Developing a prognostic prediction model using Delta Radiomic Features and Clinical Information for Lung Cancer Detection
1) A machine learning model combining delta radiomic features with clinical information for lung cancer detection will be developed. AUROC, accuracy, specification and sensitivity will be reported. We aim to achieve this objective by applying deep learning methods. We hypothesize that the features to be extracted should present:1) nodules’ several attributes (nodule malignancy, size, speculation and etc..) and other clustering futures. 2) lung masses or more complicated tissue. 3) emphysema characteristics.
2) Aggregate the above features and construct a patient-level prediction model for lung cancer detection. Patients’ clinical characteristics will be added in this phase.
Xiaofeng Wang, Cleveland Clinic
Xiaozhen Han, Cleveland Clinic
Xinge Ji, Cleveland Clinic
Yige Sun, Case Western Reserve University