 10, 16132, Genoa, Italy
| | - Simone Schiaffino
- Radiology Unit, IRCCS Policlinico San Donato, Via Morandi 30, San Donato Milanese, 20097, Milan, Italy
| | - Simona Tosto
- Diagnostic Senology, IRCCS - Policlinico San Martino, Largo Rosanna Benzi 10, 16132, Genoa, Italy
| | - Edoardo Raviola
- Università Vita-Salute, San Raffaele, Via Olgettina 58, 20132, Milan, Italy
| | - Massimo Calabrese
- Diagnostic Senology, IRCCS - Policlinico San Martino, Largo Rosanna Benzi 10, 16132, Genoa, Italy
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Amato F, Bicchierai G, Cirone D, Depretto C, Di Naro F, Vanzi E, Scaperrotta G, Bartolotta TV, Miele V, Nori J. Preoperative loco-regional staging of invasive lobular carcinoma with contrast-enhanced digital mammography (CEDM). Radiol Med 2019; 124:1229-1237. [PMID: 31773458 DOI: 10.1007/s11547-019-01116-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 11/15/2019] [Indexed: 12/15/2022]
Abstract
The aim of our study was to assess the performance of contrast-enhanced digital mammography (CEDM) in the preoperative loco-regional staging of invasive lobular carcinoma (ILC) patients, about the valuation of the extension of disease and in measurement of lesions. Then, we selected retrospectively, among the 1500 patients underwent to CEDM at the Breast Diagnostics Department of the Careggi University Hospital of Florence and the National Cancer Institute of Milan from September 2016 to November 2018, 31 women (mean age 57.1 aa; range 41-78 aa) with a definitive histological diagnosis of ILC. CEDM has proved to be a promising imaging technique, being characterized by a sensitivity of 100% in the detection of the index lesion, and of 84.2% in identifying any adjunctive lesions: It was the presence of a non-mass enhancement (NME) to lower the sensitivity of the technique (25% vs. 100% for mass-like enhancements or a mass closely associated with a NME). Specificity in the characterization of additional lesions was 66.7%, and the diagnosis of the extension of disease was correct in 77.4% of cases: NME also led to a decrease in diagnostic accuracy in the evaluation of disease extension up to 40% versus 85% for masses and 80% for masses associated with NME (M/NME). Moreover, in 12/31 (38.7%), CEDM allowed to correctly identify lesions not shown by mammography + ultrasonography + tomosynthesis: In the half of these (6/12), there was a multicentricity, thus allowing an adequate surgical planning change. CEDM was also very accurate in analyzing the maximum diameter of the masses, while it was much less reliable in the case of the M/NME and pure NME. In conclusion, CEDM is a new promising imaging technique in the loco-regional preoperative staging and in the evaluation of disease extension for ILC, especially in case of mass enhancement lesions.
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Affiliation(s)
- Francesco Amato
- Department of Radiology, University of Palermo, Palermo, Italy
| | - Giulia Bicchierai
- Diagnostic Senology Unit, Azienda Ospedaliero-Universitaria Careggi, Largo G. A. Brambilla 3, 50134, Florence, Italy.
| | - Donatello Cirone
- General Management Staff, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Catherine Depretto
- Breast Imaging Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Federica Di Naro
- Diagnostic Senology Unit, Azienda Ospedaliero-Universitaria Careggi, Largo G. A. Brambilla 3, 50134, Florence, Italy
| | - Ermanno Vanzi
- Diagnostic Senology Unit, Azienda Ospedaliero-Universitaria Careggi, Largo G. A. Brambilla 3, 50134, Florence, Italy
| | | | | | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Jacopo Nori
- Diagnostic Senology Unit, Azienda Ospedaliero-Universitaria Careggi, Largo G. A. Brambilla 3, 50134, Florence, Italy
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Mostafa AAE, Eltomey MA, Elaggan AM, Hashish AA. Automated breast ultrasound (ABUS) as a screening tool: initial experience. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2019. [DOI: 10.1186/s43055-019-0032-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Breast cancer is a major health problem, being the most common cancer in women. Early detection of breast cancer aims to the reduction of mortality and morbidity rates. Conventional screening methods include mammography and ultrasonography; however, both modalities have their limitations. Automated breast ultrasound (ABUS) is a recent technological advancement in the field of breast imaging having the benefit of standardization of the scans and lack of operator dependence as in conventional handheld ultrasound scans. The aim of this work was to report our initial experience of the added value of ABUS as a breast screening tool. The study included 200 patients who had screening mammograms, ultrasound, and ABUS.
Results
A significant difference was found between the number of lesions detected by ABUS and conventional ultrasound. A significant difference was found between lesions detected by ABUS and mammography which was most evident in patients with dense breasts.
Conclusions
ABUS is a valuable tool in the screening of the breast with improved lesion detection, especially in patients with dense breasts.
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Le EPV, Wang Y, Huang Y, Hickman S, Gilbert FJ. Artificial intelligence in breast imaging. Clin Radiol 2019; 74:357-366. [PMID: 30898381 DOI: 10.1016/j.crad.2019.02.006] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 02/22/2019] [Indexed: 12/15/2022]
Abstract
This article reviews current limitations and future opportunities for the application of computer-aided detection (CAD) systems and artificial intelligence in breast imaging. Traditional CAD systems in mammography screening have followed a rules-based approach, incorporating domain knowledge into hand-crafted features before using classical machine learning techniques as a classifier. The first commercial CAD system, ImageChecker M1000, relies on computer vision techniques for pattern recognition. Unfortunately, CAD systems have been shown to adversely affect some radiologists' performance and increase recall rates. The Digital Mammography DREAM Challenge was a multidisciplinary collaboration that provided 640,000 mammography images for teams to help decrease false-positive rates in breast cancer screening. Winning solutions leveraged deep learning's (DL) automatic hierarchical feature learning capabilities and used convolutional neural networks. Start-ups Therapixel and Kheiron Medical Technologies are using DL for breast cancer screening. With increasing use of digital breast tomosynthesis, specific artificial intelligence (AI)-CAD systems are emerging to include iCAD's PowerLook Tomo Detection and ScreenPoint Medical's Transpara. Other AI-CAD systems are focusing on breast diagnostic techniques such as ultrasound and magnetic resonance imaging (MRI). There is a gap in the market for contrast-enhanced spectral mammography AI-CAD tools. Clinical implementation of AI-CAD tools requires testing in scenarios mimicking real life to prove its usefulness in the clinical environment. This requires a large and representative dataset for testing and assessment of the reader's interaction with the tools. A cost-effectiveness assessment should be undertaken, with a large feasibility study carried out to ensure there are no unintended consequences. AI-CAD systems should incorporate explainable AI in accordance with the European Union General Data Protection Regulation (GDPR).
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Affiliation(s)
- E P V Le
- University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK; EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, University of Cambridge, Cambridge CB3 0WA, UK
| | - Y Wang
- EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, University of Cambridge, Cambridge CB3 0WA, UK
| | - Y Huang
- EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, University of Cambridge, Cambridge CB3 0WA, UK; Department of Radiology, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK
| | - S Hickman
- Department of Radiology, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK
| | - F J Gilbert
- EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, University of Cambridge, Cambridge CB3 0WA, UK; Department of Radiology, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK.
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Girometti R, Tomkova L, Cereser L, Zuiani C. Breast cancer staging: Combined digital breast tomosynthesis and automated breast ultrasound versus magnetic resonance imaging. Eur J Radiol 2018; 107:188-195. [DOI: 10.1016/j.ejrad.2018.09.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 07/05/2018] [Accepted: 09/03/2018] [Indexed: 10/28/2022]
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50
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Speckle Reduction on Ultrasound Liver Images Based on a Sparse Representation over a Learned Dictionary. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8060903] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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