Differentiation of Serous and Non-serous Epithelial Ovarian Cancer by Radiological Imaging

Devina Yudistiarta, Wigati Dhamiyati, Lina Choridah

Abstract


Background: Ovarian cancer has a high incidence and fatality rate, with the most prevalent type is epithelial origin. The serous subtype of epithelial cancer predominates. For therapy selection, it is important to differentiate this subtype by histopathological examination as a gold standard method. Since some patients are not eligible for a biopsy, radiological modalities such as Magnetic Resonance Imaging (MRI) are superior in discriminating tissue compared to computed tomography (CT) or ultrasound. The use of the T1 weighted imaging (T1WI) and T2WI sequences can best differentiate between a cystic and a solid lesion. The goal of this study was to use radiological examination to assist in the identification of ovarian cancer.

Methods: This cross-sectional study used secondary data from histopathology and MRI results from patients with ovarian cancer. Data was gathered utilizing electronic medical records in Dr. Sardjito Hospital between January 2017 and May 2022. The following MRI characteristics are evaluated including ascites, papillary projection, solid nodule, signal intensity of solid and cystic components, size, configuration, enhancement of contrast, and bilateral of the lesion.

Results: Thirty-eight subjects made up the study’s sample, and 63% of them had serous subtypes. Bilateral lesion suggested a three times greater likelihood that it was serous ovarian cancer (p 0.02; binary logistic regression). Age >50 years old and strong enhancement on contrast was also relevant for separating the serous subtype from other subtypes (enhancement p 0.02; age p 0.044).

Conclusions: A bilateral lesion with a significantly enhanced pattern can be seen on the MRI of a serous subtype of epithelial ovarian cancer. The elderly are also more likely to develop this cancer.


Keywords


cancer, ovarian, epithelial, serous

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DOI: 10.33371/ijoc.v18i1.1007

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References


International WCRF. Ovarian cancer statistics | World Cancer Research Fund International [Internet]. 2022 [cited 2023 Feb 21]. Available from: https://www.wcrf.org/cancer-trends/ovarian-cancer-statistics/

Desai A, Xu J, Aysola K, et al. Epithelial ovarian cancer: An overview. World J Transl Med. 2014;3(1):1.

Momenimovahed Z, Tiznobaik A, Taheri S, Salehiniya H. Ovarian cancer in the world: Epidemiology and risk factors. Int J Womens Health. 2019;11:287–99.

Andrew E, Green M. Ovarian Cancer: Practice Essentials, Background, Pathophysiology [Internet]. Medscape. 2021 [cited 2022 Sep 17]. Available from: https://emedicine.medscape.com/article/255771-overview

Matulonis UA, Sood AK, Fallowfield L, et al. Ovarian cancer. Nat Rev Dis Prim. 2016;2:1–22.

Foti PV, Attinà G, Spadola S, et al. MR imaging of ovarian masses: classification and differential diagnosis. Insights Imaging. 2016;7(1):21–41.

Aluloski I, Tanturovski M, Jovanovic R, et al. Survival of Advanced Stage High-Grade Serous Ovarian Cancer Patients in the Republic of Macedonia. Open Access Maced J Med Sci. 2017 Dec 15;5(7):904.

Sohaib SA, Sahdev A, Van Trappen P, et al. Characterization of adnexal mass lesions on MR imaging. AJR Am J Roentgenol. 2003 May;180(5):1297-304.

Reid BM, Permuth JB, Sellers TA. Epidemiology of ovarian cancer: a review. Cancer Biol Med. 2017;14(1):9–32.

Tanaka YO, Okada S, Satoh T, et al. Differentiation of epithelial ovarian cancer subtypes by use of imaging and clinical data: a detailed analysis. Cancer Imaging. 2016;16(1):1–9.

Smebye ML, Haugom L, Davidson B, et al. Bilateral ovarian carcinomas differ in the expression of metastasis-related genes. Oncol Lett. 2017;13(1):184–90.

Pannu HK, Ma W, Zabor EC, et al. Enhancement of Ovarian Malignancy on Clinical Contrast Enhanced MRI Studies. ISRN Obstet Gynecol. 2013 Feb 13;2013:1–8.


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