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Breast cancer is the second most prevalent type of cancer among women. Breast ultrasound (BUS) imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities in the breast. To improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is helpful for breast cancer detection and classification. Normally, a CAD system consists of four stages: pre-processing, segmentation, feature extraction, and classification. In this chapter, the pre-processing step includes speckle noise removal using speckle reducing anisotropic diffusion (SRAD) filter. The goal of segmentation is to locate the region of interest (ROI) and active contour-based segmentation and fuzzy C means segmentation (FCM) are used in this work. The texture features are extracted and fed to a classifier to categorize the images as normal, benign, and malignant. In this work, three classifiers, namely k-nearest neighbors (KNN) algorithm, decision tree algorithm, and random forest classifier, are used and the performance is compared based on the accuracy of classification.

Background: Breast cancers are widely prevalent in females worldwide and their diagnosis is effectively facilitated by the analysis of various hormone receptors and certain biomarkers. One of the key biomarkers is the proliferation marker Ki-67 which is a nucleoprotein used for predicting these cancers. This study was carried out to analyse the pattern of presentation of Ki-67 in breast cancer. Materials and Methods: This cross-sectional, retrospective, record based study was carried out among 100 resected breast cancer specimens. Haemotoxylin and Eosin stained slide cut from formalin fixed paraffin wax embedded, resected specimens of breast carcinoma were analyzed. The histological diagnosis of tumor was diagnosed as per WHO classification. The positive or negative immunohistochemical expression of Estrogen, progesterone and Androgen receptors in addition were evaluated using standard protocol. Ki-67 expression was also analyzed and correlated with the hormonal receptor presentation. Results: Ki-67 was graded as 14%. Significantly high expression of Ki-67 was witnessed in 61% of the specimens. As far as the other biomarkers were examined, negative expressions of ER, PR, Her2neu and AR were strongly correlating to Ki-67 [removed]90.5%. 86.8%, 81.1% and 100%) respectively. However, the observed difference was statistically not significant when compared to the other biomarkers but statistically significant as an independent prognostic marker. Conclusion: Our study has succinctly elaborated that Ki-67 can be used as an efficient and independent biomarker to determine the survival outcomes of breast cancers. Considering its role in establishing therapeutic protocols, further research is required to evaluate the impact of Ki-67 expression at the molecular level. Keywords: Breast cancer, Biomarkers, Hormone receptors, Ki- 67, Immunohistochemistry.

Breast cancer is the most common cancer in women. Although current therapies have increased survival rates for some breast cancer types, other aggressive invasive breast cancers remain difficult to treat. As the onset of breast cancer is often associated with the appearance of extracellular markers, these could be used to better target therapeutic agents. Here, we demonstrated by nanobiophysical approaches that overexpression of α-sialylated glycans in breast cancer provides an opportunity to combat cancer cells with oncolytic reoviruses. Notably, a correlation between cellular glycan expression and the mechanical properties of reovirus attachment and infection is observed in a serotype-dependent manner. Furthermore, we enhance the infectivity of reoviruses in malignant cells by the coinjection of α-sialylated glycans. In conclusion, this study supports both the use of reoviruses as an oncolytic agent in nanomedicine and the role of α-sialylated glycans as adjuvants in oncolysis, offering new perspective in oncolytic cancer therapy.

Breast cancer (BC) has been the second largest cause of death for women around the world for the past few years. BC is characterized by the chronic pain, genes mutation, color (redness), changes in the size and texture of the skin. BC classification helps clinicians to find a comprehensive and accurate response to treatment, with the most common binary classification (benign / malignant cancer). Nowadays, the Machine Learning (ML) techniques are commonly used in the case of classification of breast cancer. They support with high classification accuracy and rapid evaluation technologies. The proposed research work is mainly focused on supervised learning algorithm, which uses four distinct classifiers: K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Artificial Neural Network (ANN) for the classification of breast cancer. Also, this research work suggests the difference between the aforementioned classifiers and determines their accuracy. The performance of the classifier is assessed based on its accuracy, sensitivity, specificity, precision and recall. Results indicate that, ANN provides the highest accuracy of 97.60% than the other classifiers.

Background Several treatments are available to reduce the risk of fragility fractures associated with osteoporosis. The choice of treatment requires knowledge of patients’ values and preferences. The aim of the present study was to summarize what is known about the values and preferences relevant to the management of osteoporosis in women. Methods We conducted a comprehensive search of several databases for studies reported in any language that had included women who had already started or were about to start any pharmacological therapy for osteoporosis. Pairs of reviewers independently selected the studies and extracted the data. The results were synthesized narratively. Results We included 26 studies reporting on 15,348 women (mean age, 66 years). The women considered the effectiveness and adverse events equally, followed by the convenience of taking the drug and its effect on daily routine (less frequent dosing was preferred, the oral route was preferred, and the injectable route was preferred over oral if given less frequently). The treatment cost and duration were less important factors for decision making. Fear of breast cancer and fear of resuming uterine bleeding were common reasons for not choosing estrogen therapy. Calcium and vitamin D were viewed as safe and natural. Across the studies, the preferences were not affected by age, previous drug exposure, or employment status. Conclusions Women starting osteoporosis medications value effectiveness and side effects equally and prefer medications given less frequently. Injectable drugs appear acceptable if given less frequently. More research on patient values and preferences is needed to guide decision making in osteoporosis.

Breast cancer is the most frequently diagnosed cancer in women worldwide. Identifying reliable biomarkers and druggable molecular targets pose to be a significant quest in breast cancer research. p21-activated kinase 1 (PAK1) is a serine/threonine kinase that direct cell motility, cytoskeletal remodelling, and has been shown to function as a downstream regulator for various cancer signalling cascades that promote cell proliferation, apoptosis deregulation and hasten mitotic abnormalities, resulting in tumor formation and progression. The heterogeneity and acquired drug resistance are important factors that challenge the treatment of breast cancer. p21-activated kinase 1 signalling is crucial for activation of the Ras/RAF/MEK/ERK, PI3K/Akt/mTOR and Wnt signalling cascades which regulate cell survival, cell cycle progression, differentiation, and proliferation. A study involving proteogenomics analysis on breast cancer tissues showed the PAK1 as outlier kinase. In addition to this, few outlier molecules were identified specific to subtypes of breast cancer. A few substrates of PAK1 in breast cancer are already known. In this paper, we have discussed a similar approach called Kinase Interacting Substrate Screening (KISS) for the identification of novel oncogenic substrates of p21-activated kinase specific to subtypes of breast cancer. Such high throughput approaches are expected to accelerate the process of identifying novel drug targets and biomarkers.

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. Cancer has generally an abnormal cell that grows and invade healthy cells in the body. Breast cancer occurs when malignant tumors develop in the breast. Early detection of the breast cancer can effective in increasing the rate of survival. There are several Medical image techniques which has been used for detecting breast cancer. The survey explored the needs for the thermography over other screening tools. Thermography de-tects and records temperature changes on the surface of the skin which is known as Digital Infrared Thermal Imaging (DITI). Thermography is known for its features like non-invasive, painless, cost effective and high chances of recovery, which has got ability to detect the breast cancer at near begin-ning. There is no such tool which provides accurate predictability unless biopsy. But the combination of tools with thermography may boost the sensitivity and specificity when compared with other mechanisms. How to Cite this Article Pubmed Style Pavithra P, Ravichandran K, Sekar K, Manikandan R. The Effect of Thermography on Breast Cancer Detection. SRP. 2018; 9(1): 10-16. doi:10.5530/srp.2018.1.3 Web Style Pavithra P, Ravichandran K, Sekar K, Manikandan R. The Effect of Thermography on Breast Cancer Detection. http://www.sysrevpharm.org/?mno=302644541 [Access: March 28, 2021]. doi:10.5530/srp.2018.1.3 AMA (American Medical Association) Style Pavithra P, Ravichandran K, Sekar K, Manikandan R. The Effect of Thermography on Breast Cancer Detection. SRP. 2018; 9(1): 10-16. doi:10.5530/srp.2018.1.3 Vancouver/ICMJE Style Pavithra P, Ravichandran K, Sekar K, Manikandan R. The Effect of Thermography on Breast Cancer Detection. SRP. (2018), [cited March 28, 2021]; 9(1): 10-16. doi:10.5530/srp.2018.1.3 Harvard Style Pavithra, P., Ravichandran, . K., Sekar, . K. & Manikandan, . R. (2018) The Effect of Thermography on Breast Cancer Detection. SRP, 9 (1), 10-16. doi:10.5530/srp.2018.1.3 Turabian Style Pavithra, PR, KS Ravichandran, KR Sekar, and R Manikandan. 2018. The Effect of Thermography on Breast Cancer Detection. Systematic Reviews in Pharmacy, 9 (1), 10-16. doi:10.5530/srp.2018.1.3 Chicago Style Pavithra, PR, KS Ravichandran, KR Sekar, and R Manikandan. "The Effect of Thermography on Breast Cancer Detection." Systematic Reviews in Pharmacy 9 (2018), 10-16. doi:10.5530/srp.2018.1.3 MLA (The Modern Language Association) Style Pavithra, PR, KS Ravichandran, KR Sekar, and R Manikandan. "The Effect of Thermography on Breast Cancer Detection." Systematic Reviews in Pharmacy 9.1 (2018), 10-16. Print. doi:10.5530/srp.2018.1.3 APA (American Psychological Association) Style Pavithra, P., Ravichandran, . K., Sekar, . K. & Manikandan, . R. (2018) The Effect of Thermography on Breast Cancer Detection. Systematic Reviews in Pharmacy, 9 (1), 10-16. doi:10.5530/srp.2018.1.3

Identification of breast cancer plays a major role in medical field nowadays. Women are facing different types of cancer and one among them is breast cancer which has severe impact. Breast cancer is of two types i.e. Malign or Benign type. Benign is given as a non-curable type of cancer and Malign is given as curable type of cancer. Breast cancer is symbolized by the modification of genes, persistent pain, changes in the measurement, change in shade(redness),skin appearance of breasts. In the early days of identifying breast cancer is done by using different algorithms namely Support Vector Machine (SVM) algorithm,K Nearest Neighbor (KNN) algorithm, MLP algorithm, etc., By using these algorithms the accuracy of detecting the cancer is not met the extend. Our idea is to detect the breast cancer using Decision Tree algorithm. The decision tree algorithm comes under the supervised learning technique. Our idea is to detect the breast cancer using Decision Tree algorithm. The tree algorithm comes under the supervised learning technique. The main advantage of this decision tree algorithm is identifying whether the predicted cancer is either malign or benign type by producing an 99% accuracy.

Prostate cancer is a heterogeneous disease with unique molecular aberrations present in patient sub-groups. Distinct prostate cancer molecular changes have been shown to associate with specific clinical outcomes, suggesting the potential of molecular markers as diagnostic and prognostic biomarkers for prostate cancer. Recurrent ETS family gene fusions, BRAF and SPINK1 overexpression account for about 50-60% of the prostate cancer cases. Genetic aberrations in the remaining 40-50% of the cases is not known. Our attempt to identify new molecular markers in prostate cancer led to the identification of NAALADL2 gene shown to be associated with aggressive prostate cancer. In this study, we carried out a comprehensive analysis of genomic changes of NAALADL2 in prostate and other solid cancers by analyzing copy number changes and gene expression using TCGA next generation RNA sequencing data. We observed recurrent amplification, rearrangement, deletion, mutations and over-expression of NAALADL2 in prostate cancer cases. Notably, we observed mutually exclusive aberrations in NAALADL2 when compared to other cases with known prostate cancer aberrations including ETS gene fusions, indicating NAALADL2 as a distinct molecular sub-set of prostate cancer. Independent validation by fluorescent in situ hybridization (FISH) analysis using break apart probe for NAALADL2 on 874 prostate cancer revealed recurrent amplification and rearrangements in about 8% (71/874) [PN1] of the cases with higher prevalence in Caucasian American than African American cases. Based on these results, we explored prostate cancer TGCA gene fusion database and identified additional cases with gene fusions involving NAALADL2. We selected one of the gene fusions identified in the TCGA database involving NAALADL2-PIK3CA and conducted in vitro functional characterization studies and showed its oncogenic properties. Gene expression microarray analysis of RWPE1 cells transfected with NAALADL2-PIK3CA showed dysregulation of genes involved in cancer related pathways, further suggesting a role for NAALADL2-PIK3CA in prostate cancer development. Based on these studies, we explored the incidence of NAALADL2 gene fusion in other solid cancers including lung squamous cell cancer (LUSC), ovarian cancer, head and neck cancer, cervical cancer and breast cancer shown to have recurrent gene fusions identified in the TCGA gene fusion database. Notably, recurrent rearrangements and amplification are seen in a large subset (45%) of LUSC patients, but not in lung adenocarcinoma, suggesting that NAALADL2 could be developed as a novel biomarker in LUSC. Further validation studies using FISH in our independent cohort of LUSC and breast cancer patients including 79 patients from Ghana confirmed recurrent rearrangements and amplification in a subset of cases. In conclusion, similar to ERG, BRAF and FGFR genes, we show recurrent gene fusions of NAALADL2 across multiple solid cancer with potential applications as a pan cancer molecular marker for cancer diagnosis and a potential target for drug development. Citation Format: Pavithra D. Arachchige, Shannon Carskadon, James Hu, Justin Fernando, Darshan S. Chandrashekar, Nilesh S. Gupta, Sean R. Williamson, Dhananjay A. Chitale, Craig G. Rogers, James O. Peabody, Mani Menon, Tarek A. Bismar, Evelyn Jiagge, Sooryanarayana Varambally, Nallasivam Palanisamy. Recurrent rearrangements of NAALADL2 in prostate, breast, cervical, head and neck and lung squamous cell carcinoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2012.

Fibroadenoma is the most common benign tumor of the breast in young females. Breast cancer arising within a fibroadenoma is a rare phenomenon. The incidence of carcinoma within a fibroadenoma is reported to be between 0.1% and 0.3% in a screened population, with a peak age of occurrence between the 4th and 5th decade. We present a case of 29-year-old female with ductal carcinoma in situ in a background of fibroadenoma. There is a low percentage of fibroadenomas harboring carcinoma; however, all breast lumps should be seriously managed; extirpation and histological examination is recommended.

Background: Breast cancer is the second commonest form of cancer among women. Several studies have been conducted to identify potential risk factors. However, role of trace elements or metals in causing breast cancer has not been studied to great extent. Aims: To estimate the serum levels of calcium, copper, magnesium, iron, phosphorus and zinc and determine their role in causing breast cancer in female patients. Settings and Design: A case-control study on female patients with breast cancer was conducted in a private superspecialty hospital and Cancer centre situated in Southern part of India. Materials and Methods: Newly diagnosed female patients with breast cancer in the age group of 30-60 y attending Oncology clinic were included in the study. These cases were selected irrespective of type and stage of the disease. The age matched control subjects were drawn from apparently healthy women attending master health check at superspecialty hospital. The patients or controls suffering from co-morbid conditions which affect serum levels of metal ions and other malignancies, and those undergoing treatment for breast cancer were excluded from the study. Serum was separated and tests were performed according to standard procedure for each metal ion on the same day. The estimation of metal ions was done by UV-Visible Spectrophotometer-CHEM 7. Statistical Analysis: Independent Samples T-test was used to calculate difference between the two means. The p-value of

Here, we report Prostaglandin A2 (PGA2) induced binding of HSP70 to a novel site on φ1 SMAR1 5′ UTR which stabilizes the wild type transcript and leads to subsequent increase in SMAR1 protein levels. SMAR1 mediated cell cycle arrest is perturbed in PGA2-treated cells when HSP70 is knocked-down. Contrarily HSP70, unlike SMAR1, is overexpressed in breast cancers. We demonstrate that this is because of the inability of HSP70 to bind to the φ17 SMAR1 UTR variant which is the predominant form in breast cancers.