Radiomics-Driven Predictive Lung Cancer Models
Principal Investigator
Name
Patrice Essien
Degrees
MS
Institution
George Washington University
Position Title
Diagnostic Medical Physicist
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1347
Initial CDAS Request Approval
Nov 5, 2024
Title
Radiomics-Driven Predictive Lung Cancer Models
Summary
The core contribution of this work is to extract radiomics features that capture intricate details of tumor textures and microenvironment interactions from the dataset. These features are to be utilized to develop a predictive model with the goal of achieving at least 95% accuracy in distinguishing between malignant and benign tumors.
Aims
-Identifying which radiomics features most significantly impact the accuracy and predictive power of lung cancer models
-Testing if radiomics-driven model achieve an accuracy of 95% or better
-Analyzing which machine learning algorithms are most effective for utilizing radiomics data in predicting patient-specific outcomes
Collaborators
Patrice Essien