Yi Xu

Ph.D.

Shanghai Jiao Tong University

Associate Professor

NLST (Learn more about this study)

NLST-517

May 31, 2019

Pulmonary nodule diagnosis, follow-up recommendation and lung cancer prognosis

Lung cancer is the deadliest type of cancer worldwide, posing a serious threat to human life. Early lung cancer is difficult to screen due to its fewer clinical symptoms, so lung nodule detection and diagnosis based on CT image analysis has become a crucial method of early diagnosis of lung cancer. However, lung cancer screening via CT images is a heavy, repetitive thus error-prone task for radiologists. So we hope to develop a nodule detection and diagnosis model leveraging the large amount of annotated NLST CT images at first and get a reliable diagnosis result.
In the second step, if a nodule is detected but not diagnosed with cancer, we will give follow-up recommendations according to its specific growth stages (volume, density, image features, etc.). To achieve this, we will explore a nodule growth model based on three-year follow-up CT images. On the other hand, if a nodule is diagnosed with cancer, we will utilize CT images and patient characteristics to develop a prognosis prediction model. A relatively accurate survival prediction will be given and we hope to improve treatment decision via this model.
In brief, early lung cancer screening, pulmonary nodule follow-up and lung cancer prognosis have significant clinical meanings. Our deep learning-based study will follow such clinical process to lighten the workload of radiologists and reduce the mortality of lung cancer.