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Artificial Intelligence To Predict The Risk Of Malignancy In Lung Cancer By Low-Dose CT Screening Studies

Principal Investigator

Name
Christian Salvatore

Degrees
PhD

Institution
DeepTrace Technologies, spin-off of University School for Advanced Studies IUSS Pavia

Position Title
Researcher

Email
salvatore@deeptracetech.com

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-739

Initial CDAS Request Approval
Dec 21, 2020

Title
Artificial Intelligence To Predict The Risk Of Malignancy In Lung Cancer By Low-Dose CT Screening Studies

Summary
In this project, we will assess and compare the feasibility of deep-learning techniques for automatic lung and nodule segmentation, followed by radiomics and deep-learning approaches aimed at classifying the risk of malignancy in lung cancer by low-dose CT screening studies

Aims

* To assess and compare the feasibility of radiomics approaches in classifying the risk of malignancy in lung cancer by low-dose CT screening studies
* To assess and compare the feasibility of deep-learning approaches in classifying the risk of malignancy in lung cancer by low-dose CT screening studies
* To test deep-learning techniques for lung segmentation
* To test deep-learning techniques for nodule segmentation

Collaborators

Matteo Interlenghi