team 2: A Deep convolutional neural network for identifying thyroid cancer

Differentiating benign thyroid nodules from cancer is a significant health challenge, as thyroid nodules are present by ultrasound (US) in a staggering 19 – 35% of the general adult population (approximately 130 million people).  Most of these nodules are benign, however the diagnostic process is costly, inefficient, and leads to unnecessary surgery and morbidity. We propose to develop a more accurate system for identifying thyroid cancer by applying a deep convolutional neural network (DCNN) algorithm to data from our thyroid cancer database, which includes a large number patients who have been followed clinically for several years. We propose to develop a new risk prediction and prognosis tool to predict the individualized risk of death and recurrence from thyroid cancer. We envision two modes of analysis; 1) exploratory analysis, and 2) clinical performance optimization. In the exploratory mode we will leverage advanced unsupervised machine learning techniques such as manifold learning and dimension reduction (DR) to visualize multi-modal, integrated, high-dimensional data (as a point cloud). We hope to build an interactive, GUI-based data visualization dashboard to allow rapid traversal of this complex data by scientists and clinicians alike. Additionally, we will incorporate and overlay the proposed predictive models as they are developed and refined, and provide the ability to annotate data/models on the fly for subsequent use with other models. In the second mode, performance optimization, we will build on the recognition patterns discovered in the augmented data exploration, and then refine and optimize specific candidate predictive models emphasizing those with high potential for clinical impact/utility. The most high-performing predictive models will then be validated retrospectively and prospectively. At the conclusion of this project, we will have a deep learning tool that has improved ability to detect thyroid cancer in patients with thyroid nodules. We also anticipate that we will have a risk prediction tool that accurately identifies the risk of recurrence and death after initial treatment of thyroid cancer. Through this process, we will have established one of the first comprehensive annotated databases of thyroid ultrasound images that will allow development and validation of a deep learning algorithm to accurately differentiate benign from malignant thyroid nodules.

Team Lead: Fiemu Nwariaku, MD, Surgery, https://www.utsouthwestern.edu/labs/nwariaku/