Reduction of unnecessary thyroid biopsies using deep learning

Thyroid nodules are extremely common lesions and highly detectable by ultrasound (US). Several studies have shown that the overall incidence of papillary thyroid cancer in patients with nodules selected for biopsy is only about 10%. Therefore, there is a clinical need for a dramatic reduction of thyroid biopsies. In this study, we present a guided classification system using deep learning that predicts malignancy of nodules from B-mode US. We retrospectively collected transverse and longitudinal images of 150 benign and 150 malignant thyroid nodules with biopsy proven results. We divided our dataset into training (n=460), validation(n=40), and test (n=100) datasets. We manually segmented nodules from B-mode US images and provided the nodule mask as a second input channel to the convolutional neural network (CNN) for increasing the attention of nodule regions in images. We evaluated the classification performance of different CNN architectures such as Inception and Resnet50 CNN architectures with different input images. The InceptionV3 model showed the best performance on the test dataset: 86% (sensitivity), 90% (specificity), and 90% precision when the threshold was set for highest accuracy. When the threshold was set for maximum sensitivity (0 missed cancers), the ROC curve suggests the number of biopsies may be reduced by 52% without missing patients with malignant thyroid nodules. We anticipate that this performance can be further improved with including more patients and the information from other ultrasound modalities.