Obstetrical & Gynecological Survey

Prediction Models of Adnexal Masses: State-of-the-Art Review

Importance Several predictive models and scoring systems have been developed to differentiate between benign and malignant ovarian masses, in order to guide effective management. These models use combinations of patient characteristics, ultrasound markers, and biochemical markers. Objective The aim of this study was to describe, compare, and prioritize, according to their strengths and qualities, all the adnexal prediction models. Evidence Acquisition This was a state-of-the-art review, synthesizing the findings of the current published literature on the available prediction models of adnexal masses. Results The existing models include subjective assessment by expert sonographers, the International Ovarian Tumor Analysis models (logistic regression models 1 and 2, Simple Rules, 3-step strategy, and ADNEX [Assessment of Different NEoplasias in the adneXa] model), the Risk of Malignancy Index, the Risk of Malignancy Ovarian Algorithm, the Gynecologic Imaging Reporting and Data System, and the Ovarian-Adnexal Reporting and Data System. Overall, subjective assessment appears to be superior to all prediction models. However, the International Ovarian Tumor Analysis models are probably the best available methods for nonexpert examiners. The Ovarian-Adnexal Reporting and Data System is an international approach that incorporates both the common European and North American approaches, but still needs to be validated. Conclusions Many prediction models exist for the assessment of adnexal masses. The adoption of a particular model is based on local guidelines, as well as sonographer's experience. The safety of expectant management of adnexal masses with benign ultrasound morphology is still under investigation. Target Audience Obstetricians and gynecologists, family physicians. Learning Objectives After participating in this activity, the learner should be better able to explain all the aspects on the available prediction models of adnexal masses; assess the available prediction models of adnexal masses in terms of their accuracy; and describe the sonographic findings of an adnexal mass and estimate the relevant malignancy risk.

Disparities in Genetic Management of Breast and Ovarian Cancer Patients

Importance Hereditary breast and ovarian cancer syndrome (HBOC) is most often caused by pathogenic variants in the BRCA1 or BRCA2 genes. Guidelines exist for genetic testing in patients at high risk, yet significant disparities in genetic testing and management remain. These disparities result in missed opportunities for cancer prevention and treatment. Objective This review details the multiple layers of disparities in genomic knowledge, testing referral, completion, and posttesting risk reduction for at-risk populations. Evidence Acquisition A comprehensive search of the PubMed database was conducted in September 2023 for studies addressing disparities at all points of HBOC risk assessment and risk reduction. Results Disparities in genomic knowledge, referral and testing, and in cancer risk reduction exist by race, ethnicity, insurance status, socioeconomic status, age, and care setting in the United States. Many mitigation strategies have been explored with some success. Conclusion Each component contributes to a “leaky pipe” in BRCA testing and management whereby patients eligible for intervention trickle out of the pipe due to inequities at each step. Implementation of proven strategies aimed at disparity reduction in this setting is essential, as well as additional strategy development. Relevance This review provides clinicians with a comprehensive understanding of disparities in the identification and management of individuals at risk for or diagnosed with HBOC and strategies to reduce disparities in their own practice. Target Audience Obstetricians and gynecologists, family physicians. Learning Objectives After participating in this activity, the learners should be better able to discuss disparities in the testing for and risk-reducing management of patients with pathogenic variants of BRCA1/2; describe populations in which these disparities are greatest; and explain proven strategies for practice change to mitigate these disparities.

Adnexal Masses in Pregnancy

ABSTRACT Importance Adnexal masses are identified in approximately 0.05% to 2.4% of pregnancies, and more recent data note a higher incidence due to widespread use of antenatal ultrasound. Whereas most adnexal masses are benign, approximately 1% to 6% are malignant. Proper diagnosis and management of adnexal masses in pregnancy are an important skill for obstetricians. Objective The aim of this study was to review imaging modalities for evaluating adnexal masses in pregnancy and imaging characteristics that differentiate benign and malignant masses, examine various types of adnexal masses, and understand complications of and explore management options for adnexal masses in pregnancy. Evidence Acquisition This was a literature review using primarily PubMed and Google Scholar. Results Ultrasound can distinguish between simple-appearing benign ovarian cysts and masses with more complex features that can be associated with malignancy. Radiologic information can help guide physicians toward recommending conservative management with observation or surgical removal during pregnancy to facilitate diagnosis and treatment. The risks of expectant management of an adnexal mass during pregnancy include rupture, torsion, need for emergent surgery, labor obstruction, and progression of malignancy. Historically, surgical removal was performed more routinely to avoid such complications in pregnancy; however, increasing knowledge has directed management toward conservative measures for benign masses. Surgical removal of adnexal masses is increasingly performed via minimally invasive techniques including laparoscopy and robotic surgery due to a decreased risk of surgical complications compared with laparotomy. Conclusions and Relevance Adnexal masses are increasingly identified in pregnancy because of the use of antenatal ultrasound. Clear and specific guidelines exist to help differentiate between benign and malignant masses. This is important for management as benign masses can usually be conservatively managed, whereas malignant masses require excision for diagnosis and treatment. A multidisciplinary approach, including referral to gynecologic oncology, should be used for masses with complex features associated with malignancy. Proper diagnosis and management of adnexal masses in pregnancy are an important skill for obstetricians. Target Audience Obstetricians and gynecologists, family physicians Learning Objectives After completing this activity, learners should be better able to compare different types of adnexal masses found in pregnancy, including incidence, clinical features, and imaging characteristics; evaluate an adnexal mass with imaging and laboratory tests; describe complications related to an adnexal mass in pregnancy; and determine management and/or surgical approaches for removal.

Applying Artificial Intelligence to Gynecologic Oncology: A Review

Importance Artificial intelligence (AI) will play an increasing role in health care. In gynecologic oncology, it can advance tailored screening, precision surgery, and personalized targeted therapies. Objective The aim of this study was to review the role of AI in gynecologic oncology. Evidence Acquisition Artificial intelligence publications in gynecologic oncology were identified by searching “gynecologic oncology AND artificial intelligence” in the PubMed database. A review of the literature was performed on the history of AI, its fundamentals, and current applications as related to diagnosis and treatment of cervical, uterine, and ovarian cancers. Results A PubMed literature search since the year 2000 showed a significant increase in oncology publications related to AI and oncology. Early studies focused on using AI to interrogate electronic health records in order to improve clinical outcome and facilitate clinical research. In cervical cancer, AI algorithms can enhance image analysis of cytology and visual inspection with acetic acid or colposcopy. In uterine cancers, AI can improve the diagnostic accuracies of radiologic imaging and predictive/prognostic capabilities of clinicopathologic characteristics. Artificial intelligence has also been used to better detect early-stage ovarian cancer and predict surgical outcomes and treatment response. Conclusions and Relevance Artificial intelligence has been shown to enhance diagnosis, refine clinical decision making, and advance personalized therapies in gynecologic cancers. The rapid adoption of AI in gynecologic oncology will depend on overcoming the challenges related to data transparency, quality, and interpretation. Artificial intelligence is rapidly transforming health care. However, many physicians are unaware that this technology is being used in their practices and could benefit from a better understanding of the statistics and computer science behind these algorithms. This review provides a summary of AI, its applicability, and its limitations in gynecologic oncology. Target Audience Obstetricians and gynecologists, family physicians Learning Objectives After completing this CME activity, physicians should be better able to describe the basic functions of AI algorithms; explain the potential applications of machine learning in diagnosis, treatment, and prognostication of cervical, endometrial, and ovarian cancers; and identify the ethical concerns and limitations of the use of AI in the management of gynecologic cancer patients.

A Review of Reconstruction for Vulvar Cancer Surgery

Importance Vulvar reconstruction may be required after vulvectomy or any vulvar surgery. Providers should be familiar with techniques for reconstruction to improve clinical outcomes. Objective This article reviews the different techniques for reconstruction after vulvectomy and describes the decision-making process for selection of appropriate techniques, postoperative care, and expected outcomes. Evidence Acquisition A literature search was conducted, focusing on the plastic surgery and gynecologic oncology literature, using the following search terms: “vulvar reconstruction,” “perineal reconstruction,” “vulvectomy,” and “vulvar cancer.” The search was limited to English publications. Results Reconstruction after vulvectomy can be performed using a variety of techniques ranging from simple or complex closure to adjacent tissue rearrangement to skin grafting, locoregional, and free flaps. The appropriate technique is best chosen based on the characteristics of the patient and postablative defect, as well as the reconstructive goals. Postoperative complications are usually minor. Conclusions Vulvar reconstruction techniques vary widely and offer patients improved outcomes. Relevance Knowledge of vulvar reconstruction techniques is necessary for gynecologists performing vulvar surgery to ensure optimal patient outcomes. Target Audience Obstetricians and gynecologists, Family Physicians Learning Objectives After completing this activity, the learner should be better able to describe 3 different techniques of vulvar reconstruction; explain the factors involved in choosing a technique; and identify possible complications of vulvar reconstruction.

Vulvar Intraepithelial Neoplasia: A Review of the Disease and Current Management

Importance Vulvar intraepithelial neoplasia (VIN) represents an increasingly common, yet challenging diagnosis that shares many common risk factors with cervical intraepithelial neoplasia. However, unlike cervical intraepithelial neoplasia, effective screening and treatment strategies are much less defined for patients with VIN. Objective The objective of this article is to review the underlying risk factors leading to the development of VIN, identify special populations at risk for VIN, and outline acceptable treatment strategies. Evidence Acquisition This literature review was performed primarily using PubMed. Results Vulvar intraepithelial neoplasia can be separated into usual VIN (uVIN) and differentiated VIN (dVIN). The more common uVIN is related to underlying human papillomavirus infection, whereas dVIN occurs in the setting of other vulvar inflammatory conditions such as lichen sclerosis. Differentiated VIN carries a higher risk of progression to invasive malignancy. Extramammary Paget disease is a rare intraepithelial adenocarcinoma unrelated to uVIN and dVIN, although management is similar. Conclusions and Relevance Vulvar intraepithelial neoplasia is a preinvasive neoplasia of the vulva with few robust strategies for surveillance or management. Careful examination with targeted biopsy is warranted for suspicious lesions, and a combination of surgical and medical management can be tailored for individual patient needs. Target Audience Obstetricians and gynecologists, family physicians Learning Objectives After reading this article, the learner should be better able to evaluate the epidemiology and pathophysiology of VIN; assess risk for underlying malignancy, especially when comparing uVIN and dVIN; and compare different options for management of VIN including medical and surgical treatments.