scholarly journals Comparative sonographic review of benign and malignant breast masses

2018 ◽  
Vol 8 (1) ◽  
pp. 42
Author(s):  
Afodun A M ◽  
Eze E D ◽  
Quadri K K ◽  
Muhammed A O ◽  
Masud M A ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Scar Tissue ◽  
Breast Masses ◽  
Ultrasound Findings ◽  
Malignant Breast ◽  
High Frequency Transducer ◽  
Focal Fibrosis ◽  
Made In

Complex breast masses may appear as suspicious ultrasound findings that usually warrant biopsy. Ductal cell carcinoma in-situ (DCIS) is a form of breast cancer with a non-uniform appearance and malignant potential. A longitudinal review of mammary gland ultrasound (with high frequency transducer) within a three-year period was conducted. Differential diagnosis of fibroadenoma, lactating adenoma, mastitis, galactocele, breast cancer, abscess and “general” masses greater than 16 mm in diameter was stratified. Based on the breast imaging reporting in data system (BIRADS), lesions were classified as benign or malignant and recommendations of cytology made in cases of observed overlap findings. Image sonomorphologic information on mass-echogenic halo and non-uniform orientation were documented; while malignant factors like scar tissue, focal fibrosis and papillomas may be associated with a false positive (conclusion) result. Doppler studies on further mass evaluation is encouraged.

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.

Diagnostic value of various vascular features of breast cancer by age

2021 ◽  
pp. 1-9
Author(s):  
Jia Lin ◽  
Wenqiang Lin ◽  
Liang Xu ◽  
Teng Lin
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Critical Role ◽  
Added Value ◽  
Breast Lesions ◽  
Diagnostic Efficiency ◽  
Complex Flow ◽  
Diagnostic Efficacy ◽  
Breast Masses ◽  
Malignant Breast

BACKGROUND: Tumor angiogenesis plays a critical role in the growth and metastasis of breast cancer and evaluating the added value of vascular features to Breast Imaging Reporting and Data System (BI-RADS) in differentiating malignant nodules from benign ones is essential. Micro-flow Imaging (MFI) is a promising noninvasive diagnostic method for the microvessels in breast tumors, but its precise value is still uncertain. OBJECTIVES: Understanding whether malignant tumor vascular characteristics by MFI are associated with breast cancer and whether the diagnostic efficiency varies by age. MATERIALS AND METHODS: We used B-mode Ultrasound and MFI to detect the characteristics of 153 solid breast lesions. Two investigators reviewed the vessels images by MFI and assessed the vascular features, respectively. Evaluating diagnostic efficacy of different vascular features combined with BI-RADS in different age groups. RESULTS: The mean size of lesions is 19.4 (range 18–78) mm. There were 94 breast masses in benign, while 59 breast masses in malignant by pathology. III Adler classification, penetrating vessels, and complex flow pattern showed a positive association with a high risk of malignant breast lesions (p <  0.05). BI-RADS combined with vessel characteristics show better improvement of diagnostic performance of breast lesions in the elderly group than in the young group. CONCLUSIONS: Vascular features by MFI contribute to malignant breast masses’ diagnosis, and the association might be modified by age.

Qualitative Diagnosis of Solid Breast Mass by Blood Flow in Solid Breast Mass Based on Color Doppler Ultrasound

2021 ◽  
Vol 11 (6) ◽  
pp. 1608-1615
Author(s):  
Ding Zuopeng ◽  
Liu Weiyong ◽  
Hu Chunmei ◽  
Wang Tao ◽  
Wang Mingming
Keyword(s):  
Breast Cancer ◽  
Blood Flow ◽  
Differential Diagnosis ◽  
Color Doppler ◽  
Flow Characteristics ◽  
Flow Distribution ◽  
Color Doppler Ultrasound ◽  
Blood Flow Distribution ◽  
Breast Masses ◽  
Malignant Breast

The incidence of breast cancer ranks first among female malignant tumor. With the increase of the sensitivity of color Doppler ultrasound blood flow, the blood flow distribution in and around the tumor can be clearly displayed, and the analysis of hemodynamic parameters is provided, which provides convenience for the study of tumor blood flow characteristics. Studies have shown that tumor cells can secrete a substance called angiogenesis factor, which makes the tumor site form a rich vascular network to promote tumor growth and metastasis. The tumor has many new blood vessels, abnormal structure, thin wall, lack of muscle layer, and is prone to form arteriovenous rash. These characteristics provide a pathological basis for color Doppler flow imaging (CDFI) for the diagnosis of breast cancer. This article discusses the role of two-dimensional sonographic features in the differential diagnosis of benign and malignant breast masses, CDFI was used to study the blood flow distribution and hemodynamic characteristics in benign and malignant breast masses; explore the value of blood flow characteristics and blood flow parameters in the differential diagnosis of breast masses. The experimental results show that the detection rate of blood flow signals and the classification of blood flow signals in the malignant group are higher than those in the benign group, mainly level II and III blood flow, and the irregular branched blood flow is more common, especially when the tumor appears penetrating blood flow supports the diagnosis of malignancy. PSV, RI and PI have a certain differential meaning in the diagnosis of benign and malignant breast masses. PSV, RI and PI of malignant masses are higher than benign masses. For tumors without obvious necrosis, the larger the tumor diameter, the richer the blood flow and the higher the blood flow grade is. The malignant tumors have more blood flow than the benign ones.

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 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.

Diagnostic Values of Breast Imaging Reporting and Data System and ultrasound Elastography in Benign and Malignant Breast Tumor

2016 ◽  
Vol 13 (10) ◽  
pp. 6509-6513
Author(s):  
Xin-Hua Lu
Keyword(s):  
Breast Cancer ◽  
Roc Curve ◽  
Predictive Value ◽  
Breast Tumor ◽  
Breast Imaging ◽  
Data System ◽  
Ultrasound Elastography ◽  
Malignant Breast Tumor ◽  
Diagnostic Values ◽  
Malignant Breast

Objective: To evaluate the diagnostic values of Breast Imaging Reporting and Data System (BI-RADS), ultrasound elastography (UE) and the combination in differentiating benign and malignant breast tumor. Methods: The BI-RADS and UE image features of 248 breast cancer patients (a total of 260 lesions) proved by surgery and pathology from February 2013 to March 2015 were retrospectively analyzed. With the pathologic results as the gold standard, the sensitivity, specificity, positive and negative predictive values, and accuracy were calculated for BI-RADS, UE and the combination. On the basis of the sensitivity and specificity, they were analyzed by receiver operating characteristic (ROC) curve. Results: In all 260 lesions, 71 lesions were benign and 189 were malignant according to UE diagnosis; 50 lesions were benign and 210 were malignant proved by BI-RADS; 55 lesions were benign and 205 were malignant diagnosed by the combination. The sensitivity (86.09%), specificity (61.64%), positive predictive value (85.19%), negative predictive value (63.38%), and accuracy (79.23%) of ultrasound elastography were all less than that of BI-RADS (98.39%, 64.38%, 88.85%, 87.62%, 94.00%) and the combination (99.47%, 73.97%, 92.31%, 90.73%, 98.18%). The areas under the ROC curve for UE, BI-RADS and the combination were respectively 0.746[95%CI(0.673–0.818)], 0.814[95%CI(0.744–0.884)] and 0.867[95%CI(0.805–0.929)]. Conclusion: Ultrasonic BI-RADS can be the first choice for diagnosing breast cancer, with UE as the auxiliary method. The combined application can further improve the diagnosis rate of benign and malignant breast tumor.

scholarly journals Discriminative Localized Sparse Approximations for Mass Characterization in Mammograms

2021 ◽  
Vol 11 ◽  
Author(s):  
Sokratis Makrogiannis ◽  
Keni Zheng ◽  
Chelsea Harris
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Breast Cancer Screening ◽  
Classification Performance ◽  
Breast Masses ◽  
Computer Aided ◽  
Sparse Approximations ◽  
Malignant Breast ◽  
Detection And Diagnosis

The most common form of cancer among women in both developed and developing countries is breast cancer. The early detection and diagnosis of this disease is significant because it may reduce the number of deaths caused by breast cancer and improve the quality of life of those effected. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) methods have shown promise in recent years for aiding in the human expert reading analysis and improving the accuracy and reproducibility of pathology results. One significant application of CADe and CADx is for breast cancer screening using mammograms. In image processing and machine learning research, relevant results have been produced by sparse analysis methods to represent and recognize imaging patterns. However, application of sparse analysis techniques to the biomedical field is challenging, as the objects of interest may be obscured because of contrast limitations or background tissues, and their appearance may change because of anatomical variability. We introduce methods for label-specific and label-consistent dictionary learning to improve the separation of benign breast masses from malignant breast masses in mammograms. We integrated these approaches into our Spatially Localized Ensemble Sparse Analysis (SLESA) methodology. We performed 10- and 30-fold cross validation (CV) experiments on multiple mammography datasets to measure the classification performance of our methodology and compared it to deep learning models and conventional sparse representation. Results from these experiments show the potential of this methodology for separation of malignant from benign masses as a part of a breast cancer screening workflow.

scholarly journals Immunohistochemical Expression of p63 in Benign and Malignant Breast Lesions

2021 ◽  
Author(s):  
Abhishek Saini ◽  
Swaran Kaur Saluja ◽  
MK Garg ◽  
Deepti Agarwal ◽  
Amrita Kulhria ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Myoepithelial Cells ◽  
Normal Breast ◽  
High Morbidity ◽  
Breast Lesions ◽  
Benign Lesions ◽  
Medical College ◽  
Malignant Breast ◽  
Malignant Lesions

Introduction: Breast carcinoma demands attention as it causes high morbidity and mortality. It is important to recognise benign lesions to distinguish them from in situ and invasive breast cancer and to assess a patient’s risk of developing breast cancer, so that the most appropriate treatment modality for each case can be established. The p63 has been characterised as a reliable marker of myoepithelial cells of lactiferous duct. It is exclusively expressed in myoepithelial cells of normal breast tissue. Hence, p63 can be of great help in the differential diagnosis involving benign lesions. Also, p63 may aid in distinguishing benign from malignant lesions. Aim: To study the Immunohistochemistry (IHC) expression of p63 in benign and malignant breast lesions. Materials and Methods: The prospective study was conducted on 76 breast specimens for a period of one year, from 1st December 2018 to 30th November 2019 in the Department of Pathology, Bhagat Phool Singh, Government Medical College for Women, Khanpur Kalan, Sonepat, Haryana, India. IHC assessment for p63 nuclear protein was performed. The intensity of p63 expression was evaluated as continuous positive, discontinuous positive and negative. The extent was scored on the basis of percentage of positive cells and assigned a score of negative (0%), 1 (<25%), 2 (26-90%) and 3 (91-100%). Results: Out of 76 cases, 38 cases were diagnosed as benign and 38 cases as malignant. IHC staining with p63 showed nuclear positivity in all benign lesions. Among malignant lesions, four were positive and 34 were negative. Conclusion: According to the above results, p63 is a very useful IHC marker in diagnosing difficult cases, cases of carcinoma in situ, borderline cases and cases with inconclusive histomorphological diagnosis.

Optoacoustic Breast Imaging: Imaging-Pathology Correlation of Optoacoustic Features in Benign and Malignant Breast Masses

2018 ◽  
Vol 211 (5) ◽  
pp. 1155-1170 ◽  
Author(s):  
Reni Butler ◽  
Philip T. Lavin ◽  
F. Lee Tucker ◽  
Lora D. Barke ◽  
Marcela Böhm-Vélez ◽  
...  
Keyword(s):  
Breast Imaging ◽  
Breast Masses ◽  
Malignant Breast

scholarly journals The Dynamic Optical Breast Imaging in the Preoperative Workflow of Women with Suspicious or Malignant Breast Lesions: Development of a New Comprehensive Score

ISRN Oncology ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Massimiliano D'Aiuto ◽  
Giuseppe Frasci ◽  
Maria Luisa Barretta ◽  
Adolfo Gallipoli ◽  
Giovanni Maria Ciuffo ◽  
...  
Keyword(s):  
Breast Imaging ◽  
Roc Analysis ◽  
Potential Role ◽  
Breast Lesions ◽  
Breast Cancer Imaging ◽  
In Situ Carcinoma ◽  
Malignant Breast ◽  
Definition Of

Purpose. To determine the diagnostic accuracy of DOBIComfortScan in patients with Breast Imaging Reporting suspect breast lesions (BI-RADS) 4-5 breast lesions. Materials and Methods. One-hundred and thirteen patients underwent DOBIComfortScan examination before surgery. Twelve parameters were taken into consideration to define DOBI findings. Results. Twenty-seven radical mastectomies, 47 quadrantectomies and 39 wide excisions, were performed. Overall, 65 invasive cancer, 9 in situ carcinoma and 39 nonmalignant lesions, were observed. Ten out of 12 considered parameters resulted significantly in association with histology at discriminant analysis. A summation score of 30.5 resulted to be the best cut off at ROC analysis, giving a sensitivity and specificity of 80% and 87%, respectively, and a positive predictive value of 92.2%. Finally the following DOBI-BI-RADS model was developed: malignant B5≥38 score); possibly malignant (B4=25-37 score); benign but the possibility of malignancy cannot be excluded (B3=20-24 score); benign (B2<20 score). Conclusion. definition of other parameters permits to improve the accuracy of this procedure. Further studies are warranted to define the potential role of DOBIComfortScan in breast cancer imaging.

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