Individual lung cancer survival estimation based on radiomics analysis
Principal Investigator
Name
Yujiao Wu
Degrees
Ph.D
Institution
University of Technology Sydney
Position Title
Ph.D
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-596
Initial CDAS Request Approval
Oct 31, 2019
Title
Individual lung cancer survival estimation based on radiomics analysis
Summary
When a patient dies in the hospital through a long treatment with painful side effects and very high financial cost, both the patients and their families suffer a lot during this period. We might ask: could it have been avoided, what if the clinicians acted differently? This is impossible to know because we cannot go back in time. Nonetheless, if we know the result in advance, we may act differently so that the decision based on estimated survival time can better improve the patients’ health care. The goal of this project is trying to leverage the recent deep learning techniques to facilitate the development in the lung cancer survival estimation. The hypothesis is that deep learning algorithm can train a model that can extract additional information beyond our experts and this information can serve for the survival estimation. The national lung cancer screening trial (NLST) dataset provides an unparalleled resource about patients with diagnosis images, clinic data, treatment, nodule findings, demographics, and other patient data. ‘These data can be used to generate a knowledgeable data mining system so that the sophisticated personalized diagnostic decision-making process is possible.
Aims
An individual survival estimation system is developed by using the NLST dataset.
Collaborators
Xiaoshui Huang, Xiaoshui.huang@sydney.edu.au
Tianqi Chen, ctqctqctq@126.com