ASSESSING THE FEASIBILITY OF USING THE RETROGLANDULAR APPROACH IN THE MANAGEMENT OF DEEPLY SEATED MALIGNANT BREAST TUMOR

Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.

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Determination of Alterations of Phase Angle of Ultrasound Transmitted Through a Malignant Breast Tumor: A Preliminary Investigation

Ultrasound in Medicine ◽

10.1007/978-1-4613-4021-8_115 ◽

1978 ◽

pp. 493-501 ◽

Cited By ~ 1

Author(s):

E. Kelly Fry ◽

N. T. Sanghvi ◽

F. J. Fry ◽

G. Gardner ◽

H. S. Gallager

Keyword(s):

Phase Angle ◽

Breast Tumor ◽

Preliminary Investigation ◽

Malignant Breast Tumor ◽

Malignant Breast

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A case of giant cell tumor of the breast, clinically suspected as malignant breast tumor

Surgical Case Reports ◽

10.1186/s40792-019-0635-4 ◽

2019 ◽

Vol 5 (1) ◽

Cited By ~ 1

Author(s):

Mitsuo Terada ◽

Naomi Gondo ◽

Masataka Sawaki ◽

Masaya Hattori ◽

Akiyo Yoshimura ◽

...

Keyword(s):

Giant Cell Tumor ◽

Giant Cell ◽

Breast Tumor ◽

Cell Tumor ◽

Malignant Breast Tumor ◽

Malignant Breast

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A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/5894010 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Mengwan Wei ◽

Yongzhao Du ◽

Xiuming Wu ◽

Qichen Su ◽

Jianqing Zhu ◽

...

Keyword(s):

Breast Tumor ◽

Texture Features ◽

Morphological Features ◽

High Dimensional ◽

Svm Classifier ◽

Ultrasound Images ◽

Malignant Breast Tumor ◽

Malignant Breast ◽

Low Dimensional

The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to women’s health worldwide. Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features. For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant. Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted. Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively. Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result. The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier. Consequently, texture and morphological features are efficiently combined. Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11% accuracy, a 94.34% sensitivity, and an 86.49% specificity, which outperforms many related benign and malignant breast tumor classification methods.

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

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2016.5594 ◽

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.

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Malignant breast tumor phospholipid profiles using 31P magnetic resonance

Cancer Letters ◽

10.1016/s0304-3835(01)00780-7 ◽

2002 ◽

Vol 176 (2) ◽

pp. 159-167 ◽

Cited By ~ 21

Author(s):

Thomas E Merchant ◽

John N Kasimos ◽

Thea Vroom ◽

Elco de Bree ◽

Jan Lei Iwata ◽

...

Keyword(s):

Magnetic Resonance ◽

Breast Tumor ◽

Malignant Breast Tumor ◽

Malignant Breast

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Sex Steroid Hormones in Serum and Tissue of Benign and Malignant Breast Tumor Patients

Disease Markers ◽

10.1155/2000/305940 ◽

2000 ◽

Vol 16 (3-4) ◽

pp. 151-157 ◽

Cited By ~ 22

Author(s):

Essam A. Mady ◽

Ezz El-Din H. Ramadan ◽

Alaa A. Ossman

Keyword(s):

Breast Cancer ◽

Postmenopausal Women ◽

Steroid Hormones ◽

Breast Tumor ◽

Tumor Tissue ◽

Breast Tumors ◽

Sex Steroid ◽

Malignant Breast Tumor ◽

Malignant Breast ◽

Benign Breast Tumors

The ability of breast tumors to synthesize sex steroid hormones is well recognized and their local production is thought to play a role in breast cancer development and growth. The aim of this study was to estimate local intra-tumoral and circulating levels of Estrone (E1), Estrone Sulfate (E1S), Estradiol (E2), Estriol (E3), and Testosterone (T) in 33 pre- and postmenopausal women with primary breast cancer in comparison to 12 pre- and postmenopausal women with benign breast tumors. The mean levels of the studied sex hormones were higher in serum and tumor tissue of breast cancer women than those with benign breast tumors apart from Testosterone which showed a significant decrease in pre- and postmenopausal women with breast cancer (P< 0.001 for follicular phase,P< 0.001 for luteal phase, andP< 0.001 for postmenopausal). The levels of the five hormones were significantly higher intra-tumoral than in serum of both benign and malignant breast tumor women with E1S as the predominant estrogen. There was only a positive significant correlation between serum and tumor tissue levels of E1(rs= 0.52,P< 0.05 for follicular;rs= 0.63,P< 0.05 for luteal andrs= 0.58,P< 0.05 for postmenopausal) and a significant correlation between serum and tumor tissue of T (rs= 0.64,P< 0.05 for follicular;rs= -0.51,P< 0.05 for luteal andrs= -0.81,P< 0.04 for postmenopausal).

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