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Gatta G, Somma F, Sardu C, De Chiara M, Massafra R, Fanizzi A, La Forgia D, Cuccurullo V, Iovino F, Clemente A, Marfella R, Grezia GD. Automated 3D Ultrasound as an Adjunct to Screening Mammography Programs in Dense Breast: Literature Review and Metanalysis. J Pers Med 2023; 13:1683. [PMID: 38138910 PMCID: PMC10744838 DOI: 10.3390/jpm13121683] [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: 09/13/2023] [Revised: 11/10/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Purpose: The purpose of this meta-analysis is to investigate the effectiveness of supplementing screening mammography with three-dimensional automated breast ultrasonography (3D ABUS) in improving breast cancer detection rates in asymptomatic women with dense breasts. Materials and Methods: We conducted a thorough review of scientific publications comparing 3D ABUS and mammography. Articles for inclusion were sourced from peer-reviewed journal databases, namely MEDLINE (PubMed) and Scopus, based on an initial screening of their titles and abstracts. To ensure a sufficient sample size for meaningful analysis, only studies evaluating a minimum of 20 patients were retained. Eligibility for evaluation was further limited to articles written in English. Additionally, selected studies were required to have participants aged 18 or above at the time of the study. We analyzed 25 studies published between 2000 and 2021, which included a total of 31,549 women with dense breasts. Among these women, 229 underwent mammography alone, while 347 underwent mammography in combination with 3D ABUS. The average age of the women was 50.86 years (±10 years standard deviation), with a range of 40-56 years. In our efforts to address and reduce bias, we applied a range of statistical analyses. These included assessing study variation through heterogeneity assessment, accounting for potential study variability using a random-effects model, exploring sources of bias via meta-regression analysis, and checking for publication bias through funnel plots and the Egger test. These methods ensured the reliability of our study findings. Results: According to the 25 studies included in this metanalysis, out of the total number of women, 27,495 were diagnosed with breast cancer. Of these, 211 were diagnosed through mammography alone, while an additional 329 women were diagnosed through the combination of full-field digital mammography (FFDSM) and 3D ABUS. This represents an increase of 51.5%. The rate of cancers detected per 1000 women screened was 23.25‱ (95% confidence interval [CI]: 21.20, 25.60; p < 0.001) with mammography alone. In contrast, the addition of 3D ABUS to mammography increased the number of tumors detected to 20.95‱ (95% confidence interval [CI]: 18.50, 23; p < 0.001) per 1000 women screened. Discussion: Even though variability in study results, lack of long-term outcomes, and selection bias may be present, this systematic review and meta-analysis confirms that supplementing mammography with 3D ABUS increases the accuracy of breast cancer detection in women with ACR3 to ACR4 breasts. Our findings suggest that the combination of mammography and 3D ABUS should be considered for screening women with dense breasts. Conclusions: Our research confirms that adding 3D automated breast ultrasound to mammography-only screening in patients with dense breasts (ACR3 and ACR4) significantly (p < 0.05) increases the cancer detection rate.
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Affiliation(s)
- Gianluca Gatta
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (M.D.C.); (A.C.)
| | - Francesco Somma
- U.O.C. Neurodiology, ASL Center Naples 1, P.O. Ospedale del Mare, 80147 Naples, Italy;
| | - Celestino Sardu
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, Piazza Luigi Miraglia 2, 80138 Naples, Italy; (C.S.); (R.M.)
| | - Marco De Chiara
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (M.D.C.); (A.C.)
| | - Raffaella Massafra
- Department of Breast Radiology, Giovanni Paolo II/I.R.C.C.S. Cancer Institute, 70124 Bari, Italy; (R.M.); (A.F.); (D.L.F.)
| | - Annarita Fanizzi
- Department of Breast Radiology, Giovanni Paolo II/I.R.C.C.S. Cancer Institute, 70124 Bari, Italy; (R.M.); (A.F.); (D.L.F.)
| | - Daniele La Forgia
- Department of Breast Radiology, Giovanni Paolo II/I.R.C.C.S. Cancer Institute, 70124 Bari, Italy; (R.M.); (A.F.); (D.L.F.)
| | - Vincenzo Cuccurullo
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (M.D.C.); (A.C.)
| | - Francesco Iovino
- Department of Translational Medical Science, School of Medicine, University of Campania Luigi Vanvitelli, 80138 Naples, Italy;
| | - Alfredo Clemente
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (M.D.C.); (A.C.)
| | - Raffaele Marfella
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, Piazza Luigi Miraglia 2, 80138 Naples, Italy; (C.S.); (R.M.)
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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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Repeat Breast Ultrasound Demonstrates Utility with Added Cancer Detection in Patients following Breast Imaging Second Opinion Recommendations. Breast J 2022; 2022:1561455. [PMID: 35711880 PMCID: PMC9187284 DOI: 10.1155/2022/1561455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/19/2021] [Accepted: 11/24/2021] [Indexed: 11/17/2022]
Abstract
Purpose Second opinion consultation for patients with suspicious findings on breast imaging and patients with known breast cancer is not uncommon. We sought to determine the frequency of second opinion breast and axillary ultrasound imaging review and the subsequent impact on clinical management. Materials and Methods An IRB-approved retrospective chart review was conducted on 400 consecutive patients with second opinion radiology interpretations performed by subspecialized breast radiologists at a designated cancer center, including mammogram and ultrasound review. The outside institution imaging reports were compared with second opinion reports to categorize ultrasound review discrepancies which were defined as any BI-RADS category change. The discrepancy frequency, relevant alterations in patient management, and added cancer detection were measured. Results The second opinion imaging review resulted in discrepant findings in 108/400 patients (27%). Patients with heterogeneously or extremely dense breasts had higher discrepancy frequency (36% discrepancy, 68/187) than those with almost entirely fatty or scattered fibroglandular breast tissue (19% discrepancy, 40/213) with P = 0.0001. Discrepancies resulted in the following changes in impression/recommendations: 70 repeat ultrasounds for better characterization of a breast lesion, 11 repeat ultrasounds of a negative region, 20 repeat ultrasounds for benign axillary lymph nodes, 5 downgrades from probably benign to benign, and 2 upgrades from benign to suspicious. Repeat ultrasounds of the axilla in 19 patients resulted in 13 biopsy recommendations, and 4 were metastatic (PPV3 31%). In the breast, repeat ultrasounds in 81 patients resulted in 14 upgrades to suspicious. Of these, 5 yielded malignancy. In addition, one patient was upgraded from benign to suspicious based on the outside image, with pathology revealing malignancy (breast PPV3 40%). Breast lesion BI-RADS category downgrades in 27 patients resulted in 10 avoided biopsies. Ultimately, second opinion ultrasound review resulted in altered management in 12% of patients (47/400). This included discovery of additional breast malignancies in 6 patients, metastatic lymph nodes in 4 patients, excisional biopsy for atypia in 1 patient, 4 patients proceeding to mastectomy, 10 patients who avoided biopsies, and 22 patients who avoided follow-up of benign findings. Conclusions In this study, subspecialized second opinion ultrasound review had an impact on preventing unnecessary procedures and follow-up exams in 8% of patients while detecting additional cancer in 2.5%.
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Does automated breast ultrasound (ABUS) add to breast tomosynthesis (DBT) in assessment of lesions in dense breasts? THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [DOI: 10.1186/s43055-021-00556-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
As mammography has its known limitations in dense breast, additional imaging is usually needed. We aimed to evaluate the role of automated breast ultrasound in addition to tomosynthesis in detection and diagnosis of breast lesions in dense breasts. Seventy patients with dense breasts subjected to full-field digital mammography (FFDM) including digital breast tomosynthesis (DBT) and automated breast ultrasound (ABUS). Both studies were evaluated by two experienced radiologists to assess breast composition, mass characterization, asymmetry, calcification, axillary lymphadenopathy, extent of disease (EOD), skin thickening, retraction, architectural distortion, and BIRADS classification. All breast masses were interpreted as above described and then correlated with final pathological diagnosis.
Results
Study included 70 females presenting with different types of breast lesions. Eighty-two masses were detected: 53 benign (n = 53/82), 29 malignant (n = 29/82). Histopathology of the masses was reached by core biopsy (n = 30), FNAC (n = 14), and excisional biopsy (n = 11). The rest of the masses (n = 27/82) were confirmed by their characteristic sonographic appearances; 20 cases of multiple bilateral anechoic simple cysts, 7 typical fibroadenomas showed stationary course on follow-up. As regards the final BIRADS score given for both modalities, tomosynthesis showed accuracy of 93.1% in characterization of malignant masses with accuracy of 94.3% in benign masses, on the other hand automated ultrasound showed 100% accuracy in characterization of malignant masses with 98.1% accuracy in benign masses.
Conclusion
Adding ABUS to tomosynthesis has proven a valuable imaging tool for characterization of breast lesions in dense breasts both as screening and diagnostic tool. They proved to be more sensitive and specific than digital mammography alone in showing tissue overlap, tumor characterization, lesion margins, extent, and multiplicity of malignant lesions.
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Tomosynthesis in pediatrics: a retrospective of its application in the world practice and own data. КЛИНИЧЕСКАЯ ПРАКТИКА 2021. [DOI: 10.17816/clinpract77802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Tomosynthesis is a modern effective and informative method of low-dose X-ray diagnostics, which allows obtaining a significant number of layered images with the subsequent volumetric image reconstruction. The use of tomosynthesis provides a one-time examination of a large anatomical area without loss of the image quality and diagnostics of difficult-to-visualize pathological changes that are not detected by digital radiography. The article presents an overview of the problem of improving low-dose imaging options in the radiation diagnostics, as well as the authors own data on the use of tomosynthesis for the diagnosis of community-acquired pneumonia in children.
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Ultrasound Image Features under Deep Learning in Breast Conservation Surgery for Breast Cancer. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6318936. [PMID: 34567484 PMCID: PMC8463209 DOI: 10.1155/2021/6318936] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/28/2021] [Accepted: 08/31/2021] [Indexed: 12/15/2022]
Abstract
This study was to analyze the effect of the combined application of deep learning technology and ultrasound imaging on the effect of breast-conserving surgery for breast cancer. A deep label distribution learning (LDL) model was designed, and the semiautomatic segmentation algorithm based on the region growing and active contour technology (RA) and the segmentation model based on optimized nearest neighbors (ON) were introduced for comparison. The designed algorithm was applied to the breast-conserving surgery of breast cancer patients. According to the difference in intraoperative guidance methods, 102 female patients with early breast cancer were divided into three groups: 34 cases in W1 group (ultrasound guidance based on deep learning segmentation model), 34 cases in W2 group (ultrasound guidance), and 34 cases in W3 group (palpation guidance). The results revealed that the tumor area segmented by the LDL algorithm constructed in this study was closer to the real tumor area; the segmentation accuracy (AC), Jaccard, and true-positive (TP) values of the LDL algorithm were obviously greater than those of the RA and ON algorithms, while the false-positive (FP) value was significantly lower in contrast to the RA and ON algorithms, showing statistically observable differences (P < 0.05); the actual resection volume of the patients in the W1 group was the closest to the ideal resection volume, which was much smaller in contrast to that of the patients in the W2 and W3 groups, showing statistical differences (P < 0.05); the positive margins of the patients in the W1 group were statistically lower than those in the W2 and W3 groups (P < 0.05). In addition, 1 patient in the W1 group was not satisfied with the cosmetic effect, 3 patients in the W2 group were not satisfied with the cosmetic effect, and 9 patients in the W3 group were not satisfied with the cosmetic effect. Finally, it was found that the ultrasound image based on the deep LDL model effectively improved the AC of tumor resection and negative margins, reduced the probability of normal tissue being removed, and improved the postoperative cosmetic effect of breast.
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Pros and Cons for Automated Breast Ultrasound (ABUS): A Narrative Review. J Pers Med 2021; 11:jpm11080703. [PMID: 34442347 PMCID: PMC8400952 DOI: 10.3390/jpm11080703] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/21/2021] [Accepted: 07/22/2021] [Indexed: 12/15/2022] Open
Abstract
Automated breast ultrasound (ABUS) is an ultrasound technique that tends to be increasingly used as a supplementary technique in the evaluation of patients with dense glandular breasts. Patients with dense breasts have an increased risk of developing breast cancer compared to patients with fatty breasts. Furthermore, for this group of patients, mammography has a low sensitivity in detecting breast cancers, especially if it is not associated with architectural distortion or calcifications. ABUS is a standardized examination with many advantages in both screening and diagnostic settings: it increases the detection rate of breast cancer, improves the workflow, and reduces the examination time. On the other hand, like any imaging technique, ABUS has disadvantages and even some limitations. Many disadvantages can be diminished by additional attention and training. Disadvantages regarding image acquisition are the inability to assess the axilla, the vascularization, and the elasticity of a lesion, while concerning the interpretation, the disadvantages are the artifacts due to poor positioning, lack of contact, motion or lesion related. This article reviews and discusses the indications, the advantages, and disadvantages of the method and also the sources of error in the ABUS examination.
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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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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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Lai X, Yang W, Li R. DBT Masses Automatic Segmentation Using U-Net Neural Networks. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:7156165. [PMID: 32411285 PMCID: PMC7204342 DOI: 10.1155/2020/7156165] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 12/17/2019] [Accepted: 12/18/2019] [Indexed: 12/02/2022]
Abstract
To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Firstly, to suppress the background tissue noise and enhance the contrast of the mass candidate regions, after the top-hat transform of DBT images, a constraint matrix is constructed and multiplied with the DBT image. Secondly, an efficient U-Net neural network is built and image patches are extracted before data augmentation to establish the training dataset to train the U-Net model. And then the presegmentation of the DBT tumors is implemented, which initially classifies per pixel into two different types of labels. Finally, all regions smaller than 50 voxels considered as false positives are removed, and the median filter smoothes the mass boundaries to obtain the final segmentation results. The proposed method can effectively improve the performance in the automatic segmentation of the masses in DBT images. Using the detection Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), and area under the curve (AUC) as evaluation indexes, the Acc, Sen, Spe, and AUC for DBT mass segmentation in the entire experimental dataset is 0.871, 0.869, 0.882, and 0.859, respectively. Our proposed U-Net-based DBT mass automatic segmentation system obtains promising results, which is superior to some classical architectures, and may be expected to have clinical application prospects.
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Affiliation(s)
- Xiaobo Lai
- College of Medical Technology, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Weiji Yang
- College of Life Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Ruipeng Li
- Hangzhou Third People's Hospital, Hangzhou 310009, China
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Fleury E, Marcomini K. Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images. Eur Radiol Exp 2019; 3:34. [PMID: 31385114 PMCID: PMC6682836 DOI: 10.1186/s41747-019-0112-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 07/02/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The purpose of this work was to evaluate computable Breast Imaging Reporting and Data System (BI-RADS) radiomic features to classify breast masses on ultrasound B-mode images. METHODS The database consisted of 206 consecutive lesions (144 benign and 62 malignant) proved by percutaneous biopsy in a prospective study approved by the local ethical committee. A radiologist manually delineated the contour of the lesions on greyscale images. We extracted the main ten radiomic features based on the BI-RADS lexicon and classified the lesions as benign or malignant using a bottom-up approach for five machine learning (ML) methods: multilayer perceptron (MLP), decision tree (DT), linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). We performed a 10-fold cross validation for training and testing of all classifiers. Receiver operating characteristic (ROC) analysis was used for providing the area under the curve with 95% confidence intervals (CI). RESULTS The classifier with the highest AUC at ROC analysis was SVM (AUC = 0.840, 95% CI 0.6667-0.9762), with 71.4% sensitivity (95% CI 0.6479-0.8616) and 76.9% specificity (95% CI 0.6148-0.8228). The best AUC for each method was 0.744 (95% CI 0.677-0.774) for DT, 0.818 (95% CI 0.6667-0.9444) for LDA, 0.811 (95% CI 0.710-0.892) for RF, and 0.806 (95% CI 0.677-0.839) for MLP. Lesion margin and orientation were the optimal features for all the machine learning methods. CONCLUSIONS ML can aid the distinction between benign and malignant breast lesion on ultrasound images using quantified BI-RADS descriptors. SVM provided the highest ROC-AUC (0.840).
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Affiliation(s)
- Eduardo Fleury
- Instituto Brasileiro de Controle do Câncer (IBCC), São Paulo, Brazil. .,Centro Universitário São Camilo, Curso de Medicina, São Paulo, Brazil.
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