A Radiomics approach to distinguish benign from malignant nodules on Low Dose CT
We hypothesize that our proposed technology can enable improved discrimination between benign versus malignant lung nodules than has been achievable by clinical and radiomic analysis previously. Additionally, we believe that the machine learning classifier developed as part of this project will have direct clinical utility for lung cancer screening and could serve as a decision support tool in conjunction with screening chest CT scans.
Training the machine learning classifier: The most discriminative radiomic features will be determined via cross-validation and Linear/Quadratic Discriminant Analysis (L/QDA) on the learning set. To mitigate selection and classifier training bias, a three-fold (one fold held-out for testing), patient-stratified, cross-validation scheme will be used and both classification selection strategies will be repeated 1000 times. The sensitivity and the specificity of each feature will be evaluated by Receiver operating characteristic (ROC) curve and AUC analysis.
1) Optimize a machine learning classifiers with radiomic features of nodular morphology on non-contrast lung CT to distinguish benign from malignant nodules.
2) Validate the machine learning classifier developed in Aim 1 to distinguish benign from malignant nodules on an independent test set
Dr. Vamsidhar Velcheti, MD - Taussig Cancer institute, Cleveland Clinic.
Dr. Robert Gilkeson, MD - University Hospitals, Cleveland
Dr. Philip Linden, MD - University Hospitals, Cleveland
Dr. Frank Jacono, MD - Louis Stokes Cleveland VA Medical Center, Cleveland
Dr. Michael Yang, MD - University Hospitals, Cleveland