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Mathematical Models of Breast and Ovarian Cancers

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dc.contributor.author Botesteanu, Dana-Adriana
dc.contributor.author Lipkowitz, Stanley
dc.contributor.author Lee, Jung-Min
dc.contributor.author Levy, Doron
dc.date.accessioned 2022-06-03T11:31:06Z
dc.date.available 2022-06-03T11:31:06Z
dc.date.issued 2016-06
dc.identifier.issn 23105496
dc.identifier.uri http://hdl.handle.net/123456789/8272
dc.description 44p;, ill. en_US
dc.description.abstract Women constitute the majority of the aging United States (US) population, and this has substantial implications on cancer population patterns and management practices. Breast cancer is the most common women's malignancy, while ovarian cancer is the most fatal gynecological malignancy in the US. In this review we focus on these subsets of women's cancers, seen more commonly in postmenopausal and elderly women. In order to systematically investigate the complexity of cancer progression and response to treatment in breast and ovarian malignancies, we assert that integrated mathematical modeling frameworks viewed from a systems biology perspective are needed. Such integrated frameworks could offer innovative contributions to the clinical women's cancers community, since answers to clinical questions cannot always be reached with contemporary clinical and experimental tools. Here, we recapitulate clinically known data regarding the progression and treatment of the breast and ovarian cancers. We compare and contrast the two malignancies whenever possible, in order to emphasize areas where substantial contributions could be made by clinically inspired and validated mathematical modeling. We show how current paradigms in the mathematical oncology community focusing on the two malignancies do not make comprehensive use of, nor substantially reflect existing clinical data, and we highlight the modeling areas in most critical need of clinical data integration. We emphasize that the primary goal of any mathematical study of women's cancers should be to address clinically relevant questions. en_US
dc.language.iso en en_US
dc.publisher University of Cape Coast en_US
dc.subject ovarian cancer en_US
dc.subject breast cancer en_US
dc.subject mathematical modeling en_US
dc.subject systems biology en_US
dc.title Mathematical Models of Breast and Ovarian Cancers en_US
dc.type Article en_US


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