MRI of ovarian tumors

There is a wide spectrum of distinctive benign and malignant ovarian neoplasms with characteristic epidemiology and pathological features. They may occur in hereditary (10–15%) or sporadic settings. Recent advances in genetics and pathology have thrown fresh light on histogenesis, etiopathogenesis, histopathology, and tumor biology of these distinctive tumors leading to improved classification systems, personalized management paradigms, and surveillance guidelines. The 2020 World Health Organization (WHO) classification of ovarian tumors recognizes a rich diversity of tumors based on histogenesis, histomorphology, and genetic abnormalities. Additionally, detailed studies of several hereditary syndromes have unraveled the genetic and molecular underpinnings of a variety of tumors, including elucidation of genetic abnormalities and tumor pathways that determine biological behavior and prognosis. For example, it is now established that most BRCA-associated high-grade serous carcinomas (HGSCs) originate from the distal fallopian tube and consistently demonstrate inactivating mutations of TP53 and BRCA genes. Both BRCA-associated and sporadic HGSCs respond exquisitely to a combination of platinum-based chemotherapy and PARP inhibitors. The risk of ovarian cancers may thus be significantly reduced by prophylactic salpingectomy or salpino-oophorectomy in patients with these hereditary syndromes. Cross-sectional imaging studies including ultrasonography (US), computed tomography (CT), magnetic resonance imaging (MRI), and 18F FDG-PET/CT play a seminal role in the detection, localization, characterization, and staging of ovarian tumors. Particularly, recent advances in MRI hardware and software allow faster acquisition of volumetric imaging data in a short breath-hold, thereby permitting thin slice, high resolution, dynamic multiphasic MRI examination as well as multiplanar reconstructions. The addition of diffusion-weighted images, ADC maps, and MR perfusion studies permit multiparametric assessment of ovarian masses, allowing for better tissue distinction and superior lesion characterization than other modalities. Signal characteristics on MRI allow detection of hemorrhage, myxoid changes, fibrous changes and help differentiate complex cystic from solid masses. An algorithmic approach based on patient demographic information, clinical/laboratory findings, and MRI imaging features may help accurately diagnose ovarian neoplasms. A tailored, patient-centric reporting system of categorization and risk-stratification of adnexal masses based on MRI findings (O-RADS MRI) has been recently introduced and validated. By allowing consistent application of standardized nomenclature, this reporting system may lead to optimal management paradigms while avoiding unnecessary or radical surgery.