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Ovarian cancer is amongst the most life-threatening malignancy of the female reproductive system, whereas 90% of those ovarian cancers are epithelial with an overall poor five-year survival rate of 44% across all stages and all races [1]–[2], [31]. This paper aims to review the current treatment and diagnostic strategies for ovarian cancer [3]. Using grounded substantial research, multiple figures were developed to show the relations of ovarian cancer diagnostics and ovarian cancer therapeutics.  It is a great start to look into what may be causing most patients to become resistant to the current standard of care, platinum-based chemotherapeutics, for ovarian cancer [4]. A comprehensive literature review will be used to understand the genetic basis of the disease and possible cancer growth patterns, so we could possibly introduce better diagnostics and therapeutics [5]. The findings show that there are a variety of treatments options other than the standard of care, platinum-based therapy [6]. Nanoparticle encapsulation therapy is one way that has been approved by the FDA to therapeutically treat ovarian cancer without the platinum resistant side effects [7]. Also, the discovery of different diagnostics for ovarian cancer can help with better individualized treatments for patients with different forms of ovarian cancer [8]. Currently, the only serous diagnostic test for the detection of ovarian cancer is high levels of Cancer Antigen 125 (CA-125), which is only shown in 50% of early staged ovarian cancers [16]. The main treatment option for ovarian cancer is platinum-based drugs, in which most cases of patients with ovarian cancer will become resistant. Detecting and treating ovarian cancer while the cells are small, contained, and still in the early stages in vivo still remains to be a challenge [9]. Here, we will demonstrate the bioelectrical interactions of the ovarian cancer cells fused with the magnetic iron oxide nanoparticles with the use of an MRI. The findings demonstrate that the diagnostic method for the early detection of epithelial ovarian cancer requires the use of magnetic iron oxide nanoparticles with specific ligand external profiles as a contrast reagent to make the small-sized ovarian cancer cells appear more visible under MRI. 

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References

  1. Kim, A., et al., Therapeutic strategies in epithelial ovarian cancer. Journal of experimental & clinical cancer research, 2012. 31(1): p. 14.
     Google Scholar
  2. Toss, A., et al., Hereditary ovarian cancer: not only BRCA 1 and 2 genes. BioMed research international, 2015. 2015.
     Google Scholar
  3. Oronsky, B., et al., A brief review of the management of platinum-resistant–platinum-refractory ovarian cancer. Medical Oncology, 2017. 34(6): p. 103.
     Google Scholar
  4. Grunewald, T. and J.A. Ledermann, Targeted therapies for ovarian cancer. Best Practice & Research Clinical Obstetrics & Gynaecology, 2017. 41: p. 139-152.
     Google Scholar
  5. Januchowski, R., et al., Inhibition of ALDH1A1 activity decreases expression of drug transporters and reduces chemotherapy resistance in ovarian cancer cell lines. The international journal of biochemistry & cell biology, 2016. 78: p. 248-259.
     Google Scholar
  6. Wang, Y., et al., Anti-proliferative effect and cell cycle arrest induced by saponins extracted from tea (Camellia sinensis) flower in human ovarian cancer cells. Journal of Functional Foods, 2017. 37: p. 310-321.
     Google Scholar
  7. Engelberth, S.A., N. Hempel, and M. Bergkvist, Development of nanoscale approaches for ovarian cancer therapeutics and diagnostics. Critical ReviewsTM in Oncogenesis, 2014. 19(3-4).
     Google Scholar
  8. Kreuzinger, C., et al., Molecular characterization of 7 new established cell lines from high grade serous ovarian cancer. Cancer letters, 2015. 362(2): p. 218-228.
     Google Scholar
  9. Davies, S., et al., High incidence of ErbB3, ErbB4 and MET expression In ovarian cancer. International journal of gynecological pathology: official journal of the International Society of Gynecological Pathologists, 2014. 33(4): p. 402.
     Google Scholar
  10. Van Berckelaer, C., et al., Current and future role of circulating tumor cells in patients with epithelial ovarian cancer. European Journal of Surgical Oncology (EJSO), 2016. 42(12): p. 1772-1779.
     Google Scholar
  11. Katchman, B.A., et al., Autoantibody biomarkers for the detection of serous ovarian cancer. Gynecologic Oncology, 2017. 146(1): p. 129-136.
     Google Scholar
  12. Herzog, T.J., et al., Impact of molecular profiling on overall survival of patients with advanced ovarian cancer. Oncotarget, 2016. 7(15): p. 19840.
     Google Scholar
  13. Hentze, J.L., et al., Searching for new biomarkers in ovarian cancer patients: Rationale and design of a retrospective study under the Mermaid III project. Contemporary Clinical Trials Communications, 2017. 8: p. 167-174.
     Google Scholar
  14. Iyer, V.R. and S.I. Lee, MRI, CT, and PET/CT for Ovarian Cancer Detection and Adnexal Lesion Characterization. American Journal of Roentgenology, 2010. 194(2): p. 311-321.
     Google Scholar
  15. Wabler, M., et al., Magnetic resonance imaging contrast of iron oxide nanoparticles developed for hyperthermia is dominated by iron content. International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group, 2014. 30(3): p. 192-200.
     Google Scholar
  16. Das, P. M., & Bast, R. C. (2008). Early detection of ovarian cancer. Biomarkers in Medicine, 2(3), 291–303. http://doi.org/10.2217/17520363.2.3.291
     Google Scholar
  17. Yallapu, M.M., et al., Multi-functional Magnetic Nanoparticles for Magnetic Resonance Imaging and Cancer Therapy. Biomaterials, 2011. 32(7): p. 1890-1905.
     Google Scholar
  18. LePine, J.A. and A. Wilcox-King, Developing novel theoretical insight from reviews of existing theory and research. Academy of Management Review, 2010. 35(4): p. 506-509.
     Google Scholar
  19. Mikulka, J., & Dvorák, P. (2014). Fast Calculation of T2 Relaxation Time in Magnetic Resonance Imaging. Session 4A12 SC1: Extended/Unconventional Electromagnetic Theory, Electro-hydrodynamics/Electro-magneto-hydrodynamics, and Electro-biology, 1860.
     Google Scholar
  20. Vaughan, S., Coward, J. I., Bast Jr., R. C., Berchuck, A., Berek, J. S., Brenton, J. D., … Balkwill, F. R. (2011). Rethinking Ovarian Cancer: Recommendations for Improving Outcomes. Nature Reviews. Cancer, 11(10), 719–725. http://doi.org/10.1038/nrc3144
     Google Scholar
  21. Yin, J., Yan, X., Yao, X., Zhang, Y., Shan, Y., Mao, N., … Pan, L. (2012). Secretion of annexin A3 from ovarian cancer cells and its association with platinum resistance in ovarian cancer patients. Journal of Cellular and Molecular Medicine, 16(2), 337–348.
     Google Scholar
  22. Sharrow, A.C., et al., Characterization of aldehyde dehydrogenase 1 high ovarian cancer cells: Towards targeted stem cell therapy. Gynecologic Oncology, 2016. 142(2): p. 341-348.
     Google Scholar
  23. Chen, S., et al., Fascaplysin inhibit ovarian cancer cell proliferation and metastasis through inhibiting CDK4. Gene, 2017. 635(Supplement C): p. 3-8.
     Google Scholar
  24. Geninatti Crich, S., et al., "Theranostic" nanoparticles loaded with imaging probes and rubrocurcumin for a combined cancer therapy by folate receptor targeting. 2017.
     Google Scholar
  25. Tajmul, M., Parween, F., Singh, L., Mathur, S. R., Sharma, J. B., Kumar, S., ... & Yadav, S. (2018). Identification and validation of salivary proteomic signatures for non-invasive detection of ovarian cancer. International journal of biological macromolecules, 108, 503-514.
     Google Scholar
  26. van Bracht, E., Raavé, R., Verdurmen, W. P., Wismans, R. G., Geutjes, P. J., Brock, R. E., ... & Daamen, W. F. (2012). Lyophilisomes as a new generation of drug delivery capsules. International journal of pharmaceutics, 439(1), 127-135.
     Google Scholar
  27. Wang, Y., et al., HPIP expression predicts chemoresistance and poor clinical outcomes in patients with epithelial ovarian cancer. Human Pathology, 2017. 60(Supplement C): p. 114-120.
     Google Scholar
  28. Chapman, J.S., et al., Immunoprofiling epithelial ovarian cancer. Gynecologic Oncology, 2017. 147(1): p. 219.
     Google Scholar
  29. Fruscio, R., et al., Ovarian cancer in pregnancy. Best Practice & Research Clinical Obstetrics & Gynaecology, 2017. 41(Supplement C): p. 108-117.
     Google Scholar
  30. Lee, M., et al., Predictive value of circulating tumor cells (CTCs) captured by microfluidic device in patients with epithelial ovarian cancer. Gynecologic Oncology, 2017. 145(2): p. 361-365.
     Google Scholar
  31. Pistollato, F., et al., The use of natural compounds for the targeting and chemoprevention of ovarian cancer. Cancer Letters, 2017.
     Google Scholar
  32. Boya, V.N., et al., Probing mucin interaction behavior of magnetic nanoparticles. Journal of Colloid and Interface Science, 2017. 488(Supplement C): p. 258-268.
     Google Scholar
  33. Chen, J.B., M.A. Neves, and M. Thompson, Biosensor surface attachment of the ovarian cancer biomarker HSP10 via His-tag modification. Sensing and Bio-Sensing Research, 2016. 11: p. 107-112.
     Google Scholar
  34. Luvero, D., A. Milani, and J.A. Ledermann, Treatment options in recurrent ovarian cancer: latest evidence and clinical potential. Therapeutic advances in medical oncology, 2014. 6(5): p. 229-239.
     Google Scholar
  35. Bookman, M.A., et al., Evaluation of new platinum-based treatment regimens in advanced-stage ovarian cancer: a Phase III Trial of the Gynecologic Cancer Intergroup. Journal of Clinical Oncology, 2009. 27(9): p. 1419-1425.
     Google Scholar
  36. Markman, M., et al., Duration of response to second-line, platinum-based chemotherapy for ovarian cancer: implications for patient management and clinical trial design. Journal of Clinical Oncology, 2004. 22(15): p. 3120-3125.
     Google Scholar
  37. Anwar, M.S., et al., Natural compounds alone and in combination with platinum drugs found to show significant anti-tumour activity against ovarian cancer cell lines. 2017, AACR.
     Google Scholar
  38. Flanagan, J.M., et al., Platinum-Based Chemotherapy Induces Methylation Changes in Blood DNA Associated with Overall Survival in Patients with Ovarian Cancer. Clinical Cancer Research, 2017. 23(9): p. 2213-2222.
     Google Scholar
  39. Razi, N., et al., Abstract TMEM-035: GLYCOMARKERS FOR PREDICTING PLATINUM–DRUG RESPONSE IN OVARIAN CANCER. 2017, AACR.
     Google Scholar
  40. Davis, A., A.V. Tinker, and M. Friedlander, “Platinum resistant” ovarian cancer: What is it, who to treat and how to measure benefit? Gynecologic Oncology, 2014. 133(3): p. 624-631.
     Google Scholar
  41. Alvarez, R.D., et al., Moving beyond the platinum sensitive/resistant paradigm for patients with recurrent ovarian cancer. Gynecologic Oncology, 2016. 141(3): p. 405-409.
     Google Scholar
  42. Chatterjee, M., L.C. Hurley, and M.A. Tainsky, Paraneoplastic antigens as biomarkers for early diagnosis of ovarian cancer. Gynecologic Oncology Reports, 2017.
     Google Scholar
  43. Gloss, B.S. and G. Samimi, Epigenetic biomarkers in epithelial ovarian cancer. Cancer letters, 2014. 342(2): p. 257-263.
     Google Scholar
  44. Park, Y., et al., Diagnostic performances of HE4 and CA125 for the detection of ovarian cancer from patients with various gynecologic and non-gynecologic diseases. Clinical Biochemistry, 2011. 44(10): p. 884-888.
     Google Scholar
  45. Heo, C.-K., Y.Y. Bahk, and E.-W. Cho, Tumor-associated autoantibodies as diagnostic and prognostic biomarkers. BMB Reports, 2012. 45(12): p. 677-685.
     Google Scholar
  46. Rechsteiner, M., et al., TP53 mutations are common in all subtypes of epithelial ovarian cancer and occur concomitantly with KRAS mutations in the mucinous type. Experimental and Molecular Pathology, 2013. 95(2): p. 235-241.
     Google Scholar
  47. Vanderstichele, A., et al., Genomic signatures as predictive biomarkers of homologous recombination deficiency in ovarian cancer. European Journal of Cancer, 2017. 86(Supplement C): p. 5-14.
     Google Scholar
  48. Kurman, R.J. and I.-M. Shih, The Origin and Pathogenesis of Epithelial Ovarian Cancer- a Proposed Unifying Theory. The American journal of surgical pathology, 2010. 34(3): p. 433-443.
     Google Scholar
  49. Maistro, S., et al., Germline mutations in BRCA1 and BRCA2 in epithelial ovarian cancer patients in Brazil. BMC Cancer, 2016. 16: p. 934.
     Google Scholar
  50. Bolton, K.L., et al., Association between BRCA1 and BRCA2 mutations and survival in women with invasive epithelial ovarian cancer. Jama, 2012. 307(4): p. 382-389.
     Google Scholar
  51. Mizuno, T., et al., Cancer stem-like cells of ovarian clear cell carcinoma are enriched in the ALDH-high population associated with an accelerated scavenging system in reactive oxygen species. Gynecologic Oncology, 2015. 137(2): p. 299-305.
     Google Scholar
  52. Ruscito, I., et al., Exploring the clonal evolution of CD133/aldehyde-dehydrogenase-1 (ALDH1)-positive cancer stem-like cells from primary to recurrent high-grade serous ovarian cancer (HGSOC). A study of the Ovarian Cancer Therapy–Innovative Models Prolong Survival (OCTIPS) Consortium. European Journal of Cancer, 2017. 79: p. 214-225.
     Google Scholar
  53. Roy, M., et al., Aldehyde dehydrogenase 1 (ALDH1A1) expression by immunohistochemistry is associated with chemo-refractoriness in patients with high-grade ovarian serous carcinoma. Human pathology, 2017.
     Google Scholar
  54. Orywal, K., et al., The activity of class I, II, III and IV alcohol dehydrogenase isoenzymes and aldehyde dehydrogenase in ovarian cancer and ovarian cysts. Advances in medical sciences, 2013. 58(2): p. 216-220.
     Google Scholar
  55. Liebscher, C.A., et al., Aldehyde dehydrogenase 1/epidermal growth factor receptor coexpression is characteristic of a highly aggressive, poor-prognosis subgroup of high-grade serous ovarian carcinoma. Human pathology, 2013. 44(8): p. 1465-1471.
     Google Scholar
  56. van der Steen, S.C., et al., Targeting the extracellular matrix of ovarian cancer using functionalized, drug loaded lyophilisomes. European Journal of Pharmaceutics and Biopharmaceutics, 2017. 113: p. 229-239.
     Google Scholar
  57. Sapiezynski, J., et al., Precision targeted therapy of ovarian cancer. Journal of Controlled Release, 2016. 243: p. 250-268.
     Google Scholar
  58. Kim, P.S., S. Djazayeri, and R. Zeineldin, Novel nanotechnology approaches to diagnosis and therapy of ovarian cancer. Gynecologic Oncology, 2011. 120(3): p. 393-403.
     Google Scholar
  59. Pi, F., et al., RNA nanoparticles harboring annexin A2 aptamer can target ovarian cancer for tumor-specific doxorubicin delivery. Nanomedicine: Nanotechnology, Biology and Medicine, 2017. 13(3): p. 1183-1193.
     Google Scholar
  60. Koutsaki, M., D.A. Spandidos, and A. Zaravinos, Epithelial–mesenchymal transition-associated miRNAs in ovarian carcinoma, with highlight on the miR-200 family: Prognostic value and prospective role in ovarian cancer therapeutics. Cancer letters, 2014. 351(2): p. 173-181.
     Google Scholar
  61. Cagel, M., et al., Mixed micelles for encapsulation of doxorubicin with enhanced in vitro cytotoxicity on breast and ovarian cancer cell lines versus Doxil®. Biomedicine & Pharmacotherapy, 2017. 95(Supplement C): p. 894-903.
     Google Scholar
  62. van der Steen, S.C.H.A., et al., Targeting the extracellular matrix of ovarian cancer using functionalized, drug loaded lyophilisomes. European Journal of Pharmaceutics and Biopharmaceutics, 2017. 113(Supplement C): p. 229-239.
     Google Scholar
  63. Skates, S.J., OCS: Development of the Risk of Ovarian Cancer Algorithm (ROCA) and ROCA screening trials. International journal of gynecological cancer: official journal of the International Gynecological Cancer Society, 2012. 22(Suppl 1): p. S24-S26.
     Google Scholar
  64. Foti, P.V., et al., MR imaging of ovarian masses: classification and differential diagnosis. Insights into Imaging, 2016. 7(1): p. 21-41.
     Google Scholar
  65. Abdelhafez, Y., et al., Role of 18F-FDG PET/CT in the detection of ovarian cancer recurrence in the setting of normal tumor markers. The Egyptian Journal of Radiology and Nuclear Medicine, 2016. 47(4): p. 1787-1794.
     Google Scholar
  66. Cai, Y., et al., Enhanced magnetic resonance imaging and staining of cancer cells using ferrimagnetic H-ferritin nanoparticles with increasing core size. International Journal of Nanomedicine, 2015. 10: p. 2619-2634.
     Google Scholar