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This paper presents the feasibility of the benign and malignant breast tumor classification according to dielectric properties. The experiment data are obtained by simulating the human breast with the help of COMSOL Multiphysics, in which way electric field amplitude of different samples is collected. Support Vector Machines (SVM) is utilized to analyze the microwave signal and distinguish benign and malignant breast tumors. The capability and reliability of classification are compared with Decision Tree and Random Forest.

Breast cancer is the most common site-specific cancer among women and it is the leading cause of cancer related deaths among women. It accounts for 29% of all newly diagnosed cancers in females and is responsible for 14% of the cancer-related deaths in women. Approximately 1 in 8 women (13%) will be diagnosed with invasive breast cancer in their lifetime. Understanding the biological behavior and natural history of breast cancer has made breast-conserving therapy (BCT) the choice of treatment in early breast cancer. Oncoplastic breast surgery (OPS) has emerged as a new approach to allow wide excision for BCS without compromising the natural shape of the breast And since one of the main factors for good oncoplastic techniques is the cosmetic outcome, inframammary crease incision is a good choice as hidden scar in the breast envelope. Here we introduce a new approach for deep breast tumors through an inframammary incision and retro glandular exploration of breast parenchyma which is a simple and easily performed techniqu

Breast cancer is particularly recognized as highly unpredictible, even from early stages. Practice has shown the need for personalised treatment in cancer patients. It is mandatory, as it is highly useful, to establish the aggressiveness profile of a malignant breast tumor, in order to plan a targeted treatment.

Granular cell tumor (GCT) of the breast is an unusual neoplasm, tipically benign, it represents between 5-6% of all GCT cases. These tumors are more common in middle-aged premenopausal women with a greater pre dilection African American race [1]. Nevertheless, there are also cases de scribed in men [2-4]. Almost all of them are favorable, the malignant cases are uncommon (only 1-3%). Sometimes it could be clinically and radio logically confused with a malignant breast tumor; so it's very important to make a differential diagnosis. The choice therapy is an extensive local extirpation with free margins [5], without the need for adjuvant chemother apy or radiotherapy. Our case is a 61-year-old woman with a GCT, and three years ago a history of breast carcinoma in the same breast.

In this study, we demonstrate the efficacy of scoring statistics derived from a medial axis transform, for differentiating tumor and non-tumor nuclei, in malignant breast tumor histopathology images. Characterizing nuclei shape is a crucial part of diagnosing breast tumors for human doctors, and these scoring metrics may be integrated into machine perception algorithms which aggregate nuclei information across a region to label whole breast lesions. In particular, we present a low-dimensional representation capturing characteristics of a skeleton extracted from nuclei. We show that this representation outperforms both prior morphological features, as well as CNN features, for classification of tumors. Nuclei and region scoring algorithms such as the one presented here can aid pathologists in the diagnosis of breast tumors.

Classification of histopathological images provides great support towards breast cancer diagnosis. Hence, in this work, a Fuzzy Min-Max with a K-highest (Kh-FMM) hyperbox expansion criteria based approach is studied and used to classify the magnification independent breast cancer histopathology images. To categorize the dataset, gray level co-occurrence matrix (GLCM) is utilized to extract features from the images, and are then feed into Kh-FMM neural network. In this paper, BreaKHis dataset is used which contains benign and malignant breast tumor microscopic biopsy images at four different magnifications. Experimental results show that the magnification factor of 200× gives higher accuracy rate than other magnifications.

Classification of histopathology images can provide support towards diagnosis of breast cancer. In this study, a Fuzzy Min-Max (FMM) network based approach is studied and used to classify the magnification independent breast cancer histopathology images. Gray level co-occurrence matrix (GLCM) is used to extract the features from images, and are then feed into FMM network to classify the dataset. BreaKHis database is used in this work, which contains microscopic benign and malignant breast tumor images at four different magnifying factors. Experimental results show that the magnifying factor of 200× gives better recognition rate than other magnification rates.

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.

Receptor-interacting protein kinase 1 (RIP1K) and RIP3K belong to RIPK family, which regulate cell survival and cell death. In the present investigation, the expression levels of RIP1K and RIP3K were evaluated in the 30 malignant, 15 benign, and 20 normal breast tissues, and their correlation with clinicopathological characteristics was also studied. The expression levels of RIP1K and RIP3K were determined, by western blot analysis. The relative RIP1K expression was significantly higher in the malignant and benign tumors when compared to those of normal tissues (P

The purpose of this study was to test the association between human 8-oxoguanine glycosylase 1 (OGG1) gene polymorphisms to the breast tumor in the Iraqi women. One-hundred-three Sample were obtained from patients with breast cancer (28 women malignant breast tumor patients group) ,(28 control group),(28 benign group) and (19 Radiotherapy group) of (30-59) years old in Al-Eluia hospital for woman care, oncology teaching hospital between September 2016 to January 2017. The results showed that there was a significant difference when comparing the control with benign and the radiotherapy patients, and there was a non- significant change in the comparison between the control and the malignant groups.

Current breast cancer risk prognosis methods have high prognostic variability which affects the chemotherapy decisions. Image analysis is a structure analysis tool that aids existing risk prognosis methods in order to improve quality of the prognosis. Fractal image analysis has been rarely used on breast tumor histology images for prognostic purposes and this paper deals with one such study using monofractal and multifractal analysis. Invasive breast tumor histology samples were used based on the absence of any systemic treatment. Obtained images were divided into two groups, named high and low risk, based on the risk prognosis for survival. Images were further subjected to computational analysis using binary and outline fractal dimensions, lacunarity for monofratal analysis and generalized dimension for multifractal analysis. Binary and outline fractal dimensions, as well as generalized dimension yielded statistically significant distinction between high risk and low risk groups. Lacunarity was also different but not statistically significant.

We aimed to identify, using histochemical methods, the degree of elastosis in a malignant breast tumor compared to a benign tumor. Sixty-four tissue blocks were used in this study of which 34 (53.1%) were Invasive ductal carcinoma (IDC) (cases) and 30 (46.8%) were fibroadenomas (controls). Examination of Verhoeff's stained sections revealed different grades of elastosis in 29 (85.2%) cases compared to seven (23.3%) in controls. These findings indicated that elastosis was a prominent feature of IDC (p < 0.000). IDC was associated with more frequent occurrence of different grades of elastosis and should, therefore, be considered a valuable histological finding. Nevertheless, more advanced technology for quantitative measurements of the staining density is recommended to confirm this variation.

This paper presents the classification of benign and malignant breast tumor based on Fine Needle Aspiration Cytology (FNAC) and Probabilistic Neural Network (PNN). Five hundred and sixty nine sets of cell nuclei characteristics obtained by applying image analysis techniques to microscopic slides of FNAC samples of breast biopsy have been used in this study. These data were obtained from the University of Wisconsin Hospitals, Madison. The dataset consist of thirty features which represent the input layer to the PNN. The PNN will classify the input features into benign and malignant. The sensitivity, specificity and accuracy were found to be equal 97.5%, 92.5% and 96.2% respectively. It can be concluded that PNN gives fast and accurate classification and it works as promising tool for classification of breast cell nuclei.

Evaluation of health outcomes can play a role in guiding individual treatment decisions. We developed a utility test based on the principles of conjoint measurement for use in patients with breast cancer. These patients can choose two treatment options: breast conserving treatment or amputation. The test was presented to patients who had undergone either mastectomy or breast conserving treatment for a malignant breast tumor ( n =15), to test feasibility and acceptability of the test. The test was modified after these first experiences and presented a second time to patients, who cooperated also in the first test round ( n =12). We discuss both our preliminary experiences with this test and its appropriateness for this specific clinical problem. We conclude, that this test is not readily convenient for this clinical problem, but that it is a promising approach to utility measurement in general, because of the extensive consistency checks that are possible. We outline the medical problems, for which the test can easily be adapted and the direction of future research in order to solve the problems that we encountered.

Primary pigmented malignant tumors of the breast are rare. In few published studies these lesions are described as metaplastic carcinoma with melanocytic differentiation or colliding carcinoma with melanoma and carcinoma components. Histological criteria to differentiate both entities do not exsist, and detail clinical examination after operation have to be necessary. We describe 35 years old female who had cytologicaly malignant, 2 cm wide lump in her lateral breast quadrant.

Hsp90 inhibitors have demonstrated unusual selectivity for tumor cells despite its ubiquitous expression. This phenomenon has remained unexplained but could be influenced by ectopically expressed Hsp90 in tumors. We have synthesized novel Hsp90 inhibitors that can carry optical or radioiodinated probes via a PEG tether. We show that these tethered inhibitors selectively recognize cells expressing ectopic Hsp90 and become internalized. The internalization process is blocked by Hsp90 antibodies, suggesting that active cycling of the protein is occurring at the plasma membrane. In mice, we show exquisite accumulation of the fluor-tethered versions within breast tumors at very sensitive levels. Cell-based assays with the radiolabeled version showed picomolar detection in cells that express ectopic Hsp90. Our findings show that fluor-tethered or radiolabeled inhibitors targeting ectopic Hsp90 can be used to detect breast cancer malignancies through non-invasive imaging. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):C86. Citation Format: Jared J. Barrott, Aaron P. Smith, Takuya Osada, Nimmi Ramanujam, Michael R. Zalutsky, Kim Lyerly, Timothy A. J. Haystead. Tethered Hsp90 inhibitors carrying optical or radioiodinated probes reveal selective internalization of ectopic Hsp90 in malignant breast tumor cells. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr C86.

The association of the Henoch-Schönlein syndrome with leukemias and lymphomas, though rare, is well known, but this type of pathology appears to be exceptional in the case of solid tumors. The authors report a case of malignant breast tumor wherein cutaneous vasculitis appeared at the moment of the disease's progression.

A 52-year-old woman with advanced cancer of the breast also suffered from pemphi goid eruptions on the skin, in the mucous membranes of the oropharynx, and in the conjunctivae. The pemphigoid lesions were suppressed by treatment with steroid hormones and disappeared after surgical removal of the malignant breast tumor. With regard to its clinical course, the disease entity described is justifiably classified as a “paraneoplastic bullosis.” The literature on this subject is briefly reviewed.

The overall experimental approach of the research discussed in this paper is the use of multidiscipline examination methods, namely, x-rays, ultrasound visualization, signal processing of ultrasound transmission data (FFT) and whole breast histology to elucidate the relation between structural features of breast tissue and their interaction with high frequency sound fields. The specific studies presented here are concerned with an attempt to determine the feasibility of using the phase angle data resulting from the FFT studies to assist in the recognition of differences between normal and malignant breast tissue.

The multidisciplinary nature of the necessary therapeutic measures basically involves three people: a physician, insofar as he represents chemotherapy and hormone therapy, a surgeon and a radiologist, supported by a pathologist.