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Study on the Evolution and Diagnostic Model of Early Stage Lung Cancer

Principal Investigator
Name
Ning Xiao
Degrees
Ph.D.
Institution
Shanxi University of Finance and Economics
Position Title
Mr
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-964
Initial CDAS Request Approval
Sep 29, 2022
Title
Study on the Evolution and Diagnostic Model of Early Stage Lung Cancer
Summary
The study on long-term lung cancer image data (complete progression of development of disease over time) is the key to early diagnosis of lung cancer. At present, the reveal of characteristics and laws of lung Cancer evolution over time is not comprehensive and the related course of disease of image data and evolution mechanism over time is not ambiguous, which cannot play a guiding role on early diagnosis of lung cancer. Aiming at this problem, the project intends to implement the following researches: the study on tracking and extraction of cross-period sequences of pulmonary lesions in long-term CT images, and to explore a deep learning method for lung cancer staging in which spatial multivariate features (deep and non-deep features) are combined under the key attributes of medical records. On this basis, a model of lesion growth and evolution based on long-term CT sequence images was proposed to reveal
the underlying mechanism for early identification of lung cancer. This study is expected to provide new technologies for large-scale early stage screening of lung cancer, which has important academic value and clinical value.
Aims

The study on tracking and extraction of cross-period sequences of pulmonary lesions in long-term CT images, and to explore a deep learning method for lung cancer staging in which spatial multivariate features (deep and non-deep features) are combined under the key attributes of medical records; the study on the dependent relationship of the 3D feature of lesions on the time dimension, and to explore the method of accurately tracing early sequence images of lung cancer in middle and late stage sequence images and the modeling mechanism of early lesion characteristics over time; the study on the relationship between long-term CT sequence image data and diagnostic information features
including time series and disease stages, and to explore the effectiveness of LSTM depth framework under time-incomplete data.

Collaborators

Yan Qiang, Taiyuan University of Technology
Juanjuan Zhao, Taiyuan University of Technology
Rui Hao, Shanxi University of Finance and Economics

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