Algorithm performs at expert level when distinguishing between benign and malignant ovarian tumors

Researchers recently developed a deep learning algorithm that can distinguish malignant from benign ovarian tumors on ultrasound with comparable performance to experts. 

When tested on 422 women with ovarian masses, the algorithm achieved a higher area under the receiver operating characteristic curve (AUC) for predicting malignancy than both experts and Ovarian-Adnexal Reporting and Data System (O-RADS). Those involved with the study suggested that these findings could be beneficial in the future of ovarian tumor assessment by providing clinical decision making support. 

Various deep learning applications have shown high diagnostic performance for the detection and classification of tumors based on computed tomography and magnetic resonance imaging, but few studies have analyzed their efficacy in ultrasound. The affordability and convenience of US has resulted in the modality often being the first choice to screen for ovarian cancer.  

Radiologist interpretations of ultrasound imaging do have known variability though, which is why the ACR developed O-RADS—to provide structured risk stratification guidelines. But even with this classification system, image interpretations remain at least somewhat subjective. For these reasons, researchers sought to understand how deep learning could play into the often arduous task of diagnosing ovarian cancer on ultrasound. 

“To date, there have been limited deep learning (DL) algorithms developed for assessing ovarian tumors, and most have been based on single-modal US images,” corresponding author Wei-Wei Feng, from the Department of Obstetrics and Gynecology at Ruijin Hospital in China, and co-authors explained. “In clinical practice, the diagnosis of ovarian cancer involves multiple US images, including gray scale, color Doppler, and power Doppler US images.”