Breast cancer—facts and figures
01 Oct 2013-pp 34-40
About: The article was published on 2013-10-01. It has received 517 citations till now. The article focuses on the topics: Breast cancer.
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TL;DR: This research highlights the need to understand more fully the role of Epstein-Barr virus in the development and Kessler-LaSalle syndrome, as well as its role in disease progression.
Abstract: Lowell E. Schnipper, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA; Thomas J. Smith, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD; Derek Raghavan, Levine Cancer Institute, Carolinas HealthCare System, Charlotte, NC; Douglas W. Blayney, Stanford Cancer Institute, Stanford University School of Medicine, Stanford; Patricia A. Ganz, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA; Therese Marie Mulvey, Southcoast Center for Cancer Care, Southcoast Health System, New Bedford, MA; Dana S. Wollins, American Society of Clinical Oncology, Alexandria, VA.
535 citations
TL;DR: Breast cancer survivors are at greater risk for CVD-related mortality compared with women without breast cancer and this increase in risk is manifested approximately 7 years after diagnosis.
Abstract: Background:Cardiovascular disease (CVD) is of increasing concern among breast cancer survivors. However, the burden of this comorbidity in this group relative to the general population, and its temporal pattern, remains unknown.Methods:We compared deaths due to CVD in a population-based sample of 1,
278 citations
TL;DR: This review should provide useful technological support for evidence-based application of herbal medicines in cancer therapy and the development of randomized controlled trials (RCTs) in this emerging research area.
Abstract: Medicinal herbs and their derivative phytocompounds are being increasingly recognized as useful complementary treatments for cancer. A large volume of clinical studies have reported the beneficial effects of herbal medicines on the survival, immune modulation, and quality of life (QOL) of cancer patients, when these herbal medicines are used in combination with conventional therapeutics. Here, we briefly review some examples of clinical studies that investigated the use of herbal medicines for various cancers and the development of randomized controlled trials (RCTs) in this emerging research area. In addition, we also report recent studies on the biochemical and cellular mechanisms of herbal medicines in specific tumor microenvironments and the potential application of specific phytochemicals in cell-based cancer vaccine systems. This review should provide useful technological support for evidence-based application of herbal medicines in cancer therapy.
249 citations
Cites background from "Breast cancer—facts and figures"
...Alkaloids Inhibition of cancer cell growth [13, 14]...
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...These TCM herbs with antibreast cancer activities can be classified into six categories: alkaloids [13, 14], coumarins [15, 16], flavonoids and polyphenols [17, 18], terpenoids [19], quinone [20], and artesunate [21] (Table 1)....
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TL;DR: The findings suggest that both the leaf and bark extracts of Moringa collected from the Saudi Arabian region possess anti-cancer activity that can be used to develop new drugs for treatment of breast and colorectal cancers.
Abstract: In this study we investigated the anti-cancer effect of Moringa oleifera leaves, bark and seed extracts. When tested against MDA-MB-231 and HCT-8 cancer cell lines, the extracts of leaves and bark showed remarkable anti-cancer properties while surprisingly, seed extracts exhibited hardly any such properties. Cell survival was significantly low in both cells lines when treated with leaves and bark extracts. Furthermore, a striking reduction (about 70–90%) in colony formation as well as cell motility was observed upon treatment with leaves and bark. Additionally, apoptosis assay performed on these treated breast and colorectal cancer lines showed a remarkable increase in the number of apoptotic cells; with a 7 fold increase in MD-MB-231 to an increase of several fold in colorectal cancer cell lines. However, no significant apoptotic cells were detected upon seeds extract treatment. Moreover, the cell cycle distribution showed a G2/M enrichment (about 2–3 fold) indicating that these extracts effectively arrest the cell progression at the G2/M phase. The GC-MS analyses of these extracts revealed numerous known anti-cancer compounds, namely eugenol, isopropyl isothiocynate, D-allose, and hexadeconoic acid ethyl ester, all of which possess long chain hydrocarbons, sugar moiety and an aromatic ring. This suggests that the anti-cancer properties of Moringa oleifera could be attributed to the bioactive compounds present in the extracts from this plant. This is a novel study because no report has yet been cited on the effectiveness of Moringa extracts obtained in the locally grown environment as an anti-cancer agent against breast and colorectal cancers. Our study is the first of its kind to evaluate the anti-malignant properties of Moringa not only in leaves but also in bark. These findings suggest that both the leaf and bark extracts of Moringa collected from the Saudi Arabian region possess anti-cancer activity that can be used to develop new drugs for treatment of breast and colorectal cancers.
198 citations
TL;DR: The present research studied the application of data mining techniques to develop predictive models for breast cancer recurrence in patients who were followed-up for two years, and found that the predicted accuracy of the DT model is the lowest of all.
Abstract: Objective: The number and size of medical databases are increasing rapidly but most of these data are not analyzed
for finding the valuable and hidden knowledge. Advanced data mining techniques can be used to discover hidden
patterns and relationships. Models developed from these techniques are useful for medical practitioners to make right
decisions. The present research studied the application of data mining techniques to develop predictive models for
breast cancer recurrence in patients who were followed-up for two years.
Method: The patients were registered in the Iranian Center for Breast Cancer (ICBC) program from 1997 to 2008.
The dataset contained 1189 records, 22 predictor variables, and one outcome variable. We implemented machine
learning techniques, i.e., Decision Tree (C4.5), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to
develop the predictive models. The main goal of this paper is to compare the performance of these three well-known
algorithms on our data through sensitivity, specificity, and accuracy.
Results and Conclusion: Our analysis shows that accuracy of DT, ANN and SVM are 0.936, 0.947 and 0.957
respectively. The SVM classification model predicts breast cancer recurrence with least error rate and highest accuracy.
The predicted accuracy of the DT model is the lowest of all. The results are achieved using 10-fold cross-validation for
measuring the unbiased prediction accuracy of each model.
180 citations