Ultrasound Provides a Faster, More Effective Ovarian Cancer Diagnosis

It is crucial to differentiate between benign and malignant ovarian cysts and tumors in a fast and reliable way, thus improving the surgical management and prognosis of the patient. A Belgian scientist has proven in a recent study that ultrasound-based simple rules may effectively differentiate between malignant and benign tumors.

The research was led by Prof. Dirk Timmerman, clinical head of gynecology at the University Hospitals Gasthuisberg Campus (Belgium). Ovarian cancer a malignant tumor in the ovaries is the most lethal female malignancy because in many instances, the disease has few symptoms. Consequently, it is frequently only diagnosed once it has reached a very advanced stage. Treatment of ovarian cancer consists of laparotomy, possibly preceded by a course of chemotherapy. A laparotomy is an invasive open operation that involves making a large incision and removing all the tumor tissue. Most cysts in the ovaries are benign, however, and they may spontaneously disappear. In instances where an operation does prove necessary, laparoscopy or keyhole surgery offers many advantages. This involves removing only the cyst itself, via very small incisions.

The ultrasound examination imaging with ultrasound sound waves is crucial, Prof. Timmerman explained, "It is not always easy to characterize ovarian tumors, but a reliable preoperative diagnosis is very important. When operating on a malignant tumor, the cystic contents should not be spilled, because spread of the cancer cells in the abdomen clearly decreases the patient's chances of survival. Conversely, when it comes to benign tumors, especially in younger women who wish to bear children it is important to avoid unnecessary operations that may reduce their fertility and may result in a large abdominal scar and a longer stay in hospital.”

Prof. Timmerman is the coordinator of the International Ovarian Tumor Analysis Group (IOTA), an international cooperation between ultrasound centers in various countries with medical doctors and an interdisciplinary team of engineers and biostatisticians at the department of electrical engineering, ESAT-SCD, Katholieke Universiteit Leuven (Belgium). Together with Dr. Lieveke Ameye and Prof. Sabine Van Huffel of ESAT-SCD, Prof. Timmerman has developed a set of rules to distinguish between benign and malignant tumors with ultrasound examination. "We needed a simple and reliable triage method,” Prof. Timmerman explained. "Existing tests were either expensive and time-consuming, or unreliable. There are mathematical models you can use to distinguish between different types of tumors, but computers are not always available during the ultrasound examination, especially not in developing countries. Moreover, in many places, ultrasound examination of the ovaries is not performed by specialized medical doctors or very experienced examiners.”

The simple ultrasound rules include external characteristics of the tumor: the irregular and solid form, diameter, papillations, internal cyst walls, blood flow, and calcifications. If a cyst has one of the five features of a benign tumor or one of the five features of a malignant tumor, it is classified in one of the two respective categories. The procedure has been tested on nearly 2,000 women who subsequently underwent surgery. In 77% of the cases, the rules proved applicable and research of the removed tumor demonstrated that the rules were very reliable for predicting whether the tumors were benign or malignant. In other cases if there are none of these features or if both benign and malignant features are present an examination by an experienced ultrasound examiner is the best method.

Hundreds of thousands of cases of ovarian cancer are diagnosed yearly worldwide. As the symptoms of ovarian cancer are not specific, it is important, according to the researchers, to plan a thorough examination if there are any symptoms, such as abdominal swelling, loss of appetite, pain, or abnormal bleeding.

Related Links:
University Hospitals Gasthuisberg
Katholieke Universiteit Leuven