New Model Predicts Risk of Deep Vein Thrombosis in Patients with Epithelial Ovarian Cancer

Although ovarian cancer is not among the most frequently diagnosed cancers, especially when compared with breast or lung cancer, it remains a serious condition. It ranks as the fifth-leading cause of cancer-related deaths among women and tends to become more prevalent with increasing age, particularly beyond 65. Epithelial ovarian cancer makes up over 90% of all ovarian cancer cases. Its symptoms, especially in the early stages, are difficult to recognize and often go unnoticed, which partly explains its high mortality rate. As the disease advances, typical symptoms include abdominal bloating and a decreased appetite—indicators that can often be misattributed to less serious health issues. Due to the nonspecific nature of these symptoms, most patients receive their diagnosis at a later stage, greatly limiting treatment options and leading to unfavorable outcomes.

The current standard of care for this type of cancer typically involves surgery followed by chemotherapy. However, these treatments can increase the likelihood of postoperative complications, which in turn can negatively affect patient survival. A major concern is the development of deep vein thrombosis, a condition in which abnormal blood clots form in deep veins. If untreated, these clots may break off and travel to the lungs, resulting in a pulmonary embolism—a life-threatening condition marked by difficulty in breathing, reduced oxygen levels, and potential respiratory failure.

Early identification of risk factors for deep vein thrombosis is therefore vital for prevention. Nomograms are widely recognized as dependable predictive tools that simplify statistical models, offering a way to support personalized treatment strategies and preventative care for different health conditions. In a new study, researchers at Wenzhou Central Hospital (Zhejiang Province, China) have developed and validated a nomogram specifically designed to predict the risk of deep vein thrombosis in patients with epithelial ovarian cancer.

Nomograms, functioning as predictive models, convert complex statistical data into clear graphical representations that produce individual numerical probabilities for clinical events. These tools are valuable in delivering tailored treatment and guiding clinical decisions for preventive strategies. However, there has been limited research on applying nomograms to predict deep vein thrombosis risk among patients with epithelial ovarian cancer.

This gap prompted researchers to conduct a study to create and test a nomogram suited for this purpose. They monitored 429 patients, of whom 116 (27%) developed deep vein thrombosis. The study, published in the journal Menopause, identified several independent risk factors significantly associated with this condition: age, body mass index, hypertriglyceridemia, tumor stage, tumor grade, CA125 level, platelet count, and fibrinogen level. The resulting nomogram, built using these parameters, showed strong predictive capability and clinical relevance in estimating the risk of deep vein thrombosis within this specific group of patients.

“Ovarian cancer is often diagnosed at a late stage and can be extremely aggressive, requiring extensive surgery and long courses of chemotherapy,” said Dr. Monica Christmas, associate medical director for The Menopause Society. "Although these treatments reduce cancer burden, they are associated with significant risks. Identifying strategies and protocols to minimize or prevent treatment-related complications is essential to optimizing patient outcomes and quality of life.”