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Principal Investigator
Name
Chang Chen
Institution
Shanghai Pulmonary Hospital, Tongji University School of Medicine
Position Title
Professor
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-707
Initial CDAS Request Approval
Sep 10, 2020
Title
A deep-learning signature of lesion detection, malignancy evaluation, risk stratification and management strategy for pulmonary nodules on computed tomography
Summary
Lung cancer is the leading cause of cancer-related death worldwide. Currently, with extensive application of low-dose computed tomography (CT), the detection of pulmonary nodules has experienced a remarkable increase. In such instances, accurate lesion detection and malignancy evaluation is of paramount importance for the optimal management strategy of pulmonary nodules. However, existing challenges in lung nodule diagnosis include inter-grader variability and high false-positive as well as false-negative rates, which pose a serious threat to public health. Therefore, we aim to develop a robust deep-learning signature of lesion detection, malignancy evaluation, risk stratification and management strategy for pulmonary nodules on computed tomography.
Two datasets, the NLST dataset based on the screening population and the surgery dataset from our center based on the treatment population, will be utilized for the model construction to potentiate the generalization and robustness of the deep-learning signature in the clinical scenarios. Furthermore, by comparing to two conventional clinical models for determining malignancy risk of pulmonary nodules (Lung-RADS Model and Brock Model), the diagnostic efficacy of the deep-learning signature will be further validated. Subsequently, we will investigate the value of the malignancy score generated from the deep-learning signature in prognosis stratification of pulmonary nodules. Still, on the basis of three-year follow-up CT images, a reliable growth model will be established to optimize the management strategy of pulmonary nodules.
In summary, our proposed deep-learning signature will provide instructive significance in individualized malignancy evaluation, prognosis stratification and management strategy decision-making for patients with indeterminate pulmonary nodules.
Aims

Aim #1: To develop a robust deep-learning signature of lesion detection and malignancy evaluation for pulmonary nodules.
Aim #2: To indicate the malignancy extent of the lung nodules based on the generated risk score from deep-learning signature and thus provide prognosis stratification for patients with indeterminate pulmonary nodules.
Aim #3: To establish a growth model based on three-year follow-up CT images and provide instructions for the optimal management strategy of pulmonary nodules.

Collaborators

Chang Chen, M.D., Ph.D., Shanghai Pulmonary Hospital, Tongji University School of Medicine
Jiajun Deng, M.D., Shanghai Pulmonary Hospital, Tongji University School of Medicine
Yifan Zhong, M.D., Shanghai Pulmonary Hospital, Tongji University School of Medicine
Jiancheng Yang, Ph.D., Shanghai Jiao Tong University
Shouyu Cheng, Ph.D., Tongji University