Ovarian Cancer Awareness: Risk Factors and Screening Techniques

There’s nothing lighthearted about ovarian cancer.

Ovarian cancer is often referred to as a ‘silent killer’ because it is usually diagnosed at an advanced stage, when treatment is less likely to result in a complete cure and full recovery.

Why is a reproductive endocrinology and infertility (REI) specialist discussing ovarian cancer? While this disease most commonly affects postmenopausal women over the age of 60 who have completed childbearing, about 10% of cases occur in women under 45, during their reproductive years. This makes ovarian cancer a highly relevant concern within my field.

Although the exact causes of ovarian cancer remain unclear, in women of reproductive age, it is often linked to genetic mutations such as BRCA1, BRCA2, or Lynch syndrome. Other contributing factors may include conditions like endometriosis (particularly endometriomas, where endometrial tissue grows within the ovary), or a family history of ovarian, breast, or colorectal cancer, even in the absence of a confirmed genetic mutation.

There is a common misconception that fertility treatments cause ovarian cancer; however, this is not supported by evidence. It’s important to clarify that women undergoing fertility treatments often have underlying conditions such as endometriosis, which are independently associated with an increased risk of ovarian cancer. The link is one of association, not causation. In fact, ovarian cancer is occasionally first detected by reproductive endocrinology and infertility (REI) specialists during the course of evaluating or treating infertility.

If you have a strong family history of cancer, talk to your doctor about genetic counseling and start early surveillance.

So, how should we approach surveillance for ovarian cancer? Pelvic exams alone are limited in sensitivity and often cannot detect ovarian masses smaller than 5 cm, even in experienced hands. While serum markers such as CA-125, CA 19-9, CA 72-4, CA 15-3, HE4 (human epididymis protein 4), and CEA (carcinoembryonic antigen) are more specific to malignancy, they are not all specific to ovarian cancer and are typically only ordered after a mass has already been identified. These markers are not routinely used in serial testing for early detection.

In contrast, imaging, particularly transvaginal ultrasound with Doppler flow analysis, can detect even small ovarian abnormalities and raise early suspicion for malignancy. When performed regularly in reproductive-age women at risk, ultrasound may aid in detecting ovarian cancer in its earliest stages, when it remains confined to the ovary and before local or distant spread occurs.

Why, then, are physicians hesitant to adopt ultrasound for early ovarian cancer detection? First, from a financial standpoint, performing annual ultrasounds on all women of reproductive age is not cost-effective. Second, because ovarian cancer is relatively rare in this population, the low incidence reduces the test’s sensitivity and positive predictive value, ultimately limiting its effectiveness as a widespread screening tool.

Still, it is essential for physicians to recognize when an ovarian lesion displays features suggestive of malignancy. Two diagnostic tools have significantly advanced the role of ultrasound in evaluating ovarian conditions: the International Ovarian Tumor Analysis (IOTA) group, established in 1999, and the Ovarian-Adnexal Reporting and Data System (O-RADS), introduced in 2021. Both systems provide structured frameworks for assessing and scoring ultrasound characteristics of ovarian lesions, offering a more objective and standardized interpretation.

When an ultrasound-detected lesion raises suspicion for malignancy, further imaging, such as CT or MRI, can offer additional detail, help identify local or distant spread, and support initial staging to guide surgical planning.

As a reproductive endocrinologist, I feel a strong responsibility to support early detection during initial ultrasounds. Ongoing ultrasound surveillance empowers women to take an active role in advocating for their health.

September is Ovarian Cancer Awareness Month, but awareness should be year-round. Speak up about symptoms, intensify surveillance, support research, donate, or simply share this post, as every action counts.

Ovarian cancer may be elusive, but knowledge empowers, and imaging provides proof. Advocate for your health. Support the women in your life. Early detection saves lives, and awareness is the first step.

Laura Detti, MD, is a Professor of Obstetrics and Gynecology, the Division and Fellowship Director of Reproductive Endocrinology and Infertility at Baylor College of Medicine, and Chief of Reproductive Endocrinology Services at the Pavilion for Women at Texas Children’s Hospital. She is also a leader of the AIUM’s Gynecologic Ultrasound Community.

Portrait of Laura Detti, MD, a reproductive endocrinologist, wearing a white lab coat with badges from Baylor College of Medicine and Texas Children's Hospital.

Mastering Ovarian Tumor Analysis: Join the AIUM-IOTA Partnership Course for Advanced Gynecologic Ultrasound

Ovarian lesions are a common finding among women, with etiologies ranging from ovarian changes related to normal hormonal function to aggressive malignancies. Therefore, the proper diagnosis and management of ovarian lesions are critical to women’s health. Here, I’ll give a brief description of ovarian tumor analysis, including descriptors, pattern recognition, and the application of the International Ovarian Tumor Analysis (IOTA) group’s Simple Rules, the IOTA ADNEX model, and O-RADS ultrasound characterization.

An ultrasound image of ovarian lesions.

Descriptive Analysis of Ovarian Tumors

The first step in the diagnosis of ovarian tumors is descriptive analysis. This step involves a detailed examination of the tumor’s characteristics, including its size, shape, texture, and location. This information is obtained through various imaging techniques, such as ultrasound, MRI, and CT scans. The following descriptors are used in descriptive analysis:

  • Size: The size of the tumor is measured in centimeters and is one of the critical factors in determining the type of tumor.
  • Shape: The shape of the tumor is described as either round or irregular. An irregular shape is often associated with malignant tumors.
  • Texture: The texture of the tumor is classified as either solid, cystic, or mixed.
  • Location: The location of the tumor is described as either unilateral or bilateral. Unilateral tumors are located on one ovary, while bilateral tumors are located on both ovaries.

Pattern Recognition of Ovarian Tumors

An essential aspect of ovarian tumor analysis is pattern recognition. It involves identifying specific patterns associated with malignant and benign tumors. The following patterns are commonly observed in ovarian tumors:

  • Solid: Solid tumors are characterized by the absence of cystic components and are often associated with malignancy.
  • Cystic: Cystic tumors are characterized by the presence of fluid-filled spaces and are typically benign.
  • Mixed: Mixed tumors have both solid and cystic components and can be either benign or malignant.

Application of the Simple Rules, the IOTA ADNEX Model, and O-RADS Ultrasound Characterization

The Simple Rules, the IOTA ADNEX Model, and O-RADS ultrasound characterization are 3 widely used methods for differentiating ovarian tumors.

  • The Simple Rules: The Simple Rules are a set of guidelines that assist in the diagnosis of ovarian tumors. The rules are based on the tumor’s size, shape, texture, and location. According to the Simple Rules, a tumor is considered benign if it meets all 3 of the following criteria: 1) it is purely cystic, 2) it is less than 10 cm in size, and 3) it has a thin, smooth wall.
  • IOTA ADNEX Model: The IOTA ADNEX Model is a predictive model that uses a combination of clinical and ultrasound findings to diagnose ovarian tumors. The model considers the tumor’s size, shape, texture, location, and other factors, such as the patient’s age and menopausal status. Then, the model provides a probability score for each tumor, indicating the likelihood of malignancy.
  • O-RADS Ultrasound Characterization: O-RADS is a standardized ultrasound reporting system that categorizes ovarian tumors based on their likelihood of malignancy. The system uses a 5-point scale, ranging from 1 (very low risk) to 5 (very high risk). The O-RADS system considers the tumor’s size, shape, texture, location, and vascularity.

The proper diagnosis and management of ovarian lesions are critical to women’s health. Descriptive analysis, pattern recognition, and the application of the Simple Rules, the IOTA ADNEX Model, and O-RADS ultrasound characterization are essential aspects of ovarian tumor analysis. These methods aid in accurately diagnosing and differentiating ovarian tumors and can guide appropriate treatment decisions.

Are you a healthcare professional looking to enhance your skills in gynecologic ultrasound and ovarian tumor analysis? Look no further than the Advanced Gynecologic Ultrasound course offered by the American Institute of Ultrasound in Medicine (AIUM) in partnership with the International Ovarian Tumor Analysis (IOTA) group.

This course offers a unique and valuable opportunity for healthcare professionals looking to enhance their skills in gynecologic ultrasound and ovarian tumor analysis. The comprehensive curriculum, hands-on training, and networking opportunities make it a worthwhile investment for healthcare professionals looking to improve patient outcomes and advance their careers. Register now for the course, taking place this June, at the AIUM Headquarters in Laurel, Maryland.

Sources
https://www.cancer.org/cancer/types/ovarian-cancer/about/what-is-ovarian-cancer.html
https://acsjournals.onlinelibrary.wiley.com/doi/pdf/10.1002/cncr.11339
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5620878/
https://pubmed.ncbi.nlm.nih.gov/18504770/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4402441/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728190/
https://www.mdpi.com/2075-4418/13/5/885

Arian Tyler, BS, is the Digital Media and Communications Coordinator for the American Institute of Ultrasound in Medicine (AIUM).