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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
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