Predicting Ovarian Malignancy based on Transvaginal Ultrasound Images using Deep Neural Networks

Abstract: Ovarian cancer is the most lethal gynaecological malignancy; however, ovarian lesions are very common and only around 1% are malignant. Due to the large number of cases, patients are triaged by gynaecologists having a high variability in diagnostic accuracy. The aim of this study is to train and validate deep neural networks and, by comparison to subjective expert assessment, determine their potential in the triage of patients with ovarian tumours. We used a transfer learning approach on pre-trained networks (VGG16, ResNet50, MobileNet), and a post-processing calibration to better align their confidence scores with the true certainty of their predictions. Our dataset contained 3077 transvaginal ultrasound images from 758 patients with ovarian tumours, where histological outcome from surgery or long-time follow-up (> 3 years) served as diagnostic ground truth. From our dataset, 150 cases (75 benign, 75 malignant), each containing 3 images, were held out for testing, while the remaining cases were used for training and model selection. The models were assessed bases on sensitivity, specificity, and AUC, along with their corresponding 95% confidence intervals. On the test set, our final model had a sensitivity of 96.0% (0.897–0.989), specificity of 86.7% (0.776–0.929), and AUC of 0.950 (0.906–0.985). When excluding the 12.7% (0.073–0.180) of cases most difficult to classify (based on the confidence score of the model output), our model had a sensitivity of 97.1% (0.909–0.994), specificity of 93.7% (0.856–0.978), and AUC of 0.958 (0.911–0.993). As comparison, the subjective expert assessment had a sensitivity and specificity of 96.0% and 88.0% respectively. We show that neural networks can be used to predict ovarian malignancy with high diagnostic accuracy, comparable to that of human experts, and thus have potential in the triage of patients with ovarian tumours.