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 today in the British Medical Journal, an international team led by Imperial College London and KU Leuven, 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 gynaecologists at the International Society for Ultrasound in Obstetrics and Gynecology World Congress in Barcelona last month. The authors of the study say doctors could start using ADNEX straight away.
Professor Tom Bourne, from the Department of Surgery and Cancer at Imperial College London, said: “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.
“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 gynaecological cancer surgeon”
“At the moment, the way we assess women with ovarian cysts for the presence of cancer and select treatment lacks accuracy. 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.
Studies have shown that ovarian cancer patients have a better chance of survival if they are referred to a specialized gynaecological cancer unit, but this only happens for a minority of women in Europe and the US at present.
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 don’t sub-classify malignant tumors. The ADNEX model can discriminate between benign, borderline, stage I invasive, stage II-IV invasive, and secondary metastatic tumors.
The study was funded by the Flemish Government: Research Foundation — Flanders (FWO), Flanders’ Agency for Innovation by Science and Technology (IWT), iMinds and the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre.
Story Source:
The above story is based on materials provided by Imperial College London. Note: Materials may be edited for content and length.
Journal Reference:
- B. Van Calster, K. Van Hoorde, L. Valentin, A. C. Testa, D. Fischerova, C. Van Holsbeke, L. Savelli, D. Franchi, E. Epstein, J. Kaijser, V. Van Belle, A. Czekierdowski, S. Guerriero, R. Fruscio, C. Lanzani, F. Scala, T. Bourne, D. Timmerman. Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study. BMJ, 2014; 349 (oct07 3): g5920 DOI: 10.1136/bmj.g5920