New test helps in choosing best treatment for ovarian cancer

Researchers have devised a new test to help doctors diagnose ovarian tumors and choose the most appropriate treatment.

Successful treatment depends in part on accurately identifying the type of tumor, but this can be difficult. As a result, many women with cancer are not sent to the right specialist surgeon, or those with a benign cyst may have a more serious operation than they need.

In a study published in the British Medical Journal (2014; doi:10.1136/bmj.g5920), an international team led by Imperial College London in the United Kingdom and KU Leuven in Belgium describe a new test, called ADNEX, which can discriminate between benign and malignant tumors, and identify different types of malignant tumor, with a high level of accuracy.

The test is based on the patient's clinical information, a simple tumor marker blood test, and features that can be identified on an ultrasound scan. As well as identifying the type of tumor, the test expresses the confidence of the diagnosis as a percentage.

Doctors can use the test in a clinical database or by entering the patient's details into a smartphone app, which was demonstrated to gynecologists at the International Society for Ultrasound in Obstetrics and Gynecology World Congress in Barcelona. The authors of the study say doctors could start using ADNEX straight away.

"It's very important to get the preoperative diagnosis right. If it isn't right, the patient might have a more extensive operation than they need, for example having an ovary removed unnecessarily,” said Professor Tom Bourne, MD, PhD, from the Department of Surgery and Cancer at Imperial College London.

"If a tumor is benign, a woman might not need any treatment at all. If it is malignant, you need to know what type of tumor it is to choose the best treatment and that treatment needs to be carried out by specialist gynecological cancer surgeon," said Bourne.

Bourne explained that accuracy is lacking in the current way women with ovarian cysts are assessed for the presence of cancer and treatments are selected. “This new approach to classifying ovarian tumors can help doctors make the right management decisions, which will improve the outcome for women with cancer. It will also reduce the likelihood of women with all types of cysts having excessive or unnecessary treatment that may impact on their fertility."

The researchers developed the test using data from 3,506 patients from 10 European countries from 1999 to 2007, looking at which information available before the operation could be used to predict the diagnosis. They then tested the model on a further 2,403 patients between 2009 and 2012.

Apart from the tumor type, the choice of treatment sometimes has to take into account implications for the woman's fertility.

Existing prediction models discriminate between benign and malignant tumors but lack accuracy and do not subclassify malignant tumors. The ADNEX model can discriminate between benign, borderline, stage I invasive, stage II-IV invasive, and secondary metastatic tumors.

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