ABM Clinical Protocol #30: Breast Masses, Breast Complaints, and Diagnostic Breast Imaging in the Lactating Woman

Breastfeeding ◽  
2022 ◽  
pp. 1010-1016
Author(s):  
Katrina B. Mitchell ◽  
Helen M. Johnson ◽  
Anne Eglash
Keyword(s):  
Breast Imaging ◽  
Clinical Protocol ◽  
Breast Masses

ABM Clinical Protocol #30: Breast Masses, Breast Complaints, and Diagnostic Breast Imaging in the Lactating Woman

2019 ◽  
Vol 14 (4) ◽  
pp. 208-214 ◽  
Author(s):  
Katrina B. Mitchell ◽  
Helen M. Johnson ◽  
Anne Eglash ◽  
Michal Young ◽  
Larry Noble ◽  
...  
Keyword(s):  
Breast Imaging ◽  
Clinical Protocol ◽  
Breast Masses

scholarly journals Combination of shear wave elastography and BI-RADS in identification of solid breast masses

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xue Zheng ◽  
Fei Li ◽  
Zhi-Dong Xuan ◽  
Yu Wang ◽  
Lei Zhang
Keyword(s):  
Shear Wave ◽  
Breast Imaging ◽  
Color Doppler ◽  
Shear Wave Elastography ◽  
Definitive Diagnosis ◽  
Pathological Examination ◽  
Diagnostic Specificity ◽  
Breast Masses ◽  
Malignant Breast ◽  
Grade Ii

Abstract Background To explore the value of quantitative shear wave elastography (SWE) plus the Breast Imaging Reporting and Data System (BI-RADS) in the identification of solid breast masses. Methods A total of 108 patients with 120 solid breast masses admitted to our hospital from January 2019 to January 2020 were enrolled in this study. The pathological examination served as the gold standard for definitive diagnosis. Both SWE and BI-RADS grading were performed. Results Out of the 120 solid breast masses in 108 patients, 75 benign and 45 malignant masses were pathologically confirmed. The size, shape, margin, internal echo, microcalcification, lateral acoustic shadow, and posterior acoustic enhancement of benign and malignant masses were significantly different (all P < 0.05). The E mean, E max, SD, and E ratio of benign and malignant masses were significantly different (all P < 0.05). The E min was similar between benign and malignant masses (P > 0.05). The percentage of Adler grade II-III of the benign masses was lower than that of the malignant masses (P < 0.05). BI-RADS plus SWE yielded higher diagnostic specificity and positive predictive value than either BI-RADS or SWE; BI-RADS plus SWE yielded the highest diagnostic accuracy among the three methods (all P < 0.05). Conclusion SWE plus routine ultrasonography BI-RADS has a higher value in differentiating benign from malignant breast masses than color doppler or SWE alone, which should be further promoted in clinical practice.

scholarly journals A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Said Boumaraf ◽  
Xiabi Liu ◽  
Chokri Ferkous ◽  
Xiaohong Ma
Keyword(s):  
Feature Selection ◽  
Predictive Value ◽  
Breast Imaging ◽  
Feature Selection Method ◽  
Computer Aided Diagnosis ◽  
Screening Mammography ◽  
Breast Masses ◽  
Computer Aided ◽  
Aided Diagnosis

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient’s age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

scholarly journals Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 631
Author(s):  
Afaf F. Moustafa ◽  
Theodore W. Cary ◽  
Laith R. Sultan ◽  
Susan M. Schultz ◽  
Emily F. Conant ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Breast Imaging ◽  
Color Doppler ◽  
Color Doppler Ultrasound ◽  
Ensemble Classifier ◽  
Diagnostic Model ◽  
Ultrasound Images ◽  
Breast Masses ◽  
Roc Area

Color Doppler is used in the clinic for visually assessing the vascularity of breast masses on ultrasound, to aid in determining the likelihood of malignancy. In this study, quantitative color Doppler radiomics features were algorithmically extracted from breast sonograms for machine learning, producing a diagnostic model for breast cancer with higher performance than models based on grayscale and clinical category from the Breast Imaging Reporting and Data System for ultrasound (BI-RADSUS). Ultrasound images of 159 solid masses were analyzed. Algorithms extracted nine grayscale features and two color Doppler features. These features, along with patient age and BI-RADSUS category, were used to train an AdaBoost ensemble classifier. Though training on computer-extracted grayscale features and color Doppler features each significantly increased performance over that of models trained on clinical features, as measured by the area under the receiver operating characteristic (ROC) curve, training on both color Doppler and grayscale further increased the ROC area, from 0.925 ± 0.022 to 0.958 ± 0.013. Pruning low-confidence cases at 20% improved this to 0.986 ± 0.007 with 100% sensitivity, whereas 64% of the cases had to be pruned to reach this performance without color Doppler. Fewer borderline diagnoses and higher ROC performance were both achieved for diagnostic models of breast cancer on ultrasound by machine learning on color Doppler features.

Low-Grade Adenosquamous Carcinoma of the Breast: Imaging and Histopathologic Characteristics of this Rare Disease

2013 ◽  
Vol 64 (4) ◽  
pp. 339-344 ◽  
Author(s):  
Elena P. Scali ◽  
Rola H. Ali ◽  
Malcolm Hayes ◽  
Scott Tyldesley ◽  
Patricia Hassell
Keyword(s):  
Breast Carcinoma ◽  
Breast Imaging ◽  
Adenosquamous Carcinoma ◽  
Malignant Neoplasms ◽  
Low Grade ◽  
Imaging Features ◽  
Carcinoma Of The Breast ◽  
Histologic Subtype ◽  
Breast Masses ◽  
Sonographic Appearance

Low-grade adenosquamous carcinoma is a rare histologic subtype of breast carcinoma that has a variable mammographic and sonographic appearance, which overlaps with both benign and malignant neoplasms. Because of its lack of unique imaging features, a diagnosis of low-grade adenosquamous carcinoma is based on histopathology. The recognition of this entity is an important consideration in the differential diagnosis of breast masses and carries implications for prognosis, which is more favorable than other types of breast carcinoma.

scholarly journals Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Zilong He ◽  
Yue Li ◽  
Weixiong Zeng ◽  
Weimin Xu ◽  
Jialing Liu ◽  
...  
Keyword(s):  
Breast Imaging ◽  
Diagnostic Performance ◽  
Area Under The Curve ◽  
Breast Mass ◽  
Cad Model ◽  
Breast Masses ◽  
Radiology Reports ◽  
Computer Aided ◽  
Mass Lesions ◽  
The Impact

Radiologists’ diagnostic capabilities for breast mass lesions depend on their experience. Junior radiologists may underestimate or overestimate Breast Imaging Reporting and Data System (BI-RADS) categories of mass lesions owing to a lack of diagnostic experience. The computer-aided diagnosis (CAD) method assists in improving diagnostic performance by providing a breast mass classification reference to radiologists. This study aims to evaluate the impact of a CAD method based on perceptive features learned from quantitative BI-RADS descriptions on breast mass diagnosis performance. We conducted a retrospective multi-reader multi-case (MRMC) study to assess the perceptive feature-based CAD method. A total of 416 digital mammograms of patients with breast masses were obtained from 2014 through 2017, including 231 benign and 185 malignant masses, from which we randomly selected 214 cases (109 benign, 105 malignant) to train the CAD model for perceptive feature extraction and classification. The remaining 202 cases were enrolled as the test set for evaluation, of which 51 patients (29 benign and 22 malignant) participated in the MRMC study. In the MRMC study, we categorized six radiologists into three groups: junior, middle-senior, and senior. They diagnosed 51 patients with and without support from the CAD model. The BI-RADS category, benign or malignant diagnosis, malignancy probability, and diagnosis time during the two evaluation sessions were recorded. In the MRMC evaluation, the average area under the curve (AUC) of the six radiologists with CAD support was slightly higher than that without support (0.896 vs. 0.850, p = 0.0209). Both average sensitivity and specificity increased (p = 0.0253). Under CAD assistance, junior and middle-senior radiologists adjusted the assessment categories of more BI-RADS 4 cases. The diagnosis time with and without CAD support was comparable for five radiologists. The CAD model improved the radiologists’ diagnostic performance for breast masses without prolonging the diagnosis time and assisted in a better BI-RADS assessment, especially for junior radiologists.

Breast Imaging Reporting and Data System Lexicon for US: Interobserver Agreement for Assessment of Breast Masses

Radiology ◽  
2009 ◽  
Vol 252 (3) ◽  
pp. 665-672 ◽  
Author(s):  
Nouf Abdullah ◽  
Benoît Mesurolle ◽  
Mona El-Khoury ◽  
Ellen Kao
Keyword(s):  
Breast Imaging ◽  
Interobserver Agreement ◽  
Data System ◽  
Breast Masses

ROLE OF ULTRASONOGRAPHIC ELASTOGRAPHY IN THE DIFFERENTIAL DIAGNOSIS OF BREAST MASSES AND ITS CONTRIBUTION TO CLASSICAL ULTRASONOGRAPHIC EVALUATION

2021 ◽  
pp. 48-50
Author(s):  
Ashok Kumar Verma ◽  
Rashmi Rashmi ◽  
Rakesh Kumar Verma ◽  
Mahendra Kumar Pandey
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Diagnostic Performance ◽  
Breast Biopsy ◽  
Strain Ratio ◽  
Breast Lesions ◽  
Breast Masses ◽  
Screening Programs ◽  
Imaging Department ◽  
Malignant Breast

Introduction: India is experiencing an unprecedented rise in the number of breast cancer cases across all sections of society. Breast cancer is now the most common malignancy in women and the second leading cause of cancer- related mortality. Breast cancer is quite easily and effectively treated, provided it is detected in it's early stages. There is a drastic drop in the survival rates when women present with advanced stage of breast cancer, regardless of the setting. Unfortunately, women in resource-poor and developing countries, like India, generally present at a later stage of disease than women elsewhere, partly due to the absence of effective awareness programs and partly due to the lack of proper mass screening programs Aims And Objectives: The diagnostic performance of elastography in differentiating benign from malignant breast lesions. To assess whether elastography has the potential to reduce the need for breast biopsy /FNAC. Cut off value of Strain Ratio for benign versus malignant breast lesions. Further characterize BI-RADS 3 lesions using elastography Materials And Methods: The study was approved by the GSVM MEDICAL COLLEGE AND LLR HOSPITAL Ethics Committee. All patients that presented to the Radiology and Imaging Department of LLR HOSPITAL for diagnostic work up for breast pathology were included in the study. After obtaining a written and signed informed consent from all patients, they were subjected to conventional B-Mode ultrasonography followed by elastography. All diagnostic breast imaging was done with Samsung RS80A ultrasound machine using linear array transducer of frequency 5-12MHz.Observations & Results: The elastography patterns for each lesion were assessed and documented in color scale. Color images were constructed automatically and displayed as a color-overlay on the B-mode image. The color pattern of each lesion was then evaluated on a scale of 1-5 according to the Tsukuba elasticity scoring system. Conclusion: Strain Ratio cutoff of 3.3 is a sensitive parameter to differentiate benign and malignant breast lesions. Elastography is a specic test for differentiating benign and malignant breast lesions. The combined use of elasticity score, strain ratio and B- Mode sonographyincreases the diagnostic performance in distinguishing benign from malignant breast masses.

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