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  1. Ibrahim R, Rahmat K, Fadzli F, Rozalli FI, Westerhout CJ, Alli K, et al.
    Singapore Med J, 2016 Jan 6.
    PMID: 26767894 DOI: 10.11622/smedj.2016001
    This study aimed to evaluate the vascular pattern of solid breast lesions using power Doppler ultrasound (PDUS) and to assess whether the presence of intratumoural penetrating vessels can be used to predict breast cancer malignancy.
    Matched MeSH terms: Ultrasonography, Mammary
  2. Salim MI, Supriyanto E, Haueisen J, Ariffin I, Ahmad AH, Rosidi B
    Med Biol Eng Comput, 2013 Apr;51(4):459-66.
    PMID: 23238828 DOI: 10.1007/s11517-012-1014-5
    This paper proposes a novel hybrid magnetoacoustic measurement (HMM) system aiming at breast cancer detection. HMM combines ultrasound and magnetism for the simultaneous assessment of bioelectric and acoustic profiles of breast tissue. HMM is demonstrated on breast tissue samples, which are exposed to 9.8 MHz ultrasound wave with the presence of a 0.25 Tesla static magnetic field. The interaction between the ultrasound wave and the magnetic field in the breast tissue results in Lorentz Force that produces a magnetoacoustic voltage output, proportional to breast tissue conductivity. Simultaneously, the ultrasound wave is sensed back by the ultrasound receiver for tissue acoustic evaluation. Experiments are performed on gel phantoms and real breast tissue samples harvested from laboratory mice. Ultrasound wave characterization results show that normal breast tissue experiences higher attenuation compared with cancerous tissue. The mean magnetoacoustic voltage results for normal tissue are lower than that for the cancerous tissue group. In conclusion, the combination of acoustic and bioelectric measurements is a promising approach for breast cancer diagnosis.
    Matched MeSH terms: Ultrasonography, Mammary/methods*
  3. Ng WL, Rahmat K, Fadzli F, Rozalli FI, Mohd-Shah MN, Chandran PA, et al.
    Medicine (Baltimore), 2016 Mar;95(12):e3146.
    PMID: 27015196 DOI: 10.1097/MD.0000000000003146
    The purpose of this study was to investigate the diagnostic efficacy of shearwave elastography (SWE) in differentiating between benign and malignant breast lesions.One hundred and fifty-nine lesions were assessed using B-mode ultrasound (US) and SWE parameters were recorded (Emax, Emean, Emin, Eratio, SD). SWE measurements were then correlated with histopathological diagnosis.The final sample contained 85 benign and 74 malignant lesions. The maximum stiffness (Emax) with a cutoff point of ≥ 56.0 kPa (based on ROC curves) provided sensitivity of 100.0%, specificity of 97.6%, positive predictive value (PPV) of 97.4%, and negative predictive value (NPV) of 100% in detecting malignant lesions. A cutoff of ≥80 kPa managed to downgrade 95.5% of the Breast Imaging-Reporting and Data System (BI-RADS) 4a lesions to BI-RADS 3, negating the need for biopsy. Using a combination of BI-RADS and SWE, the authors managed to improve the PPV from 2.3% to 50% in BI-RADS 4a lesions.SWE of the breast provides highly specific and sensitive quantitative values that are beneficial in the characterization of breast lesions. Our results showed that Emax is the most accurate value for differentiating benign from malignant lesions.
    Matched MeSH terms: Ultrasonography, Mammary
  4. Liu H, Tan T, van Zelst J, Mann R, Karssemeijer N, Platel B
    J Med Imaging (Bellingham), 2014 Jul;1(2):024501.
    PMID: 26158036 DOI: 10.1117/1.JMI.1.2.024501
    We investigated the benefits of incorporating texture features into an existing computer-aided diagnosis (CAD) system for classifying benign and malignant lesions in automated three-dimensional breast ultrasound images. The existing system takes into account 11 different features, describing different lesion properties; however, it does not include texture features. In this work, we expand the system by including texture features based on local binary patterns, gray level co-occurrence matrices, and Gabor filters computed from each lesion to be diagnosed. To deal with the resulting large number of features, we proposed a combination of feature-oriented classifiers combining each group of texture features into a single likelihood, resulting in three additional features used for the final classification. The classification was performed using support vector machine classifiers, and the evaluation was done with 10-fold cross validation on a dataset containing 424 lesions (239 benign and 185 malignant lesions). We compared the classification performance of the CAD system with and without texture features. The area under the receiver operating characteristic curve increased from 0.90 to 0.91 after adding texture features ([Formula: see text]).
    Matched MeSH terms: Ultrasonography, Mammary
  5. Pang T, Wong JHD, Ng WL, Chan CS
    Comput Methods Programs Biomed, 2021 May;203:106018.
    PMID: 33714900 DOI: 10.1016/j.cmpb.2021.106018
    BACKGROUND AND OBJECTIVE: The capability of deep learning radiomics (DLR) to extract high-level medical imaging features has promoted the use of computer-aided diagnosis of breast mass detected on ultrasound. Recently, generative adversarial network (GAN) has aided in tackling a general issue in DLR, i.e., obtaining a sufficient number of medical images. However, GAN methods require a pair of input and labeled images, which require an exhaustive human annotation process that is very time-consuming. The aim of this paper is to develop a radiomics model based on a semi-supervised GAN method to perform data augmentation in breast ultrasound images.

    METHODS: A total of 1447 ultrasound images, including 767 benign masses and 680 malignant masses were acquired from a tertiary hospital. A semi-supervised GAN model was developed to augment the breast ultrasound images. The synthesized images were subsequently used to classify breast masses using a convolutional neural network (CNN). The model was validated using a 5-fold cross-validation method.

    RESULTS: The proposed GAN architecture generated high-quality breast ultrasound images, verified by two experienced radiologists. The improved performance of semi-supervised learning increased the quality of the synthetic data produced in comparison to the baseline method. We achieved more accurate breast mass classification results (accuracy 90.41%, sensitivity 87.94%, specificity 85.86%) with our synthetic data augmentation compared to other state-of-the-art methods.

    CONCLUSION: The proposed radiomics model has demonstrated a promising potential to synthesize and classify breast masses on ultrasound in a semi-supervised manner.

    Matched MeSH terms: Ultrasonography, Mammary
  6. Jalalian A, Mashohor SB, Mahmud HR, Saripan MI, Ramli AR, Karasfi B
    Clin Imaging, 2013 May-Jun;37(3):420-6.
    PMID: 23153689 DOI: 10.1016/j.clinimag.2012.09.024
    Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information. Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed.
    Matched MeSH terms: Ultrasonography, Mammary/methods*
  7. Jalalian A, Mashohor S, Mahmud R, Karasfi B, Saripan MIB, Ramli ARB
    EXCLI J, 2017;16:113-137.
    PMID: 28435432 DOI: 10.17179/excli2016-701
    Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists' performance in finding and discriminating between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detection on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed.
    Matched MeSH terms: Ultrasonography, Mammary
  8. Cheah SD, Imi Sairi AH
    BMJ Case Rep, 2020 Nov 04;13(11).
    PMID: 33148555 DOI: 10.1136/bcr-2020-236818
    A 32-year-old woman presented with a 3 cm×3 cm left breast lump associated with bloody nipple discharge during her early pregnancy. Examination and ultrasonography showed benign features, whereas core needle biopsies revealed a benign papilloma. Six months after her delivery, a 6 cm×6 cm benign papilloma was completely excised via circumareolar incision. The majority of intraductal papillomas are small; however, they can also present as a large mass rarely. We should be wary of a malignant papillary lesion when there is the presence of atypia on core needle biopsy or imaging-histology discordance. A concordant benign papilloma with benign imaging findings is otherwise reassuring. Clinicians need to be aware of this uncommon presentation of large intraductal papilloma as a complete curative excision can be achieved through a cosmetically placed incision.
    Matched MeSH terms: Ultrasonography, Mammary/methods
  9. Dinesh BJ, Hayati F, Azizan N, Abdul Rashid NF
    BMJ Case Rep, 2019 Sep 18;12(9).
    PMID: 31537599 DOI: 10.1136/bcr-2019-231516
    Florid papillomatosis (FP) of the nipple, or nipple adenoma, is a rare breast tumour, affecting middle-aged group population. A 46-year-old woman presented to us with a cauliflower-like FP of the right nipple with no blood stained discharge or breast lump. FP can be mistaken clinically for Paget's disease and occasionally misinterpreted as invasive ductal or intraductal carcinoma. Extensive intervention, correct diagnosis and prompt treatment are essential. Any breast pathology requires triple assessment including FP of the nipple. Once the diagnosis of ductal carcinoma is excluded, simple complete excision can be undertaken. This is to ensure complete obliteration of disease recurrence and preservation of cosmetic result. We discuss the pathology and psychosocial aspects of FP.
    Matched MeSH terms: Ultrasonography, Mammary/methods
  10. Tan SL, Rahmat K, Rozalli FI, Mohd-Shah MN, Aziz YF, Yip CH, et al.
    Clin Radiol, 2014 Jan;69(1):63-71.
    PMID: 24156797 DOI: 10.1016/j.crad.2013.08.007
    To investigate the capability and diagnostic accuracy of diffusion-weighted imaging (DWI) in differentiating benign from malignant breast lesions using 3 T magnetic resonance imaging (MRI).
    Matched MeSH terms: Ultrasonography, Mammary
  11. Elagili F, Abdullah N, Fong L, Pei T
    Asian J Surg, 2007 Jan;30(1):40-4.
    PMID: 17337370
    To assess ultrasonographically (US) guided needle aspiration of breast abscesses as an alternative to surgical incision and drainage.
    Matched MeSH terms: Ultrasonography, Mammary
  12. Hanna RM, Ashebu SD
    Australas Radiol, 2002 Sep;46(3):252-6.
    PMID: 12196231
    In a retrospective study of giant breast masses over a period of 20 years (1980-2000), we encountered 18 patients with fibroadenomas. Most of them were adolescents and young adults. The bimodal age incidence seen in Caucasians was not observed. The masses ranged from 6 to 15 cm in size and in the youngest patient, they were bilateral. All 18 patients were examined by mammography and 10 of them by ultrasonography (US) as well. The right breast was involved in 12 patients and the left in six. The diagnosis was confirmed pathologically in all patients, by excision biopsy in 17 patients and by fine needle aspiration cytology and excision biopsy in one patient. The radiological findings were the same as those previously described. All patients were treated by simple enuculation. There was only one recurrence over a follow-up period from 2 months to 3 years.
    Matched MeSH terms: Ultrasonography, Mammary
  13. Ngah, U.K., Aziz, S.A., Aziz, M.E., Murad, M., Mahdi, N.M.N., Shakaff, A.Y.M., et al.
    ASM Science Journal, 2008;2(1):1-11.
    MyJurnal
    The incidences of breast cancer have been rising at an alarming rate. Mass breast screening programmes involving mammography and ultrasound in certain parts of the world have also proven their benefits in early detection. However, radiologists may be confronted with increased workload. An attempt has been made in this paper to rectify part of the problems faced in this area. Expert systems based on the interpretation of mammographic and ultrasound images for classifying patient cases could be utilized by doctors (expert and non-expert) in screening. These softwares consist of MAMMEX (for mammogram) and SOUNDEX (for breast ultrasound) could be used to deduce cases according to Breast Imaging Recording and Data System (BI-RADS), based on patients’ history, physical and clinical assessment, mammograms and breast ultrasound images. A total of 179 retrospective cases from the Radiology Department, hospital of the University of Science Malaysia, Kubang Kerian, Kelantan were used in this study. A receiver operating characteristic (ROC) curve analysis was implemented, based on the usage of a two-class forced choice of classifying suspicious and malignant findings as positive with normal, benign and probably benign classified as negative. Results yielded an area under the curve (AUC) of 0.997 with the least standard error value of 0.003 for MAMMEX while an AUC of 0.996 with the least standard error of 0.004 was accomplished for SOUNDEX. A system which very closely simulated radiologists was also successfully developed in this study. The ROC curve analysis indicated that the expert systems developed were of high performance and reliability.
    Matched MeSH terms: Ultrasonography, Mammary
  14. Selvi V, Nori J, Meattini I, Francolini G, Morelli N, De Benedetto D, et al.
    Biomed Res Int, 2018;2018:1569060.
    PMID: 30046588 DOI: 10.1155/2018/1569060
    PURPOSE: The prevalence of invasive lobular carcinoma (ILC), the second most common type of breast cancer, accounts for 5%-15% of all invasive breast cancer cases. Its histological feature to spread in rows of single cell layers explains why it often fails to form a palpable lesion and the lack of sensitivity of mammography and ultrasound (US) to detect it. It also has a higher incidence of multifocal, multicentric, and contralateral disease when compared to the other histological subtypes. The clinicopathologic features and outcomes of Invasive Ductolobular Carcinoma (IDLC) are very similar to the ILC. The purpose of our study is to assess the importance of MRI in the preoperative management and staging of patients affected by ILC or IDLC.

    MATERIALS AND METHODS: We identified women diagnosed with ILC or IDLC. We selected the patients who had preoperative breast MRI. For each patient we identified the areas of multifocal, multicentric, or contralateral disease not visible to standard exams and detected by preoperative MRI. We analyzed the potential correlation between additional cancer areas and histological cancer markers.

    RESULTS: Of the 155 women who met our inclusion criteria, 93 (60%) had additional cancer areas detected by MRI. In 61 women, 39,4% of the overall population, the additional cancer areas were confirmed by US/tomosynthesis second look and biopsy. Presurgical MRI staging changed surgical management in the 37,4% of the patients. Only six patients of the overall population needed a reoperation after the initial surgery. No statistically significant correlation was found between MRI overestimation and the presence of histological peritumoral vascular/linfatic invasion. No statistically significant correlation was found between additional cancer areas and histological cancer markers.

    CONCLUSIONS: Our study suggests that MRI is an important tool in the preoperative management and staging of patients affected by lobular or ductolobular invasive carcinoma.

    Matched MeSH terms: Ultrasonography, Mammary
  15. Vairavan R, Abdullah O, Retnasamy PB, Sauli Z, Shahimin MM, Retnasamy V
    Curr Med Imaging Rev, 2019;15(2):85-121.
    PMID: 31975658 DOI: 10.2174/1573405613666170912115617
    BACKGROUND: Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

    DISCUSSION: This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

    CONCLUSION: This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

    Matched MeSH terms: Ultrasonography, Mammary/methods
  16. Majedah S, Alhabshi I, Salim S
    BMJ Case Rep, 2013;2013.
    PMID: 23417932 DOI: 10.1136/bcr-2012-007961
    Matched MeSH terms: Ultrasonography, Mammary
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