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Aribal E, Seker ME, Guldogan N, Yilmaz E. Value of automated breast ultrasound in screening: Standalone and as a supplemental to digital breast tomosynthesis. Int J Cancer 2024; 155:1466-1475. [PMID: 38989802 DOI: 10.1002/ijc.35093] [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: 02/19/2024] [Revised: 06/22/2024] [Accepted: 06/27/2024] [Indexed: 07/12/2024]
Abstract
We aimed to determine the value of standalone and supplemental automated breast ultrasound (ABUS) in detecting cancers in an opportunistic screening setting with digital breast tomosynthesis (DBT) and compare this combined screening method to DBT and ABUS alone in women older than 39 years with BI-RADS B-D density categories. In this prospective opportunistic screening study, 3466 women aged 39 or older with BI-RADS B-D density categories and with a mean age of 50 were included. The screening protocol consisted of DBT mediolateral-oblique views, 2D craniocaudal views, and ABUS with three projections for both breasts. ABUS was evaluated blinded to mammography findings. Statistical analysis evaluated diagnostic performance for DBT, ABUS, and combined workflows. Twenty-nine cancers were screen-detected. ABUS and DBT exhibited the same cancer detection rates (CDR) at 7.5/1000 whereas DBT + ABUS showed 8.4/1000, with ABUS contributing an additional CDR of 0.9/1000. Standalone ABUS outperformed DBT in detecting 12.5% more invasive cancers. DBT displayed better accuracy (95%) compared to ABUS (88%) and combined approach (86%). Sensitivities for DBT and ABUS were the same (84%), with DBT + ABUS showing a higher rate (94%). DBT outperformed ABUS in specificity (95% vs. 88%). DBT + ABUS exhibited a higher recall rate (14.89%) compared to ABUS (12.38%) and DBT (6.03%) (p < .001). Standalone ABUS detected more invasive cancers compared to DBT, with a higher recall rate. The combined approach showed a higher CDR by detecting one additional cancer per thousand.
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Affiliation(s)
- Erkin Aribal
- School of Medicine, Department of Radiology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
- Department of Radiology, Acibadem Altunizade Hospital, Istanbul, Turkey
| | - Mustafa Ege Seker
- School of Medicine, Department of Radiology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Nilgün Guldogan
- Department of Radiology, Acibadem Altunizade Hospital, Istanbul, Turkey
| | - Ebru Yilmaz
- Department of Radiology, Acibadem Altunizade Hospital, Istanbul, Turkey
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Eun NL, Lee E, Park AY, Son EJ, Kim JA, Youk JH. Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2024; 45:412-417. [PMID: 38593859 DOI: 10.1055/a-2230-2455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
PURPOSE To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis. MATERIALS AND METHODS We retrospectively collected a dataset consisting of 516 breast lesions (364 benign and 152 malignant) in 471 women who underwent B-mode US and MFI. The internal dataset was split into training (n = 410) and test datasets (n = 106) for developing AI algorithms from deep convolutional neural networks from MFI. AI algorithms were trained to provide malignancy risk (0-100%). The developed AI algorithms were further validated with an independent external dataset of 264 lesions (229 benign and 35 malignant). The diagnostic performance of B-mode US, AI algorithms, or their combinations was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). RESULTS The AUROC of the developed three AI algorithms (0.955-0.966) was higher than that of B-mode US (0.842, P < 0.0001). The AUROC of the AI algorithms on the external validation dataset (0.892-0.920) was similar to that of the test dataset. Among the AI algorithms, no significant difference was found in all performance metrics combined with or without B-mode US. Combined B-mode US and AI algorithms had a higher AUROC (0.963-0.972) than that of B-mode US (P < 0.0001). Combining B-mode US and AI algorithms significantly decreased the false-positive rate of BI-RADS category 4A lesions from 87% to 13% (P < 0.0001). CONCLUSION AI-based MFI diagnosed breast cancers with better performance than B-mode US, eliminating 74% of false-positive diagnoses in BI-RADS category 4A lesions.
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Affiliation(s)
- Na Lae Eun
- Radiology, Gangnam Severance Hospital, Seoul, Korea (the Republic of)
| | - Eunjung Lee
- Computational Science and Engineering, Yonsei University, Seoul, Korea (the Republic of)
| | - Ah Young Park
- Radiology, Bundang CHA Medical Center, Seongnam, Korea (the Republic of)
| | - Eun Ju Son
- Radiology, Gangnam Severance Hospital, Seoul, Korea (the Republic of)
| | - Jeong-Ah Kim
- Radiology, Gangnam Severance Hospital, Seoul, Korea (the Republic of)
| | - Ji Hyun Youk
- Department of Radiology, Yonsei University College of Medicine, Seoul, Korea, Republic of
- Radiology, Gangnam Severance Hospital, Seoul, Korea (the Republic of)
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Badu-Peprah A, Otoo OK, Amamoo M, Quarshie F, Adomako B. Breast imaging reporting and data system for sonography: Positive and negative predictive values of sonographic features in Kumasi, Ghana. Transl Oncol 2024; 45:101976. [PMID: 38697004 PMCID: PMC11070917 DOI: 10.1016/j.tranon.2024.101976] [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: 12/22/2023] [Revised: 04/15/2024] [Accepted: 04/27/2024] [Indexed: 05/04/2024] Open
Abstract
BACKGROUND Breast cancer is the most common female cancer globally. The method of choice for screening and diagnosing breast cancer is mammography, which is not widely available in Ghana as compared to ultrasonography. This study aimed to evaluate the sonographic features of solid breast lesions using the new sonographic Breast Imaging- Reporting and Data System (BI-RADS-US) lexicon for malignancy with histopathology as the gold standard. METHODS This was a prospective quantitative study that sonographically scanned female patients with breast masses and consecutively selected cases recommended for core biopsy from May 2018 to May 2021. Sixty (60) solid breast masses were described using the sonographic BI-RADS lexicon features. Lesion description and biopsy results from histopathology were compared and analyzed using Pearson's Chi-square test. Odds ratios, sensitivity, specificity, and predictive values were also calculated. Statistical significance level was set at p ≤ 0.05. RESULTS Irregular shape (p < 0.0001), spiculated mass margins (p < 0.0001), and not parallel mass orientation (p= 0.0007) were more commonly associated with malignant masses. The sensitivity of breast ultrasound for malignancy was 93.9 % and the specificity was 55.6 % with an overall accuracy rate of 76.6 %. The negative predictive value was 88.7 % and the positive predictive value was 72.1 %. Descriptors like irregular shape, non-parallel orientation, angular and spiculated margins, echogenic halo, and markedly hypoechoic internal content, demonstrated higher odds ratios for malignancy. CONCLUSIONS This study adds valuable insights to the diagnosis of breast cancer using the sonographic BI-RADS lexicon features. The results demonstrate that specific sonographic descriptors can effectively differentiate between benign and malignant breast masses.
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Affiliation(s)
- Augustina Badu-Peprah
- Radiology Directorate, Komfo Anokye Teaching Hospital, Kumasi, Ghana; Radiology Department, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
| | - Obed Kojo Otoo
- Radiology Directorate, Komfo Anokye Teaching Hospital, Kumasi, Ghana
| | - Mansa Amamoo
- Radiology Directorate, Komfo Anokye Teaching Hospital, Kumasi, Ghana
| | - Frank Quarshie
- Research Directorate, Klintaps College of Health and Allied Sciences, Klagon-Tema,Ghana
| | - Benjamin Adomako
- Research and Development Unit, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
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Yan L, Liang Z, Zhang H, Zhang G, Zheng W, Han C, Yu D, Zhang H, Xie X, Liu C, Zhang W, Zheng H, Pei J, Shen D, Qian X. A domain knowledge-based interpretable deep learning system for improving clinical breast ultrasound diagnosis. COMMUNICATIONS MEDICINE 2024; 4:90. [PMID: 38760506 PMCID: PMC11101659 DOI: 10.1038/s43856-024-00518-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 05/03/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Though deep learning has consistently demonstrated advantages in the automatic interpretation of breast ultrasound images, its black-box nature hinders potential interactions with radiologists, posing obstacles for clinical deployment. METHODS We proposed a domain knowledge-based interpretable deep learning system for improving breast cancer risk prediction via paired multimodal ultrasound images. The deep learning system was developed on 4320 multimodal breast ultrasound images of 1440 biopsy-confirmed lesions from 1348 prospectively enrolled patients across two hospitals between August 2019 and December 2022. The lesions were allocated to 70% training cohort, 10% validation cohort, and 20% test cohort based on case recruitment date. RESULTS Here, we show that the interpretable deep learning system can predict breast cancer risk as accurately as experienced radiologists, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval = 0.882 - 0.921), sensitivity of 75.2%, and specificity of 91.8% on the test cohort. With the aid of the deep learning system, particularly its inherent explainable features, junior radiologists tend to achieve better clinical outcomes, while senior radiologists experience increased confidence levels. Multimodal ultrasound images augmented with domain knowledge-based reasoning cues enable an effective human-machine collaboration at a high level of prediction performance. CONCLUSIONS Such a clinically applicable deep learning system may be incorporated into future breast cancer screening and support assisted or second-read workflows.
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Affiliation(s)
- Lin Yan
- School of Mathematics, Xi'an University of Finance and Economics, Xi'an, China
| | - Zhiying Liang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Hao Zhang
- Department of Neurosurgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Gaosong Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Weiwei Zheng
- Department of Ultrasound, Xuancheng People's Hospital, Xuancheng, China
| | - Chunguang Han
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Dongsheng Yu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Hanqi Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xinxin Xie
- Department of Ultrasound, Peking University Third Hospital, Beijing, China
| | - Chang Liu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wenxin Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Hui Zheng
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jing Pei
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
- State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
- Shanghai Clinical Research and Trial Center, Shanghai, China.
| | - Xuejun Qian
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
- State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
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Brasier-Lutz P, Jäggi-Wickes C, Schädelin S, Burian R, Schoenenberger CA, Zanetti-Dällenbach R. Patient perception of meander-like versus radial breast ultrasound. Ultrasound Int Open 2024; 10:a22829193. [PMID: 38737925 PMCID: PMC11086955 DOI: 10.1055/a-2282-9193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 03/06/2024] [Indexed: 05/14/2024] Open
Abstract
Background Radial breast ultrasound scanning (r-US) and commonly used meander-like ultrasound scanning (m-US) have recently been shown to be equally sensitive and specific with regard to the detection of breast malignancies. As patient satisfaction has a strong influence on patient compliance and thus on the quality of health care, we compare here the two US scanning techniques with regard to patient comfort during breast ultrasound (BUS) and analyze whether the patient has a preference for either scanning technique. Materials and Methods Symptomatic and asymptomatic women underwent both m-US and r-US scanning by two different examiners. Patient comfort and preference were assessed using a visual analog scale-based (VAS) questionnaire and were compared using a Mann-Whitney U test. Results Analysis of 422 VAS-based questionnaires showed that perceived comfort with r-US (r-VAS 8 cm, IQR [5.3, 9.1]) was significantly higher compared to m-US (m-VAS 5.6 cm, IQR [5.2, 7.4]) (p < 0.001). 53.8% of patients had no preference, 44.3% of patients clearly preferred r-US, whereas only 1.9% of patients preferred m-US. Conclusion: Patients experience a higher level of comfort with r-US and favor r-US over m-US. As the diagnostic accuracy of r-US has been shown to be comparable to that of m-US and the time required for examination is shorter, a switch from m-US to r-US in routine clinical practice might be beneficial. R-US offers considerable potential to positively affect patient compliance but also to save examination time and thus costs.
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Affiliation(s)
| | | | - Sabine Schädelin
- Department of Clinical Research, Statistics and Data Management,
University of Basel, Basel, Switzerland
| | - Rosemarie Burian
- Gynecology and Obstetrics, University Hospital Basel,
Basel, Switzerland
| | - Cora-Ann Schoenenberger
- Department of Chemistry, University of Basel, 4056 Basel,
Switzerland
- Gynecology/Gynecologic Oncology, Sankt Claraspital AG,
Basel, Switzerland
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Kwon H, Oh S, Kim MG, Kim Y, Jung G, Lee HJ, Kim SY, Bae HM. Artificial Intelligence-Enhanced Quantitative Ultrasound for Breast Cancer: Pilot Study on Quantitative Parameters and Biopsy Outcomes. Diagnostics (Basel) 2024; 14:419. [PMID: 38396457 PMCID: PMC10888332 DOI: 10.3390/diagnostics14040419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 02/03/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Traditional B-mode ultrasound has difficulties distinguishing benign from malignant breast lesions. It appears that Quantitative Ultrasound (QUS) may offer advantages. We examined the QUS imaging system's potential, utilizing parameters like Attenuation Coefficient (AC), Speed of Sound (SoS), Effective Scatterer Diameter (ESD), and Effective Scatterer Concentration (ESC) to enhance diagnostic accuracy. B-mode images and radiofrequency signals were gathered from breast lesions. These parameters were processed and analyzed by a QUS system trained on a simulated acoustic dataset and equipped with an encoder-decoder structure. Fifty-seven patients were enrolled over six months. Biopsies served as the diagnostic ground truth. AC, SoS, and ESD showed significant differences between benign and malignant lesions (p < 0.05), but ESC did not. A logistic regression model was developed, demonstrating an area under the receiver operating characteristic curve of 0.90 (95% CI: 0.78, 0.96) for distinguishing between benign and malignant lesions. In conclusion, the QUS system shows promise in enhancing diagnostic accuracy by leveraging AC, SoS, and ESD. Further studies are needed to validate these findings and optimize the system for clinical use.
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Affiliation(s)
- Hyuksool Kwon
- Laboratory of Quantitative Ultrasound Imaging, Seoul National University Bundang Hospital, Seong-nam 13620, Republic of Korea; (H.K.); (S.O.)
- Imaging Division, Department of Emergency Medicine, Seoul National University Bundang Hospital, Seong-nam 13620, Republic of Korea
| | - Seokhwan Oh
- Laboratory of Quantitative Ultrasound Imaging, Seoul National University Bundang Hospital, Seong-nam 13620, Republic of Korea; (H.K.); (S.O.)
- Electrical Engineering Department, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; (M.-G.K.); (Y.K.); (G.J.); (H.-J.L.); (S.-Y.K.)
| | - Myeong-Gee Kim
- Electrical Engineering Department, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; (M.-G.K.); (Y.K.); (G.J.); (H.-J.L.); (S.-Y.K.)
| | - Youngmin Kim
- Electrical Engineering Department, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; (M.-G.K.); (Y.K.); (G.J.); (H.-J.L.); (S.-Y.K.)
| | - Guil Jung
- Electrical Engineering Department, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; (M.-G.K.); (Y.K.); (G.J.); (H.-J.L.); (S.-Y.K.)
| | - Hyeon-Jik Lee
- Electrical Engineering Department, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; (M.-G.K.); (Y.K.); (G.J.); (H.-J.L.); (S.-Y.K.)
| | - Sang-Yun Kim
- Electrical Engineering Department, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; (M.-G.K.); (Y.K.); (G.J.); (H.-J.L.); (S.-Y.K.)
| | - Hyeon-Min Bae
- Electrical Engineering Department, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; (M.-G.K.); (Y.K.); (G.J.); (H.-J.L.); (S.-Y.K.)
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Han HJ, Huang CS, Lu TP, Tseng LM, Chie WC, Huang CC. Evaluating clinical efficacy of hospital-based surveillance with mammography and ultrasonography for breast cancer. J Formos Med Assoc 2024; 123:78-87. [PMID: 37400295 DOI: 10.1016/j.jfma.2023.06.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 12/01/2022] [Accepted: 06/07/2023] [Indexed: 07/05/2023] Open
Abstract
Periodic mammography and/or sonography examinations are conducted across numerous hospitals nationalwidely, especially for antedees with a positive mammography screening. Despite the regular practice, clinical efficacy of hospital-based breast cancer surveillance remains unclear. Specifically, the impact of surveillance interval upon survival and prognostic surrogates stratified by menopausal status, as well as malignant transition rate should be deciphered. We retrieved cancer registry to ascertain 841 breast cancers with surveillance history through administration data. Healthy controls underwent breast surveillance and were concurrently free of cancer. More benign diseases rather than cancers were identified from premenopausal women (age ≤50 years) with sonography alone within one year, as well as older women (age >50) with both mammography and sonography one to two years before a cancer or benign diagnosis. Among breast cancers, mammography alone during the antecedent one to two years had a protective effect for diagnosing carcinoma in situ rather than invasive cancer (age-adjusted odds ratio: 0.048, P = 0.016). Three-state time homogeneous Markov model showed that hospital-based breast surveillance within 2 years of disease onset reduced the malignant transition rate by 65.16% (59.79-76.74%). The clinical efficacy of breast cancer surveillance was evidenced.
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Affiliation(s)
- Hsin-Ju Han
- Department of Surgery, Taipei Veterans General Hospital, Taipei City, Taiwan
| | - Ching-Shui Huang
- Department of Surgery, Cathay General Hospital, Taipei City, Taiwan; School of Medicine, Taipei Medical University, Taipei City, Taiwan
| | - Tzu-Pin Lu
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei City, Taiwan
| | - Ling-Ming Tseng
- Department of Surgery, Taipei Veterans General Hospital, Taipei City, Taiwan; Comprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei City, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan.
| | - Wei-Chu Chie
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei City, Taiwan.
| | - Chi-Cheng Huang
- Department of Surgery, Taipei Veterans General Hospital, Taipei City, Taiwan; Comprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei City, Taiwan; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei City, Taiwan.
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Lobig F, Caleyachetty A, Forrester L, Morris E, Newstead G, Harris J, Blankenburg M. Performance of Supplemental Imaging Modalities for Breast Cancer in Women With Dense Breasts: Findings From an Umbrella Review and Primary Studies Analysis. Clin Breast Cancer 2023:S1526-8209(23)00088-5. [PMID: 37202338 DOI: 10.1016/j.clbc.2023.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/28/2023] [Accepted: 04/14/2023] [Indexed: 05/20/2023]
Abstract
Breast cancer screening performance of supplemental imaging modalities by breast density and breast cancer risk has not been widely studied, and the optimal choice of modality for women with dense breasts remains unclear in clinical practice and guidelines. This systematic review aimed to assess breast cancer screening performance of supplemental imaging modalities for women with dense breasts, by breast cancer risk. Systematic reviews (SRs) in 2000 to 2021, and primary studies in 2019 to 2021, on outcomes of supplemental screening modalities (digital breast tomography [DBT], MRI (full/abbreviated protocol), contrast enhanced mammography (CEM), ultrasound (hand-held [HHUS]/automated [ABUS]) in women with dense breasts (BI-RADS C&D) were identified. None of the SRs analyzed outcomes by cancer risk. Meta-analysis of the primary studies was not feasible due to lack of studies (MRI, CEM, DBT) or methodological heterogeneity (ultrasound); therefore, findings were summarized narratively. For average risk, a single MRI trial reported a superior screening performance (higher cancer detection rate [CDR] and lower interval cancer rate [ICR]) compared to HHUS, ABUS and DBT. For intermediate risk, ultrasound was the only modality assessed, but accuracy estimates ranged widely. For mixed risk, a single CEM study reported the highest CDR, but included a high proportion of women with intermediate risk. This systematic review does not allow a complete comparison of supplemental screening modalities for dense breast populations by breast cancer risk. However, the data suggest that MRI and CEM might generally offer superior screening performance versus other modalities. Further studies of screening modalities are urgently required.
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Affiliation(s)
| | | | | | - Elizabeth Morris
- University of California Davis, Department of Radiology, Sacramento, CA 95817, USA
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Hussein H, Abbas E, Keshavarzi S, Fazelzad R, Bukhanov K, Kulkarni S, Au F, Ghai S, Alabousi A, Freitas V. Supplemental Breast Cancer Screening in Women with Dense Breasts and Negative Mammography: A Systematic Review and Meta-Analysis. Radiology 2023; 306:e221785. [PMID: 36719288 DOI: 10.1148/radiol.221785] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Background The best supplemental breast cancer screening modality in women at average risk or intermediate risk for breast cancer with dense breast and negative mammogram remains to be determined. Purpose To conduct systematic review and meta-analysis comparing clinical outcomes of the most common available supplemental screening modalities in women at average risk or intermediate risk for breast cancer in patients with dense breasts and mammography with negative findings. Materials and Methods A comprehensive search was conducted until March 12, 2020, in Medline, Epub Ahead of Print and In-Process and Other Non-Indexed Citations; Embase Classic and Embase; Cochrane Central Register of Controlled Trials; and Cochrane Database of Systematic Reviews, for Randomized Controlled Trials and Prospective Observational Studies. Incremental cancer detection rate (CDR); positive predictive value of recall (PPV1); positive predictive value of biopsies performed (PPV3); and interval CDRs of supplemental imaging modalities, digital breast tomosynthesis, handheld US, automated breast US, and MRI in non-high-risk patients with dense breasts and mammography negative for cancer were reviewed. Data metrics and risk of bias were assessed. Random-effects meta-analysis and two-sided metaregression analyses comparing each imaging modality metrics were performed (PROSPERO; CRD42018080402). Results Twenty-two studies reporting 261 233 screened patients were included. Of 132 166 screened patients with dense breast and mammography negative for cancer who met inclusion criteria, a total of 541 cancers missed at mammography were detected with these supplemental modalities. Metaregression models showed that MRI was superior to other supplemental modalities in CDR (incremental CDR, 1.52 per 1000 screenings; 95% CI: 0.74, 2.33; P < .001), including invasive CDR (invasive CDR, 1.31 per 1000 screenings; 95% CI: 0.57, 2.06; P < .001), and in situ disease (rate of ductal carcinoma in situ, 1.91 per 1000 screenings; 95% CI: 0.10, 3.72; P < .04). No differences in PPV1 and PPV3 were identified. The limited number of studies prevented assessment of interval cancer metrics. Excluding MRI, no statistically significant difference in any metrics were identified among the remaining imaging modalities. Conclusion The pooled data showed that MRI was the best supplemental imaging modality in women at average risk or intermediate risk for breast cancer with dense breasts and mammography negative for cancer. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Hooley and Butler in this issue.
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Affiliation(s)
- Heba Hussein
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Engy Abbas
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Sareh Keshavarzi
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Rouhi Fazelzad
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Karina Bukhanov
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Supriya Kulkarni
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Frederick Au
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Sandeep Ghai
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Abdullah Alabousi
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
| | - Vivianne Freitas
- From the Joint Department of Medical Imaging-Breast Division, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9 (H.H., E.A., K.B., S. Kulkarni, F.A., S.G., V.F.); Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester, United Kingdom (H.H.); Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada (S. Keshavarzi); Department of Library and Information Services, University Health Network-Princess Margaret Cancer Centre, Toronto, Canada (R.F.); and Faculty of Health Sciences, Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, Canada (A.A.)
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10
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The tumor-infiltrating lymphocyte ultrasonography score can provide a diagnostic prediction of lymphocyte-predominant breast cancer preoperatively. J Med Ultrason (2001) 2022; 49:709-717. [PMID: 36002708 DOI: 10.1007/s10396-022-01240-4] [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: 05/02/2022] [Accepted: 06/08/2022] [Indexed: 10/15/2022]
Abstract
PURPOSE Tumor-infiltrating lymphocytes (TILs) are known to predict the therapeutic effect in breast cancer. Although a preoperative tissue biopsy can be used to evaluate TILs, TILs that are heterogeneously distributed might require examination of all preoperative tissue biopsy samples. We have recently reported that the TIL ultrasonography (US) score, as determined by characteristic US findings, provides excellent predictive performance for lymphocyte predominant breast cancer (LPBC). We herein aimed to determine whether the preoperative TIL-US score can more accurately predict LPBC than preoperative tissue biopsy. METHODS We assessed 161 patients with invasive breast cancer that were treated with curative surgery between January 2014 and December 2017. Stromal lymphocytes were examined on preoperative tissue biopsy tissues and surgical pathological specimens. Breast cancer samples with ≥ 50% stromal TILs were defined as pre-LPBC (preoperative tissue biopsy) and LPBC (surgical pathological specimens). Useful factors for predicting LPBC were searched among clinicopathological factors. RESULTS The TIL-US score cutoff value for predicting LPBC was 4 points based on the receiver operating characteristic curves (area under the curve: 0.88). Several significant predictors for LPBC were revealed by the undertaken multivariate logistic regression analysis (odds ratios: TIL-US score, 26.8; pre-LPBC, 18.6; HER2, 9.2; all, p < 0.05). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.74, 0.89, 0.85, 0.67, and 0.92 for the TIL-US score, respectively, and 0.51, 0.98, 0.87, 0.91, and 0.86 for the pre-LPBC, respectively. CONCLUSION TIL-US scores can predict LPBC preoperatively and are characterized by a significantly high sensitivity and negative predictive value.
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11
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Khalid Z, Khan G, Arbab MA. Extrinsically evolved system for breast cancer detection. EVOLUTIONARY INTELLIGENCE 2022. [DOI: 10.1007/s12065-022-00752-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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12
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Mask Branch Network: Weakly Supervised Branch Network with a Template Mask for Classifying Masses in 3D Automated Breast Ultrasound. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Automated breast ultrasound (ABUS) is being rapidly utilized for screening and diagnosing breast cancer. Breast masses, including cancers shown in ABUS scans, often appear as irregular hypoechoic areas that are hard to distinguish from background shadings. We propose a novel branch network architecture incorporating segmentation information of masses in the training process. The branch network is integrated into neural network, providing the spatial attention effect. The branch network boosts the performance of existing classifiers, helping to learn meaningful features around the target breast mass. For the segmentation information, we leverage the existing radiology reports without additional labeling efforts. The reports, which is generated in medical image reading process, should include the characteristics of breast masses, such as shape and orientation, and a template mask can be created in a rule-based manner. Experimental results show that the proposed branch network with a template mask significantly improves the performance of existing classifiers. We also provide qualitative interpretation of the proposed method by visualizing the attention effect on target objects.
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13
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Gordon PB. The Impact of Dense Breasts on the Stage of Breast Cancer at Diagnosis: A Review and Options for Supplemental Screening. Curr Oncol 2022; 29:3595-3636. [PMID: 35621681 PMCID: PMC9140155 DOI: 10.3390/curroncol29050291] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 04/23/2022] [Accepted: 04/25/2022] [Indexed: 11/16/2022] Open
Abstract
The purpose of breast cancer screening is to find cancers early to reduce mortality and to allow successful treatment with less aggressive therapy. Mammography is the gold standard for breast cancer screening. Its efficacy in reducing mortality from breast cancer was proven in randomized controlled trials (RCTs) conducted from the early 1960s to the mid 1990s. Panels that recommend breast cancer screening guidelines have traditionally relied on the old RCTs, which did not include considerations of breast density, race/ethnicity, current hormone therapy, and other risk factors. Women do not all benefit equally from mammography. Mortality reduction is significantly lower in women with dense breasts because normal dense tissue can mask cancers on mammograms. Moreover, women with dense breasts are known to be at increased risk. To provide equity, breast cancer screening guidelines should be created with the goal of maximizing mortality reduction and allowing less aggressive therapy, which may include decreasing the interval between screening mammograms and recommending consideration of supplemental screening for women with dense breasts. This review will address the issue of dense breasts and the impact on the stage of breast cancer at the time of diagnosis, and discuss options for supplemental screening.
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Affiliation(s)
- Paula B Gordon
- Department of Radiology, Faculty of Medicine, University of British Columbia, 505-750 West Broadway, Vancouver, BC V5Z 1H4, Canada
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14
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da Nobrega YNG, Carvalhal G, Teixeira JPV, de Camargo BP, do Rego TG, Malheiros Y, Silva Filho TDME, Vent TL, Acciavatti RJ, Maidment ADA, Barufaldi B. Multiclass Segmentation of Suspicious Findings in Simulated Breast Tomosynthesis Images Using a U-Net. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12286:122860L. [PMID: 39183730 PMCID: PMC11343363 DOI: 10.1117/12.2626225] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Our lab has built a next-generation tomosynthesis (NGT) system utilizing scanning motions with more degrees of freedom than clinical digital breast tomosynthesis systems. We are working toward designing scanning motions that are customized around the locations of suspicious findings. The first step in this direction is to demonstrate that these findings can be detected with a single projection image, which can guide the remainder of the scan. This paper develops an automated method to identify findings that are prone to be masked. Perlin-noise phantoms and synthetic lesions were used to simulate masked cancers. NGT projections of phantoms were simulated using ray-tracing software. The risk of masking cancers was mapped using the ground-truth labels of phantoms. The phantom labels were used to denote regions of low and high risk of masking suspicious findings. A U-Net model was trained for multiclass segmentation of phantom images. Model performance was quantified with a receiver operating characteristic (ROC) curve using area under the curve (AUC). The ROC operating point was defined to be the point closest to the upper left corner of ROC space. The output predictions showed an accurate segmentation of tissue predominantly adipose (mean AUC of 0.93). The predictions also indicate regions of suspicious findings; for the highest risk class, mean AUC was 0.89, with a true positive rate of 0.80 and a true negative rate of 0.83 at the operating point. In summary, this paper demonstrates with virtual phantoms that a single projection can indeed be used to identify suspicious findings.
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15
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Ha SM, Kim HK, Kim Y, Noh DY, Han W, Chang JM. Diagnostic performance improvement with combined use of proteomics biomarker assay and breast ultrasound. Breast Cancer Res Treat 2022; 192:541-552. [PMID: 35084623 DOI: 10.1007/s10549-022-06527-1] [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: 10/28/2021] [Accepted: 01/16/2022] [Indexed: 11/27/2022]
Abstract
PURPOSE To investigate the combined use of blood-based 3-protein signature and breast ultrasound (US) for validating US-detected lesions. METHODS From July 2011 to April 2020, women who underwent whole-breast US within at least 6 months from sampling period were retrospectively included. Blood-based 3-protein signature (Mastocheck®) value and US findings were evaluated. Following outcome measures were compared between US alone and the combination of Mastocheck® value with US: sensitivity, specificity, positive predictive value (PPV), negative predictive value, area under the receiver operating characteristic curve (AUC), and biopsy rate. RESULTS Among the 237 women included, 59 (24.9%) were healthy individuals and 178 (75.1%) cancer patients. Mean size of cancers was 1.2 ± 0.8 cm. Median value of Mastocheck® was significantly different between nonmalignant (- 0.24, interquartile range [IQR] - 0.48, - 0.03) and malignant lesions (0.55, IQR - 0.03, 1.42) (P < .001). Utilizing Mastocheck® value with US increased the AUC from 0.67 (95% confidence interval [CI] 0.61, 0.73) to 0.81 (95% CI 0.75, 0.88; P < .001), and specificity from 35.6 (95% CI 23.4, 47.8) to 64.4% (95% CI 52.2, 76.6; P < .001) without loss in sensitivity. PPV was increased from 82.2 (95% CI 77.1, 87.3) to 89.3% (95% CI 85.0, 93.6; P < .001), and biopsy rate was significantly decreased from 79.3 (188/237) to 72.1% (171/237) (P < .001). Consistent improvements in specificity, PPV, and AUC were observed in asymptomatic women, in women with dense breast, and in those with normal/benign mammographic findings. CONCLUSION Mastocheck® is an effective tool that can be used with US to improve diagnostic specificity and reduce false-positive findings and unnecessary biopsies.
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Affiliation(s)
- Su Min Ha
- Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Hong-Kyu Kim
- Department of Surgery, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - Yumi Kim
- Department of Surgery, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
- Department of Surgery, CHA University Gangnam Medical Center, Seoul, Republic of Korea
| | - Dong-Young Noh
- Department of Surgery, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
- Department of Surgery, CHA University Gangnam Medical Center, Seoul, Republic of Korea
| | - Wonshik Han
- Department of Surgery, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Jung Min Chang
- Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea.
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16
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Li Y, Liu Y, Huang L, Wang Z, Luo J. Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints. Med Image Anal 2021; 76:102315. [PMID: 34902792 DOI: 10.1016/j.media.2021.102315] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 11/16/2021] [Accepted: 11/23/2021] [Indexed: 12/24/2022]
Abstract
Breast tumor segmentation is an important step in the diagnostic procedure of physicians and computer-aided diagnosis systems. We propose a two-step deep learning framework for breast tumor segmentation in breast ultrasound (BUS) images which requires only a few manual labels. The first step is breast anatomy decomposition handled by a semi-supervised semantic segmentation technique. The input BUS image is decomposed into four breast anatomical structures, namely fat, mammary gland, muscle and thorax layers. Fat and mammary gland layers are used as constrained region to reduce the search space for breast tumor segmentation. The second step is breast tumor segmentation performed in a weakly-supervised learning scenario where only image-level labels are available. Breast tumors are first recognized by a classification network and then segmented by the proposed class activation mapping and deep level set (CAM-DLS) method. For breast anatomy decomposition, the proposed framework achieves Dice similarity coefficient (DSC) of 83.0 ± 11.8%, 84.3 ± 10.0%, 80.7 ± 15.4% and 91.0 ± 11.4% for fat, mammary gland, muscle and thorax layers, respectively. For breast tumor recognition, the proposed framework achieves sensitivity of 95.8%, precision of 92.4%, specificity of 93.9%, accuracy of 94.8% and F1-score of 0.941. For breast tumor segmentation, the proposed framework achieves DSC of 77.3% and intersection-over-union (IoU) of 66.0%. In conclusion, the proposed framework could efficiently perform breast tumor recognition and segmentation simultaneously in a weakly-supervised setting with anatomical constraints.
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Affiliation(s)
- Yongshuai Li
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Yuan Liu
- Senior Department of Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing 100039, China; Senior Department of Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing 100039, China
| | - Lijie Huang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Zhili Wang
- Department of Ultrasound, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China.
| | - Jianwen Luo
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.
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17
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Shen Y, Shamout FE, Oliver JR, Witowski J, Kannan K, Park J, Wu N, Huddleston C, Wolfson S, Millet A, Ehrenpreis R, Awal D, Tyma C, Samreen N, Gao Y, Chhor C, Gandhi S, Lee C, Kumari-Subaiya S, Leonard C, Mohammed R, Moczulski C, Altabet J, Babb J, Lewin A, Reig B, Moy L, Heacock L, Geras KJ. Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun 2021; 12:5645. [PMID: 34561440 PMCID: PMC8463596 DOI: 10.1038/s41467-021-26023-2] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 09/14/2021] [Indexed: 02/08/2023] Open
Abstract
Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.
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Affiliation(s)
- Yiqiu Shen
- grid.137628.90000 0004 1936 8753Center for Data Science, New York University, New York, NY USA
| | - Farah E. Shamout
- grid.440573.1Engineering Division, NYU Abu Dhabi, Abu Dhabi, UAE
| | - Jamie R. Oliver
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Jan Witowski
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Kawshik Kannan
- grid.482020.c0000 0001 1089 179XDepartment of Computer Science, Courant Institute, New York University, New York, NY USA
| | - Jungkyu Park
- grid.137628.90000 0004 1936 8753Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY USA
| | - Nan Wu
- grid.137628.90000 0004 1936 8753Center for Data Science, New York University, New York, NY USA
| | - Connor Huddleston
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Stacey Wolfson
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Alexandra Millet
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Robin Ehrenpreis
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Divya Awal
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Cathy Tyma
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Naziya Samreen
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Yiming Gao
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Chloe Chhor
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Stacey Gandhi
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Cindy Lee
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Sheila Kumari-Subaiya
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Cindy Leonard
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Reyhan Mohammed
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Christopher Moczulski
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Jaime Altabet
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - James Babb
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Alana Lewin
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Beatriu Reig
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Linda Moy
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA ,grid.137628.90000 0004 1936 8753Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY USA
| | - Laura Heacock
- grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA
| | - Krzysztof J. Geras
- grid.137628.90000 0004 1936 8753Center for Data Science, New York University, New York, NY USA ,grid.137628.90000 0004 1936 8753Department of Radiology, NYU Grossman School of Medicine, New York, NY USA ,grid.137628.90000 0004 1936 8753Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY USA
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18
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Klevos GA, Collado-Mesa F, Net JM, Yepes MM. Utility of supplemental screening with breast ultrasound in asymptomatic women with dense breast tissue who are not at high risk for breast cancer. Indian J Radiol Imaging 2021; 27:52-58. [PMID: 28515586 PMCID: PMC5385776 DOI: 10.4103/0971-3026.202962] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Objective: To assess the results of an initial round of supplemental screening with hand-held bilateral breast ultrasound following a negative screening mammogram in asymptomatic women with dense breast tissue who are not at high risk for breast cancer. Materials and Methods: A retrospective, Health Insurance Portability and Accountability Act compliant, Institutional Research Board approved study was performed at a single academic tertiary breast center. Informed consent was waived. A systematic review of the breast imaging center database was conducted to identify and retrieve data for all asymptomatic women, who were found to have heterogeneously dense or extremely dense breast tissue on screening bilateral mammograms performed from July 1, 2010 through June 30, 2012 and who received a mammographic final assessment American College of Radiology's (ACR) Breast Imaging Reporting and Data System (BI-RADS) category 1 or BI-RADS category 2. Hand-held screening ultrasound was performed initially by a technologist followed by a radiologist. Chi-square and t-test were used and statistical significance was considered at P < 0.05. Results: A total of 1210 women were identified. Of these, 394 underwent the offered supplemental screening ultrasound. BI-RADS category 1 or 2 was assigned to 323 women (81.9%). BI-RADS category 3 was assigned to 50 women (12.9%). A total of 26 biopsies/aspirations were recommended and performed in 26 women (6.6%). The most common finding for which biopsy was recommended was a solid mass (88.5%) with an average size of 0.9 cm (0.5–1.7 cm). Most frequent pathology result was fibroadenoma (60.8%). No carcinoma was found. Conclusion: Our data support the reported occurrence of a relatively high number of false positives at supplemental screening with breast ultrasound following a negative screening mammogram in asymptomatic women with dense breast tissue, who are not at a high risk of developing breast cancer, and suggests that caution is necessary in establishing wide implementation of this type of supplemental screening for all women with dense breast tissue without considering other risk factors for breast cancer.
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Affiliation(s)
- Geetika A Klevos
- Department of Radiology, Sylvester Comprehensive Cancer Center, University of Miami Hospital and Clinics, Miami, Florida, USA
| | - Fernando Collado-Mesa
- Department of Radiology, Sylvester Comprehensive Cancer Center, University of Miami Hospital and Clinics, Miami, Florida, USA
| | - Jose M Net
- Department of Radiology, Sylvester Comprehensive Cancer Center, University of Miami Hospital and Clinics, Miami, Florida, USA
| | - Monica M Yepes
- Department of Radiology, Sylvester Comprehensive Cancer Center, University of Miami Hospital and Clinics, Miami, Florida, USA
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SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11073248] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Early diagnosis of breast cancer unequivocally improves the survival rate of patients and is crucial for disease treatment. With the current developments in infrared imaging, breast screening using dynamic thermography seems to be a great complementary method for clinical breast examination (CBE) prior to mammography. In this study, we propose a sparse deep convolutional autoencoder model named SPAER to extract low-dimensional deep thermomics to aid breast cancer diagnosis. The model receives multichannel, low-rank, approximated thermal bases as input images. SPAER provides a solution for high-dimensional deep learning features and selects the predominant basis matrix using matrix factorization techniques. The model has been evaluated using five state-of-the-art matrix factorization methods and 208 thermal breast cancer screening cases. The best accuracy was for non-negative matrix factorization (NMF)-SPAER + Clinical and NMF-SPAER for maintaining thermal heterogeneity, leading to finding symptomatic cases with accuracies of 78.2% (74.3–82.5%) and 77.7% (70.9–82.1%), respectively. SPAER showed significant robustness when tested for additive Gaussian noise cases (3–20% noise), evaluated by the signal-to-noise ratio (SNR). The results suggest high performance of SPAER for preserveing thermal heterogeneity, and it can be used as a noninvasive in vivo tool aiding CBE in the early detection of breast cancer.
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20
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Fatima K, Mohsin F, Rao MO, Alvi MI. Mammographic Breast Density in Pakistani Women, Factors Affecting It, and Inter-Observer Variability in Assessment. Cureus 2021; 13:e14050. [PMID: 33898136 PMCID: PMC8059668 DOI: 10.7759/cureus.14050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Introduction Breast density on mammography can affect the sensitivity of breast cancer detection and is an independent risk factor for breast cancer. The incidence of breast cancer in Pakistani women is reported to be the highest among women in Asia. No published data is describing the patterns of mammographic breast density in this population. We undertook this study to assess the Breast Imaging Reporting and Data System (BI-RADS) patterns of breast density on mammography, factors that affect breast density, and inter-observer variability in breast density assessment. Methods Bilateral breast mammograms were retrospectively reviewed for breast density by two separate readers (resident and attending radiologist). Breast density was categorized into four types according to the BI-RADS lexicon. Types 1 and 2 were grouped into non-dense and types 3 and 4 into dense breasts. The association of patient factors with breast density was assessed, with p < 0.05 considered statistically significant. The inter-observer variability in breast density assessment between the two readers was calculated using Cohen's κ coefficient. Results A total of 612 women underwent mammography in the study period. Type 3 (heterogeneously dense breast parenchyma) was the most frequent pattern (51.6%) followed by type 2 (scattered fibroglandular) pattern (38.9%). Fatty parenchyma (type 1) and extremely dense parenchyma (type 4) were the least common. Breast density was inversely related to age (p < 0.001) and parity (p <0.002). Breast density was also lower in postmenopausal women (p < 0.001). There was no statistically significant difference in mean age at menarche, age at first delivery, family history of breast cancer, or presence of cancer among women with dense and non-dense breasts. The inter-observer agreement was almost perfect (κ = 0.86). Conclusion The majority of women in our population (56.9%) had dense breasts (BI-RADS type 3 and 4) which decrease the sensitivity of breast cancer detection on mammography suggesting it may be insufficient as the sole screening/diagnostic tool in this population. Lower breast density was associated with increasing age, parity, and post-menopausal status. Breast density assessment was almost perfect among the resident and attending radiologist.
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Affiliation(s)
| | - Farwa Mohsin
- Radiology, Aga Khan University Hospital, Karachi, PAK
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21
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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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Wang L, Strigel RM. Supplemental Screening for Patients at Intermediate and High Risk for Breast Cancer. Radiol Clin North Am 2020; 59:67-83. [PMID: 33223001 DOI: 10.1016/j.rcl.2020.09.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The sensitivity of mammography is more limited in patients with dense breasts and some patients at higher risk for breast cancer. Patients with intermediate or high risk for breast cancer may begin screening earlier and benefit from supplemental screening techniques beyond standard 2-dimensional mammography. A patient's individual risk factors for developing breast cancer, their breast density, and the evidence supporting specific modalities for a given clinical scenario help to determine the need for supplemental screening and the modality chosen. Additional factors include the availability of supplemental screening techniques at an individual institution, cost, insurance coverage, and state-specific breast density legislation.
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Affiliation(s)
- Lilian Wang
- Northwestern Medicine, Chicago, IL, USA; Prentice Women's Hospital, 250 East Superior Street, 4th Floor, Room 04-2304, Chicago, IL 60611, USA
| | - Roberta M Strigel
- Breast Imaging and Intervention, University of Wisconsin, 600 Highland Avenue, Madison, WI 53792-3252, USA.
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Yousefi B, Akbari H, Maldague XP. Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics. BIOSENSORS 2020; 10:E164. [PMID: 33142939 PMCID: PMC7693609 DOI: 10.3390/bios10110164] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/29/2020] [Accepted: 10/30/2020] [Indexed: 12/11/2022]
Abstract
Breast cancer is the most common cancer in women. Early diagnosis improves outcome and survival, which is the cornerstone of breast cancer treatment. Thermography has been utilized as a complementary diagnostic technique in breast cancer detection. Artificial intelligence (AI) has the capacity to capture and analyze the entire concealed information in thermography. In this study, we propose a method to potentially detect the immunohistochemical response to breast cancer by finding thermal heterogeneous patterns in the targeted area. In this study for breast cancer screening 208 subjects participated and normal and abnormal (diagnosed by mammography or clinical diagnosis) conditions were analyzed. High-dimensional deep thermomic features were extracted from the ResNet-50 pre-trained model from low-rank thermal matrix approximation using sparse principal component analysis. Then, a sparse deep autoencoder designed and trained for such data decreases the dimensionality to 16 latent space thermomic features. A random forest model was used to classify the participants. The proposed method preserves thermal heterogeneity, which leads to successful classification between normal and abnormal subjects with an accuracy of 78.16% (73.3-81.07%). By non-invasively capturing a thermal map of the entire tumor, the proposed method can assist in screening and diagnosing this malignancy. These thermal signatures may preoperatively stratify the patients for personalized treatment planning and potentially monitor the patients during treatment.
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Affiliation(s)
- Bardia Yousefi
- Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Hamed Akbari
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Xavier P.V. Maldague
- Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
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Kore SS, Kadam AB. A novel incomplete sparse least square optimized regression model for abdominal mass detection in ultrasound images. EVOLUTIONARY INTELLIGENCE 2020. [DOI: 10.1007/s12065-020-00431-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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25
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Supplemental breast cancer-screening ultrasonography in women with dense breasts: a systematic review and meta-analysis. Br J Cancer 2020; 123:673-688. [PMID: 32528118 PMCID: PMC7434777 DOI: 10.1038/s41416-020-0928-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 05/14/2020] [Accepted: 05/21/2020] [Indexed: 01/09/2023] Open
Abstract
Background Mammography is not effective in detecting breast cancer in dense breasts. Methods A search in Medline, Cochrane, EMBASE and Google Scholar databases was conducted from January 1, 1980 to April 10, 2019 to identify women with dense breasts screened by mammography (M) and/or ultrasound (US). Meta-analysis was performed using the random-effect model. Results A total of 21 studies were included. The pooled sensitivity values of M alone and M + US in patients were 74% and 96%, while specificity of the two methods were 93% and 87%, respectively. Screening sensitivity was significantly higher in M + US than M alone (risk ratio: M alone vs. M + US = 0.699, P < 0.001), but the slight difference in specificity was statistically significant (risk ratio = 1.060, P = 0.001). Pooled diagnostic performance of follow-up US after initial negative mammography demonstrated a high pooled sensitivity (96%) and specificity (88%). The findings were supported by subgroup analysis stratified by study country, US method and timing of US. Conclusions Breast cancer screening by supplemental US among women with dense breasts shows added detection sensitivity compared with M alone. However, US slightly decreased the diagnostic specificity for breast cancer. The cost-effectiveness of supplemental US in detecting malignancy in dense breasts should be considered additionally.
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26
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Screening Breast Ultrasound: Update After 10 Years of Breast Density Notification Laws. AJR Am J Roentgenol 2020; 214:1424-1435. [DOI: 10.2214/ajr.19.22275] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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27
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Chough DM, Berg WA, Bandos AI, Rathfon GY, Hakim CM, Lu AH, Gizienski TA, Ganott MA, Gur D. A Prospective Study of Automated Breast Ultrasound Screening of Women with Dense Breasts in a Digital Breast Tomosynthesis-based Practice. JOURNAL OF BREAST IMAGING 2020; 2:125-133. [PMID: 38424893 DOI: 10.1093/jbi/wbaa006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Indexed: 03/02/2024]
Abstract
OBJECTIVE To assess prospectively the interpretative performance of automated breast ultrasound (ABUS) as a supplemental screening after digital breast tomosynthesis (DBT) or as a standalone screening of women with dense breast tissue. METHODS Under an IRB-approved protocol (written consent required), women with dense breasts prospectively underwent concurrent baseline DBT and ABUS screening. Examinations were independently evaluated, in opposite order, by two of seven Mammography Quality Standards Act-qualified radiologists, with the primary radiologist arbitrating disagreements and making clinical management recommendations. We report results for 1111 screening examinations (598 first year and 513 second year) for which all diagnostic workups are complete. Imaging was also retrospectively reviewed for all cancers. Statistical assessments used a 0.05 significance level and accounted for correlation between participants' examinations. RESULTS Of 1111 women screened, primary radiologists initially "recalled" based on DBT alone (6.6%, 73/1111, CI: 5.2%-8.2%), of which 20 were biopsied, yielding 6/8 total cancers. Automated breast ultrasound increased recalls overall to 14.4% (160/1111, CI: 12.4%-16.6%), with 27 total biopsies, yielding 1 additional cancer. Double reading of DBT alone increased the recall rate to 10.7% (119/1111), with 21 biopsies, with no improvement in cancer detection. Double reading ABUS increased the recall rate to 15.2% (169/1111, CI: 13.2%-17.5%) of women, of whom 22 were biopsied, yielding the detection of 7 cancers, including one seen only on double reading ABUS. Inter-radiologist agreement was similar for recall recommendations from DBT (κ = 0.24, CI: 0.14-0.34) and ABUS (κ = 0.23, CI: 0.15-0.32). Integrated assessments from both readers resulted in a recall rate of 15.1% (168/1111, CI: 13.1%-17.4%). CONCLUSION Supplemental or standalone ABUS screening detected cancers not seen on DBT, but substantially increased noncancer recall rates.
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Affiliation(s)
- Denise M Chough
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA
| | - Wendie A Berg
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA
| | - Andriy I Bandos
- University of Pittsburgh, Graduate School of Public Health, Department of Biostatistics, Pittsburgh, PA
| | | | - Christiane M Hakim
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA
| | - Amy H Lu
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA
| | - Terri-Ann Gizienski
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA
| | - Marie A Ganott
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA
| | - David Gur
- University of Pittsburgh School of Medicine, Department of Radiology, Radiology Imaging Research, Pittsburgh, PA
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Goto Y, Tsugawa K, Furuya Y, Maezato M, Tagami Y, Ogawa Y, Saisu M, Yamazaki M, Kuramochi F. Behavior of Japanese women after being informed about the benefits and disadvantages of breast cancer screening: a questionnaire survey. Breast Cancer 2020; 27:739-747. [PMID: 32140843 DOI: 10.1007/s12282-020-01071-6] [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] [Received: 07/31/2019] [Accepted: 02/25/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND The US Preventative Services Task Force assessed the efficacy of breast cancer screening according to the sum of its benefits and disadvantages. We estimate that the balance of the benefits and disadvantages varies among women depending on their demographic background. METHODS Between March 2016 and March 2017, we conducted a questionnaire survey among Japanese women who underwent population-based or opportunistic breast cancer screening at our multicenter institutions. We investigated the behavior modification among women after being informed about the benefits and disadvantages of breast cancer screening depending on their demographic background. RESULTS Out of 3032 questionnaires that were returned, 2936 (96.8%) were evaluated. The percentage of women with prior knowledge about the benefits and disadvantages of breast cancer screening before reading the leaflets that we created was 24%. However, 95% of the women were willing to undergo screening next time, despite knowing the disadvantages. Regarding overdiagnosis, the young women tended to choose usual treatment, and the elderly women tended to choose active surveillance. In response to the question on the significance of screening, the young women wished to avoid death by breast cancer; whereas, the elderly women wished to live a safe life. CONCLUSION Our results indicate that the information of disadvantages does not lead to a reduction in screening rates. Additionally, we found that the balance between the benefits and disadvantages of breast cancer screening varies among women depending on their demographic background, especially age.
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Affiliation(s)
- Yuka Goto
- Advanced Breast Imaging Center of St. Marianna University School of Medicine, 6-7-2 Manpukuji Asao-ku, Kawasaki, Kanagawa, 215-8520, Japan.
| | - Koichiro Tsugawa
- Division of Breast Endocrine Surgery, Department of Surgery, St. Marianna University School of Medicine Hospital, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Yuko Furuya
- Advanced Breast Imaging Center of St. Marianna University School of Medicine, 6-7-2 Manpukuji Asao-ku, Kawasaki, Kanagawa, 215-8520, Japan
| | - Miwako Maezato
- Imaging Center of St. Marianna University School of Medicine Hospital, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Yoshimi Tagami
- Imaging Center of St. Marianna University School of Medicine Hospital, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Yuri Ogawa
- Imaging Center of St. Marianna University School of Medicine Hospital, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Misako Saisu
- Imaging Center of St. Marianna University School of Medicine Hospital, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Memi Yamazaki
- St. Marianna University Yokohama City Seibu Hospital, 1197-1 Yasashichou, Asahi-ku, Yokohama, Kanagawa, 241-0811, Japan
| | - Fuyumi Kuramochi
- Kawasaki Municipal Tama Hospital, 1-30-37 Syukugawara, Tama-ku, Kawasaki, Kanagawa, 214-8525, Japan
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Ko ES, Morris EA. Abbreviated Magnetic Resonance Imaging for Breast Cancer Screening: Concept, Early Results, and Considerations. Korean J Radiol 2020; 20:533-541. [PMID: 30887736 PMCID: PMC6424827 DOI: 10.3348/kjr.2018.0722] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 12/27/2018] [Indexed: 12/29/2022] Open
Abstract
Breast magnetic resonance imaging (MRI) has been increasingly utilized, especially in screening for high-risk cases, because of its high sensitivity and superior ability to detect cancers as compared with mammography and ultrasound. Several limitations such as higher cost, longer examination time, longer interpretation time, and low availability have hindered the wider application of MRI, especially for screening of average-risk women. To overcome some of these limitations and increase access to MRI screening, an abbreviated breast MRI protocol has been introduced. Abbreviated breast MRI is becoming popular and challenges the status quo. This review aims to present an overview of abbreviated MRI, discuss the current findings, and introduce ongoing prospective trials.
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Affiliation(s)
- Eun Sook Ko
- Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.,Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
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Berg WA, Vourtsis A. Screening Breast Ultrasound Using Handheld or Automated Technique in Women with Dense Breasts. JOURNAL OF BREAST IMAGING 2019; 1:283-296. [PMID: 38424808 DOI: 10.1093/jbi/wbz055] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 08/01/2019] [Indexed: 03/02/2024]
Abstract
In women with dense breasts (heterogeneously or extremely dense), adding screening ultrasound to mammography increases detection of node-negative invasive breast cancer. Similar incremental cancer detection rates averaging 2.1-2.7 per 1000 have been observed for physician- and technologist-performed handheld ultrasound (HHUS) and automated ultrasound (AUS). Adding screening ultrasound (US) for women with dense breasts significantly reduces interval cancer rates. Training is critical before interpreting examinations for both modalities, and a learning curve to achieve optimal performance has been observed. On average, about 3% of women will be recommended for biopsy on the prevalence round because of screening US, with a wide range of 2%-30% malignancy rates for suspicious findings seen only on US. Breast Imaging Reporting and Data System 3 lesions identified only on screening HHUS can be safely followed at 1 year rather than 6 months. Computer-aided detection and diagnosis software can augment performance of AUS and HHUS; ongoing research on machine learning and deep learning algorithms will likely improve outcomes and workflow with screening US.
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Affiliation(s)
- Wendie A Berg
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of the University of Pittsburgh School of Medicine, Department of Radiology, Pittsburgh, PA
| | - Athina Vourtsis
- Diagnostic Mammography Medical Diagnostic Imaging Unit, Athens, Greece
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31
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Tanaka H, Chiu SW, Watanabe T, Kaoku S, Yamaguchi T. Computer-aided diagnosis system for breast ultrasound images using deep learning. Phys Med Biol 2019; 64:235013. [PMID: 31645021 DOI: 10.1088/1361-6560/ab5093] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The purpose of this study was to develop a computer-aided diagnosis (CAD) system for the classification of malignant and benign masses in the breast using ultrasonography based on a convolutional neural network (CNN), a state-of-the-art deep learning technique. We explored the regions for the correct classification by generating a heat map that presented the important regions used by the CNN for human malignancy/benign classification. Clinical data was obtained from a large-scale clinical trial previously conducted by the Japan Association of Breast and Thyroid Sonology. Images of 1536 breast masses (897 malignant and 639 benign) confirmed by pathological examinations were collected, with each breast mass captured from various angles using an ultrasound (US) imaging probe. We constructed an ensemble network by combining two CNN models (VGG19 and ResNet152) fine-tuned on balanced training data with augmentation and used the mass-level classification method to enable the CNN to classify a given mass using all views. For an independent test set consisting of 154 masses (77 malignant and 77 benign), our network showed outstanding classification performance with a sensitivity of 90.9% (95% confidence interval 84.5-97.3), a specificity of 87.0% (79.5-94.5), and area under the curve (AUC) of 0.951 (0.916-0.987) compared to that of the two CNN models. In addition, our study indicated that the breast masses themselves were not detected by the CNN as important regions for correct mass classification. Collectively, this CNN-based CAD system is expected to assist doctors by improving the diagnosis of breast cancer in clinical practice.
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Affiliation(s)
- Hiroki Tanaka
- Division of Biostatistics, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan. Author to whom any correspondence should be addressed
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Rajitha B, Malla RR, Vadde R, Kasa P, Prasad GLV, Farran B, Kumari S, Pavitra E, Kamal MA, Raju GSR, Peela S, Nagaraju GP. Horizons of nanotechnology applications in female specific cancers. Semin Cancer Biol 2019; 69:376-390. [PMID: 31301361 DOI: 10.1016/j.semcancer.2019.07.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 06/23/2019] [Accepted: 07/04/2019] [Indexed: 12/20/2022]
Abstract
Female-specific cancers are the most common cancers in women worldwide. Early detection methods remain unavailable for most of these cancers, signifying that most of them are diagnosed at later stages. Furthermore, current treatment options for most female-specific cancers are surgery, radiation and chemotherapy. Although important milestones in molecularly targeted approaches have been achieved lately, current therapeutic strategies for female-specific cancers remain limited, ineffective and plagued by the emergence of chemoresistance, which aggravates prognosis. Recently, the application of nanotechnology to the medical field has allowed the development of novel nano-based approaches for the management and treatment of cancers, including female-specific cancers. These approaches promise to improve patient survival rates by reducing side effects, enabling selective delivery of drugs to tumor tissues and enhancing the uptake of therapeutic compounds, thus increasing anti-tumor activity. In this review, we focus on the application of nano-based technologies to the design of novel and innovative diagnostic and therapeutic strategies in the context of female-specific cancers, highlighting their potential uses and limitations.
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Affiliation(s)
- Balney Rajitha
- Department of Pathology, WellStar Hospital, Marietta, GA, 30060, USA
| | - Rama Rao Malla
- Department of Biochemistry, GITAM Institute of Science, GITAM University, Visakhapatnam, AP, 530045, India
| | - Ramakrishna Vadde
- Department of Biotechnology and Bioinformatics, Yogi Vemana University, Kadapa, AP, 516003, India
| | - Prameswari Kasa
- Dr. LV Prasad Diagnostics and Research Laboratory, Khairtabad, Hyderabad, TS, 500004, India
| | | | - Batoul Farran
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Seema Kumari
- Department of Biochemistry, GITAM Institute of Science, GITAM University, Visakhapatnam, AP, 530045, India
| | - Eluri Pavitra
- Department of Biological Engineering, Biohybrid Systems Research Center (BSRC), Inha University, 100, Inha-ro, Incheon 22212, Republic of Korea
| | - Mohammad Amjad Kamal
- King Fahd Medical Research Center, King Abdulaziz University, P. O. Box 80216, Jeddah 21589, Saudi Arabia; Enzymoics, 7 Peterlee Place, Hebersham, NSW 2770, Australia; Novel Global Community Educational Foundation, Australia
| | - Ganji Seeta Rama Raju
- Department of Energy and Materials Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea
| | - Sujatha Peela
- Department of Biotechnology, Dr. B.R. Ambedkar University, Srikakulam, AP, 532410, India
| | - Ganji Purnachandra Nagaraju
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.
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Lux MP, Emons J, Bani MR, Wunderle M, Sell C, Preuss C, Rauh C, Jud SM, Heindl F, Langemann H, Geyer T, Brandl AL, Hack CC, Adler W, Schulz-Wendtland R, Beckmann MW, Fasching PA, Gass P. Diagnostic Accuracy of Breast Medical Tactile Examiners (MTEs): A Prospective Pilot Study. Breast Care (Basel) 2019; 14:41-47. [PMID: 31019442 DOI: 10.1159/000495883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Background The usefulness of clinical breast examination (CBE) in general and in breast cancer screening programs has been a matter of debate. This study investigated whether adding vision-impaired medical tactile examiners (MTEs) improves the predictiveness of CBE for suspicious lesions and analyzed the feasibility and acceptability of this approach. Methods The prospective study included 104 patients. Physicians and MTEs performed CBEs, and mammography and ultrasound results were used as the gold standard. Sensitivity and specificity were calculated and logistic regression models were used to compare the predictive value of CBE by physicians alone, MTEs alone, and physicians and MTEs combined. Results For CBEs by physicians alone, MTEs alone, and both combined, sensitivity was 71, 82, and 89% and specificity was 55, 45, and 35%, respectively. Using adjusted logistic regression models, the validated areas under the curve were 0.685, 0.692, and 0.710 (median bootstrapped p value (DeLong) = 0.381). Conclusion The predictive value for a suspicious breast lesion in CBEs performed by MTEs in patients without prior surgery was similar to that of physician-conducted CBEs. Including MTEs in the CBE procedure in breast units thus appears feasible and could be a way of utilizing their skills.
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Affiliation(s)
- Michael P Lux
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Julius Emons
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Mayada R Bani
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Marius Wunderle
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Charlotte Sell
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Caroline Preuss
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Claudia Rauh
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Sebastian M Jud
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Felix Heindl
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Hanna Langemann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Thomas Geyer
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Anna-Lisa Brandl
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Carolin C Hack
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Werner Adler
- Institute of Biometry and Epidemiology, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | | | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Paul Gass
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
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Dória MT, Jales RM, Conz L, Derchain SFM, Sarian LOZ. Diagnostic accuracy of shear wave elastography – Virtual touch™ imaging quantification in the evaluation of breast masses: Impact on ultrasonography’s specificity and its ultimate clinical benefit. Eur J Radiol 2019; 113:74-80. [DOI: 10.1016/j.ejrad.2019.02.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 01/29/2019] [Accepted: 02/03/2019] [Indexed: 12/18/2022]
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Vourtsis A, Berg WA. Breast density implications and supplemental screening. Eur Radiol 2019; 29:1762-1777. [PMID: 30255244 PMCID: PMC6420861 DOI: 10.1007/s00330-018-5668-8] [Citation(s) in RCA: 109] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 06/21/2018] [Accepted: 07/13/2018] [Indexed: 12/14/2022]
Abstract
Digital breast tomosynthesis (DBT) has been widely implemented in place of 2D mammography, although it is less effective in women with extremely dense breasts. Breast ultrasound detects additional early-stage, invasive breast cancers when combined with mammography; however, its relevant limitations, including the shortage of trained operators, operator dependence and small field of view, have limited its widespread implementation. Automated breast sonography (ABS) is a promising technique but the time to interpret and false-positive rates need to be improved. Supplemental screening with contrast-enhanced magnetic resonance imaging (MRI) in high-risk women reduces late-stage disease; abbreviated MRI protocols may reduce cost and increase accessibility to women of average risk with dense breasts. Contrast-enhanced digital mammography (CEDM) and molecular breast imaging improve cancer detection but require further validation for screening and direct biopsy guidance should be implemented for any screening modality. This article reviews the status of screening women with dense breasts. KEY POINTS: • The sensitivity of mammography is reduced in women with dense breasts. Supplemental screening with US detects early-stage, invasive breast cancers. • Tomosynthesis reduces recall rate and increases cancer detection rate but is less effective in women with extremely dense breasts. • Screening MRI improves early diagnosis of breast cancer more than ultrasound and is currently recommended for women at high risk. Risk assessment is needed, to include breast density, to ascertain who should start early annual MRI screening.
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Affiliation(s)
- Athina Vourtsis
- "Diagnostic Mammography", Medical Diagnostic Imaging Unit, Founding President of the Hellenic Breast Imaging Society, Kifisias Ave 362, Chalandri, 15233, Athens, Greece.
| | - Wendie A Berg
- Department of Radiology, Magee-Womens Hospital of UPMC, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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Fukui K, Masumoto N, Shiroma N, Kanou A, Sasada S, Emi A, Kadoya T, Yokozaki M, Arihiro K, Okada M. Novel tumor-infiltrating lymphocytes ultrasonography score based on ultrasonic tissue findings predicts tumor-infiltrating lymphocytes in breast cancer. Breast Cancer 2019; 26:573-580. [DOI: 10.1007/s12282-019-00958-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 02/24/2019] [Indexed: 01/23/2023]
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Pu H, Zhang XL, Xiang LH, Zhang JL, Xu G, Liu H, Tang GY, Zhao BH, Wu R. The efficacy of added shear wave elastography (SWE) in breast screening for women with inconsistent mammography and conventional ultrasounds (US). Clin Hemorheol Microcirc 2019; 71:83-94. [PMID: 29843228 DOI: 10.3233/ch-180398] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Huan Pu
- Department of Medical Ultrasound, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
- Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Xue-Li Zhang
- Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Li-Hua Xiang
- Department of Medical Ultrasound, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
- Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Jiu-Long Zhang
- Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Guang Xu
- Department of Medical Ultrasound, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
- Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Hui Liu
- Department of Medical Ultrasound, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
- Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Guang-Yu Tang
- Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Bing-Hui Zhao
- Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Rong Wu
- Department of Medical Ultrasound, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
- Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
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Huppe AI, Inciardi MF, Redick M, Carroll M, Buckley J, Hill JD, Gatewood JB. Automated Breast Ultrasound Interpretation Times: A Reader Performance Study. Acad Radiol 2018; 25:1577-1581. [PMID: 29661602 DOI: 10.1016/j.acra.2018.03.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 03/15/2018] [Accepted: 03/15/2018] [Indexed: 11/19/2022]
Abstract
RATIONALE AND OBJECTIVES This study aimed to determine the average time for breast radiologists of varied experience to interpret automated breast ultrasound (ABUS) examinations. MATERIALS AND METHODS A reader performance study was conducted on female patients, with ACR BI-RADS 4 breast density classifications of C or D, who received both an ABUS screening examination and a digital mammogram from 2013 to 2014 at an academic institution. Three faculty breast radiologists with varied levels of ABUS experience (advanced, intermediate, novice) read all ABUS examinations, with interpretation times and final impressions (categorized as "normal" or "abnormal") recorded for each examination. RESULTS Ninety-nine patients were included, with all readers demonstrating an average ABUS interpretation time of less than 3 minutes. Compared to the other two readers, the intermediate reader had a significantly longer mean interpretation time at 2.6 minutes (95% confidence interval 2.4-2.8; P < .001). In addition to having the shortest mean interpretation time, the novice reader also demonstrated reduced times in subsequent interpretations, with a significant decrease in interpretation times of 3.1 seconds (95% confidence interval 0.4-5.8) for every 10 ABUS examinations interpreted (P < .05). CONCLUSIONS Overall, mean ABUS interpretation time by radiologists of all experience levels was short, at less than 3 minutes per examination, which should not deter radiologists from incorporating ABUS examinations into a busy clinical environment.
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Affiliation(s)
- Ashley I Huppe
- The University of Kansas Medical Center, 3901 Rainbow Boulevard, Mail Stop 4032, Kansas City, KS 66160.
| | - Marc F Inciardi
- The University of Kansas Medical Center, 3901 Rainbow Boulevard, Mail Stop 4032, Kansas City, KS 66160
| | - Mark Redick
- The University of Kansas Medical Center, 3901 Rainbow Boulevard, Mail Stop 4032, Kansas City, KS 66160
| | - Melissa Carroll
- The University of Kansas Medical Center, 3901 Rainbow Boulevard, Mail Stop 4032, Kansas City, KS 66160
| | - Jennifer Buckley
- The University of Kansas Medical Center, 3901 Rainbow Boulevard, Mail Stop 4032, Kansas City, KS 66160
| | - Jacqueline D Hill
- The University of Kansas Medical Center, 3901 Rainbow Boulevard, Mail Stop 4032, Kansas City, KS 66160
| | - Jason B Gatewood
- The University of Kansas Medical Center, 3901 Rainbow Boulevard, Mail Stop 4032, Kansas City, KS 66160
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Clinical, mammographic, and ultrasonographic characteristics of diabetic mastopathy: A case series. Clin Imaging 2018; 53:204-209. [PMID: 30423508 DOI: 10.1016/j.clinimag.2018.11.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 10/25/2018] [Accepted: 11/02/2018] [Indexed: 02/07/2023]
Abstract
PURPOSE Diabetic mastopathy (DMP) is a rare benign breast lesion that mimics breast cancer on ultrasound. Our aims were to identify patient characteristics and imaging features of the disease. METHODS We conducted retrospective searches of our database for DMP lesions that were pathologically confirmed between January 2004 and November 2015. Mammographic and ultrasound features were reviewed by two experienced radiologists. RESULTS Twelve women were identified with 16 lesions. Most patients (83%) had type 2 diabetes mellitus (DM) and over half were insulin-dependent (58.3%), with a mean time of 16.9 years between the diagnosis of DM and that of DMP. There were negative findings on mammography for 46.7% of the lesions, including larger-sized lesions. Ultrasound revealed various features, including irregular shape (81.3%), indistinct margins (100%), parallel orientation to the chest wall (93.8%), marked hypoechogenicity (87.5%), and posterior shadowing (62.5%). CONCLUSIONS DMP was more common in patients with longstanding DM; in particular, type 2 DM and insulin-dependent patients. DMP lesions were usually occult on mammography, despite the relatively large size of DMP, which may help distinguish DMP from invasive cancer. Ultrasound detected several features that are also present in invasive cancer, making tissue sampling necessary to distinguish these.
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Xu Y, Pan B, Zhou YD, Yao R, Zhu QL, Zhang J, Mao F, Lin Y, Shen SJ, Sun Q. Mammography-detected ultrasound-negative asymptomatic micro-calcifications in Chinese women: Would it be safe to watch and wait? Med Hypotheses 2018; 118:9-12. [PMID: 30037622 DOI: 10.1016/j.mehy.2018.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 06/14/2018] [Indexed: 11/28/2022]
Abstract
Although mammography (MG) has been widely used for breast cancer screening in the western world, over-diagnosis remains controversial. Milestone studies showed that ultrasound (US) was an effective primary screening test for breast cancer both in the western world and in China. US improves the sensitivity of screening in Chinese women who have denser breasts and develop breast cancer earlier than Caucasian counterparts, and is used as the primary imaging test in the hospital-based opportunistic screening among asymptomatic self-referred women. Our previous work showed that US result might further differentiate the MG-detected breast cancers into low risk (US+) and ultra-low risk (US-). Indeed, most of the MG+/US- breast cancers would be ultra-low risk cancers and almost always present as MG micro-calcifications. Furthermore, majority of the commonest MG+/US- abnormal finding of micro-calcification is usually benign. Biopsy of benign breast disease increases not only the risk of breast cancer, but the expenses of screening and healthcare. Our hypothesis proposes that mammography-positive ultrasound-negative (MG+/US-) asymptomatic micro-calcifications might not need immediate invasive procedures and be safe to observe until the micro-calcifications increase significantly or become US-positive. If this hypothesis is proved, US would serve as the primary imaging test for breast cancer screening in China, with MG as the selective screening test and diagnostic tool for surgical plan. Unnecessary biopsy or surgery might be avoided with screening expenses considerably decrease.
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Affiliation(s)
- Ying Xu
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, PR China
| | - Bo Pan
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, PR China
| | - Yi-Dong Zhou
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, PR China
| | - Ru Yao
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, PR China
| | - Qing-Li Zhu
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730 PR China
| | - Jing Zhang
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730 PR China
| | - Feng Mao
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, PR China
| | - Yan Lin
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, PR China
| | - Song-Jie Shen
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, PR China
| | - Qiang Sun
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, PR China.
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Brennan SB, D'Alessio D, Kaplan J, Edelweiss M, Heerdt AS, Morris EA. Positive predictive value of biopsy of palpable masses following mastectomy. Breast J 2018; 24:789-797. [PMID: 30033648 DOI: 10.1111/tbj.13037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 09/01/2017] [Accepted: 09/13/2017] [Indexed: 01/02/2023]
Abstract
Determine the positive predictive value (PPV) of biopsy of palpable masses following mastectomy (MX). Determine if there are patient characteristics, tumor, or imaging features more predictive of cancer. IRB-approved retrospective review of 16 396 breast ultrasounds June 2008-December 2015 identified patients with MX presenting with palpable masses. Medical records and imaging studies were reviewed. Statistical analysis was performed using Fisher's exact test. 95% confidence intervals (CI) were calculated. In all, 117 patients presented with palpable masses on the MX side. 101/117 patients who had a palpable mass on physical examination had a true sonographic mass to correlate with the clinical findings. 91/101 (90%) underwent biopsy: 19/91 (21%, 95% CI; 13-31) biopsies were malignant. 72/91 (79%) were benign. All 19 cancers were on the original cancer side. Recurrences ranged from 0.4 to 4.5 cm maximum diameter, mean 1.3 cm. Prophylactic vs therapeutic mastectomy was very statistically significant (P = .01). The use of tamoxifen or an AI was also statistically significant (P = .04). Patient age (P = 1.0), radiation therapy (P = 1.05), chemotherapy (P = .2), immediate breast reconstruction (P = .2), or implant vs flap (P = .2) had no statistically significant association with finding cancer on biopsy. Lesion shape (irregular vs oval/round) was highly statistically significant (P = .0003) as was non-parallel orientation on ultrasound (P = .008). Circumscribed vs non-circumscribed margins was also statistically significant (P = .008). The PPV of biopsy of palpable masses on the side of MX was 21% (95% CI; 13-31). All recurrences were on the original cancer side and this was very statistically significant.
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Affiliation(s)
- Sandra B Brennan
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Donna D'Alessio
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jennifer Kaplan
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marcia Edelweiss
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alexandra S Heerdt
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Elizabeth A Morris
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Rebolj M, Assi V, Brentnall A, Parmar D, Duffy SW. Addition of ultrasound to mammography in the case of dense breast tissue: systematic review and meta-analysis. Br J Cancer 2018; 118:1559-1570. [PMID: 29736009 PMCID: PMC6008336 DOI: 10.1038/s41416-018-0080-3] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 03/07/2018] [Accepted: 03/19/2018] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Mammography is less effective in detecting cancer in dense than in fatty breasts. METHODS We undertook a systematic search in PubMed to identify studies on women with dense breasts who underwent screening with mammography supplemented with ultrasound. A meta-analysis was undertaken on the proportion of cancers detected only by ultrasound, out of all screen-detected cancers, and the proportion of women with negative mammography who were referred for assessment following ultrasound screening. RESULTS Twenty-nine studies satisfied our inclusion criteria. The proportion of total cancers detected only by ultrasound was 0.29 (95% CI: 0.27-0.31), consistent with an approximately 40% increase in the detection of cancers compared to mammography. In the studied populations, this translated into an additional 3.8 (95% CI: 3.4-4.2) screen-detected cases per 1000 mammography-negative women. About 13% (32/248) of cancers were in situ from 17 studies with information on this subgroup. Ultrasound approximately doubled the referral for assessment in three studies with these data. CONCLUSIONS Studies have consistently shown an increased detection of breast cancer by supplementary ultrasound screening. An inclusion of supplementary ultrasound into routine screening will need to consider the availability of ultrasound and diagnostic assessment capacities.
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Affiliation(s)
- Matejka Rebolj
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Valentina Assi
- Edinburgh Clinical Trials Unit, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Adam Brentnall
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Dharmishta Parmar
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Stephen W Duffy
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
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Sarigoz T, Ertan T, Topuz O, Sevim Y, Cihan Y. Role of digital infrared thermal imaging in the diagnosis of breast mass: A pilot study. INFRARED PHYSICS & TECHNOLOGY 2018; 91:214-219. [DOI: 10.1016/j.infrared.2018.04.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Weber B, Hayes J, Phil Evans W. Breast Density and the Importance of Supplemental Screening. CURRENT BREAST CANCER REPORTS 2018. [DOI: 10.1007/s12609-018-0275-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Menezes GLG, Pijnappel RM, Meeuwis C, Bisschops R, Veltman J, Lavin PT, van de Vijver MJ, Mann RM. Downgrading of Breast Masses Suspicious for Cancer by Using Optoacoustic Breast Imaging. Radiology 2018; 288:355-365. [PMID: 29664342 DOI: 10.1148/radiol.2018170500] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To assess the ability of optoacoustic (OA) ultrasonography (US) to help correctly downgrade benign masses classified as Breast Imaging Reporting and Data System (BI-RADS) 4a and 4b to BI-RADS 3 or 2. Materials and Methods OA/US technology uses laser light to detect relative amounts of oxygenated and deoxygenated hemoglobin in and around suspicious breast masses. In this prospective, multicenter study, results of 209 patients with 215 breast masses classified as BI-RADS 4a or 4b at US are reported. Patients were enrolled between 2015 and 2016. Masses were first evaluated with US with knowledge of previous clinical information and imaging results, and from this information a US imaging-based probability of malignancy (POM) and BI-RADS category were assigned to each mass. The same masses were then re-evaluated at OA/US. During the OA/US evaluation, radiologists scored five OA/US features, and then reassigned an OA/US-based POM and BI-RADS category for each mass. BI-RADS downgrade and upgrade percentages at OA/US were assessed by using a weighted sum of the five OA feature scores. Results At OA/US, 47.9% (57 of 119; 95% CI: 0.39, 0.57) of benign masses classified as BI-RADS 4a and 11.1% (three of 27; 95% CI: 0.03, 0.28) of masses classified as BI-RADS 4b were correctly downgraded to BI-RADS 3 or 2. Two of seven malignant masses classified as BI-RADS 4a at US were incorrectly downgraded, and one of 60 malignant masses classified as BI-RADS 4b at US was incorrectly downgraded for a total of 4.5% (three of 67; 95% CI: 0.01, 0.13) false-negative findings. Conclusion At OA/US, benign masses classified as BI-RADS 4a could be downgraded in BI-RADS category, which would potentially decrease biopsies negative for cancer and short-interval follow-up examinations, with the limitation that a few masses may be inappropriately downgraded.
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Affiliation(s)
- Gisela L G Menezes
- From the Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, E01.132, P.O. Box 85500, 3508, GA Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands (G.L.G.M., R.M.P.); Department of Radiology, Rijnstate Hospital, Arnhem, the Netherlands (C.M.); Department of Radiology, Albert Schweitzer Hospital, Dordrecht, the Netherlands (R.B.); Department of Radiology, Hospital Group Twente (ZGT), Almelo, the Netherlands (J.V.); Boston Biostatistics Research Foundation, Framingham, Mass (P.T.L.); Department of Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands (M.J.v.d.V.); and Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (R.M.M.)
| | - Ruud M Pijnappel
- From the Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, E01.132, P.O. Box 85500, 3508, GA Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands (G.L.G.M., R.M.P.); Department of Radiology, Rijnstate Hospital, Arnhem, the Netherlands (C.M.); Department of Radiology, Albert Schweitzer Hospital, Dordrecht, the Netherlands (R.B.); Department of Radiology, Hospital Group Twente (ZGT), Almelo, the Netherlands (J.V.); Boston Biostatistics Research Foundation, Framingham, Mass (P.T.L.); Department of Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands (M.J.v.d.V.); and Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (R.M.M.)
| | - Carla Meeuwis
- From the Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, E01.132, P.O. Box 85500, 3508, GA Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands (G.L.G.M., R.M.P.); Department of Radiology, Rijnstate Hospital, Arnhem, the Netherlands (C.M.); Department of Radiology, Albert Schweitzer Hospital, Dordrecht, the Netherlands (R.B.); Department of Radiology, Hospital Group Twente (ZGT), Almelo, the Netherlands (J.V.); Boston Biostatistics Research Foundation, Framingham, Mass (P.T.L.); Department of Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands (M.J.v.d.V.); and Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (R.M.M.)
| | - Robertus Bisschops
- From the Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, E01.132, P.O. Box 85500, 3508, GA Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands (G.L.G.M., R.M.P.); Department of Radiology, Rijnstate Hospital, Arnhem, the Netherlands (C.M.); Department of Radiology, Albert Schweitzer Hospital, Dordrecht, the Netherlands (R.B.); Department of Radiology, Hospital Group Twente (ZGT), Almelo, the Netherlands (J.V.); Boston Biostatistics Research Foundation, Framingham, Mass (P.T.L.); Department of Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands (M.J.v.d.V.); and Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (R.M.M.)
| | - Jeroen Veltman
- From the Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, E01.132, P.O. Box 85500, 3508, GA Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands (G.L.G.M., R.M.P.); Department of Radiology, Rijnstate Hospital, Arnhem, the Netherlands (C.M.); Department of Radiology, Albert Schweitzer Hospital, Dordrecht, the Netherlands (R.B.); Department of Radiology, Hospital Group Twente (ZGT), Almelo, the Netherlands (J.V.); Boston Biostatistics Research Foundation, Framingham, Mass (P.T.L.); Department of Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands (M.J.v.d.V.); and Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (R.M.M.)
| | - Philip T Lavin
- From the Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, E01.132, P.O. Box 85500, 3508, GA Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands (G.L.G.M., R.M.P.); Department of Radiology, Rijnstate Hospital, Arnhem, the Netherlands (C.M.); Department of Radiology, Albert Schweitzer Hospital, Dordrecht, the Netherlands (R.B.); Department of Radiology, Hospital Group Twente (ZGT), Almelo, the Netherlands (J.V.); Boston Biostatistics Research Foundation, Framingham, Mass (P.T.L.); Department of Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands (M.J.v.d.V.); and Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (R.M.M.)
| | - Marc J van de Vijver
- From the Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, E01.132, P.O. Box 85500, 3508, GA Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands (G.L.G.M., R.M.P.); Department of Radiology, Rijnstate Hospital, Arnhem, the Netherlands (C.M.); Department of Radiology, Albert Schweitzer Hospital, Dordrecht, the Netherlands (R.B.); Department of Radiology, Hospital Group Twente (ZGT), Almelo, the Netherlands (J.V.); Boston Biostatistics Research Foundation, Framingham, Mass (P.T.L.); Department of Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands (M.J.v.d.V.); and Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (R.M.M.)
| | - Ritse M Mann
- From the Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, E01.132, P.O. Box 85500, 3508, GA Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands (G.L.G.M., R.M.P.); Department of Radiology, Rijnstate Hospital, Arnhem, the Netherlands (C.M.); Department of Radiology, Albert Schweitzer Hospital, Dordrecht, the Netherlands (R.B.); Department of Radiology, Hospital Group Twente (ZGT), Almelo, the Netherlands (J.V.); Boston Biostatistics Research Foundation, Framingham, Mass (P.T.L.); Department of Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands (M.J.v.d.V.); and Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands (R.M.M.)
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Combined screening with mammography and ultrasound in a population-based screening program. Eur J Radiol 2018; 101:24-29. [DOI: 10.1016/j.ejrad.2018.01.022] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Revised: 01/20/2018] [Accepted: 01/22/2018] [Indexed: 11/20/2022]
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Pan B, Yao R, Zhu QL, Wang CJ, You SS, Zhang J, Xu QQ, Cai F, Shi J, Zhou YD, Mao F, Lin Y, Guan JH, Shen SJ, Liang ZY, Jiang YX, Sun Q. Clinicopathological characteristics and long-term prognosis of screening detected non-palpable breast cancer by ultrasound in hospital-based Chinese population (2001-2014). Oncotarget 2018; 7:76840-76851. [PMID: 27689334 PMCID: PMC5363553 DOI: 10.18632/oncotarget.12319] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 09/12/2016] [Indexed: 01/08/2023] Open
Abstract
Purpose The mainstay modality of breast cancer screening in China is the hospital-based opportunistic screening among asymptomatic self-referred women. There is little data about the ultrasound (US) detected non-palpable breast cancer (NPBC) in Chinese population. Methods We analyzed 699 consecutive NPBC from 1.8-2.3 million asymptomatic women from 2001 to 2014, including 572 US-detected NPBC from 3,786 US-positive women and 127 mammography (MG) detected NPBC from 788 MG-positive women. The clinicopathological features, disease-free survival (DFS) and overall survival (OS) were compared between the US- and MG-detected NPBC. Prognostic factors of NPBC were identified. Results Compared to MG, US could detect more invasive NPBC (83.6% vs 54.3%, p<0.001), lymph node positive NPBC (19.1% vs 10.2%, p=0.018), lower grade (24.8% vs 16.5%, p<0.001), multifocal (19.2% vs 6.3%, p<0.001), PR positive (71.4% vs 66.9%, p=0.041), Her2 negative (74.3% vs 54.3%, p<0.001), Ki67 high (defined as >14%, 46.3% vs 37.0%, p=0.031) cancers and more NPBC who received chemotherapy (40.7% vs 21.3%, p<0.001). There was no significant difference in 10-year DFS and OS between US-detected vs MG-detected NPBC, DCIS and invasive NPBC. For all NPBC and the US-detected NPBC, the common DFS-predictors included pT, pN, p53 and bilateral cancers. Conclusion US could detect more invasive, node-positive, multifocal NPBC in hospital-based asymptomatic Chinese female, who could achieve comparable 10-year DFS and OS as MG-detected NPBC. US would not delay early detection of NPBC with improved cost-effectiveness, thus could serve as the feasible initial imaging modality in hospital-based opportunistic screening among Chinese women.
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Affiliation(s)
- Bo Pan
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Ru Yao
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Qing-Li Zhu
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Chang-Jun Wang
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Shan-Shan You
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Jing Zhang
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Qian-Qian Xu
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Feng Cai
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Jie Shi
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Yi-Dong Zhou
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Feng Mao
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Yan Lin
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Jing-Hong Guan
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Song-Jie Shen
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Zhi-Yong Liang
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Yu-Xin Jiang
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
| | - Qiang Sun
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P. R. China
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Geisel J, Raghu M, Hooley R. The Role of Ultrasound in Breast Cancer Screening: The Case for and Against Ultrasound. Semin Ultrasound CT MR 2018; 39:25-34. [DOI: 10.1053/j.sult.2017.09.006] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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50
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Brancato B, Bonardi R, Catarzi S, Iacconi C, Risso G, Taschini R, Ciatto S. Negligible Advantages and Excess Costs of Routine Addition of Breast Ultrasonography to Mammography in Dense Breasts. TUMORI JOURNAL 2018; 93:562-6. [DOI: 10.1177/030089160709300608] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Aim To assess the role of breast ultrasonography as a complement to negative mammography in radiologically dense breasts. Material and methods Out of a total series of 49,044 consecutive mammograms reported as negative in asymptomatic women, 25,665 (52.3%) were coded as dense (BI-RADS D3–4) and ultrasonography was recommended. Due to organizational problems, ultrasonography was performed immediately or within 1 month only in 5,227 cases, representing the study series. Results Two cancers were detected at immediate ultrasonography (0.03%). The cancer detection rate in women aged 40–49 and 50–69 years was 0.002% and 0.07%, respectively. The benign biopsy rate was 0.5% for core biopsies and 0.02% for surgical biopsies. The cost per ultrasonography-assessed woman was € 56.05, whereas the cost per additional mammographically occult but ultrasonography-detected cancer was € 146,496.53. The mammograms of the 2 cancer cases underwent blind review by an expert reader and were confirmed as negative. Discussion Our findings show a low cancer detection rate, substantially lower compared to other clinical studies of ultrasonography in dense breasts, though in accordance with preliminary evidence from an Italian randomized clinical trial within a population-based screening program. The policy of adding ultrasonography to negative mammography in dense breasts seems to have very limited cost-effectiveness, and should not be adopted in routine practice before results of ongoing clinical trials are available.
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Affiliation(s)
| | - Rita Bonardi
- Centro per lo Studio e la Prevenzione Oncologica, Florence, Italy
| | - Sandra Catarzi
- Centro per lo Studio e la Prevenzione Oncologica, Florence, Italy
| | - Chiara Iacconi
- Centro per lo Studio e la Prevenzione Oncologica, Florence, Italy
| | - Gabriella Risso
- Centro per lo Studio e la Prevenzione Oncologica, Florence, Italy
| | - Renzo Taschini
- Centro per lo Studio e la Prevenzione Oncologica, Florence, Italy
| | - Stefano Ciatto
- Centro per lo Studio e la Prevenzione Oncologica, Florence, Italy
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