Ovarian Cancer: ADNEX Tool Helps Distinguish Benign From Malignant Ovarian Cysts

Ben Van Calster PhD Department of Development and Regeneration KU Leuven, Herestraat Leuven, BelgiumMedicalResearch.com Interview with:
Ben Van Calster PhD
Department of Development and Regeneration
KU Leuven, Herestraat Leuven, Belgium

 

MedicalResearch: What is the background for this study? What are the main findings?

Dr. Van Calster: Ovarian cancer is a very common type of cancer among women, with over 200,000 new cases per year worldwide. It is the most lethal of gynecological malignancies. Research has shown that the referral of patients with ovarian cancer to specialized gynecological oncologists in high volume centers improves survival. However, audits in Europe and the United States also show that only a minority of women with ovarian cancer are appropriately triaged to receive specialist care. In addition, different types of malignancies are not treated in the same way. Hence optimal personalized management of an ovarian tumor hinges on the detailed preoperative diagnosis of its nature. Unfortunately, current prediction models focused on the discrimination between benign and malignant tumors without further specification of the likely type of malignancy.

Various prediction models and rules have been developed to help predict whether an ovarian mass is benign or malignant. A recent systematic review meta-analysis has shown that the IOTA model LR2 and simple rules perform better than any other previous test. However none of these tests give anything other than a dichotomous outcome – i.e. cancer or non-cancer. In practice the position is more nuanced.

The ADNEX model estimates the likelihood that a tumor is benign, borderline malignant, stage I cancer, stage II-IV cancer, or secondary metastatic cancer. This model is the first that is able to differentiate between benign and these four subtypes of malignancy. To do so, ADNEX uses three clinical predictors (age, serum CA-125 level, and type of center), and six ultrasound characteristics of the tumor (maximum diameter of lesion, proportion of solid tissue, more than 10 cyst locules, number of papillary projections, acoustic shadows, and ascites). The model is based on data from almost 6,000 women recruited at 24 centers in 10 countries.

MedicalResearch: What should clinicians and patients take away from your report?

Dr. Van Calster: The message for clinician’s and patients is that ADNEX is a tool that can accurately say whether an ovarian cyst is benign or malignant and in addition sub-classify any type of malignancy. Patients who find they have an ovarian cyst might now be offered the possibility of the cyst being evaluated with ADNEX and then find themselves being informed with a high level of accuracy whether they need surgery and where that surgery should be carried out. By knowing the subtype of malignancy the possibility of less aggressive surgery may be an option – which may be particularly important in young women where fertility preservation is a critical issue. As the ADNEX model provides a more fine-tuned picture of an ovarian mass prior to surgery, the model has clear potential to improve patient triage and personalize management. We hope that in this way prediction made using ADNEX may positively influence survival and morbidity of patients with an ovarian tumor.

Initially what all patients and their physician want to know if an ovarian mass is found is whether it is malignant. To discriminate between benign and malignant masses without further differentiation, the Risk of Malignancy Index (RMI; Jacobs et al, BJOG 1990) is currently the most widely used tool. However our large multicenter databases demonstrate ADNEX represents a significant improvement on the RMI. If we examine the performance of the two tests, our paper reports a sensitivity of 96.5% and a specificity of 71.3% of ADNEX at a 10% risk cut-off for the overall risk of malignancy. In contrast using the same data and statistical methods, if we fix the sensitivity of RMI at 96.5% the specificity is 50.0%. Similarly, fixing the specificity of RMI at 71.3% results in a sensitivity of 86.2%.

MedicalResearch: What recommendations do you have for future research as a result of this study?

Dr. Van Calster: It is important that the performance of ADNEX is further monitored, and that the model is updated when newly collected datasets become available to ensure optimal performance. It is also important to keep searching for new (bio)markers that can further improve diagnostic performance in addition to the nine ADNEX predictors. ADNEX and other IOTA diagnostic models and rules have been developed on masses that have undergone surgery. Although we anticipate test performance will be maintained it is important to see what happens in the long term to masses classified as benign that are not selected for surgery. This is being addressed in the IOTA 5 study that has already recruited over 5000 new masses. Other areas of work include ascertaining whether pre-operative features of cancers may predict both operability and final outcome from the disease.

 Citation:

Van Calster Ben, Van Hoorde Kirsten, Valentin Lil, Testa Antonia C, Fischerova Daniela, Van Holsbeke Caroline et al. 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:g5920

 

 

 

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