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Current and Futuristic Roadmap of Ovarian Cancer Management: An Overview

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Ovarian Cancer: Molecular & Diagnostic Imaging and Treatment Strategies

Abstract

Ovarian cancer (OC) is the most lethal gynecological malignancy among women worldwide. In most cases, it is diagnosed late at an advanced stage and does not respond well to existing therapies leading to its poor prognosis. In addition, other factors including epidemiological, complex histological diversity, multiple molecular alterations, and overlapping signaling pathways are also important contributors to poor disease outcome. Efforts have continued to develop a deeper understanding of the molecular pathogenesis and altered signaling nodes that provide hope for better clinical management through the development of novel approaches for early diagnosis, disease subtyping, prognosis, and therapy. In this chapter, we provide a detailed overview of OC and its histological subtypes and discuss prevalent molecular aberrations and active signaling pathways that drive OC progression. We also summarize various diagnostic and prognostic markers and therapeutic approaches currently being employed and discuss emerging findings that hold the potential to change the future course of OC management.

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Author Contributions

O.M., S.K.S., and S.D. wrote the manuscript; S.D., S.S., R.P.R., and A.P.S. proofread and corrected it; O.M. prepared figures; A.P.S. conceptualized the idea, provided resources, and supervised the project. All authors read and approved the final manuscript.

Funding: Authors would like to acknowledge the funding from NIH/NCI [R01CA175772, R01CA224306, U01CA185490 (to A.P.S.); R01CA204801, R01CA231925 (to S.S.)] and USAMCI.

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Correspondence to Ajay Pratap Singh .

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Miree, O., Srivastava, S.K., Dasgupta, S., Singh, S., Rocconi, R., Singh, A.P. (2021). Current and Futuristic Roadmap of Ovarian Cancer Management: An Overview. In: Schatten, H. (eds) Ovarian Cancer: Molecular & Diagnostic Imaging and Treatment Strategies. Advances in Experimental Medicine and Biology, vol 1330. Springer, Cham. https://doi.org/10.1007/978-3-030-73359-9_1

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