Deep Learning for Prediction of Progression and Overall Survival of Lung Cancer using CT Examinations
We propose a two-stage deep learning methodology to predict progression and overall survival of patients with malignant lung lesions using CT imaging examinations. The first stage is a convolutional neural net (CNN) that will be used to make binary predictions for progression and to extract deeper radiographic features from the CT images. We will utilize the recently developed CNN architecture EfficientNet, which was constructed by systematically up-scaling existing CNNs by width, depth, and resolution. The second stage is a Random Survival Forest (RSF) model using the extracted radiographic features along with clinical features to predict overall survival and calculate risk scores for each patient. Both these approaches have been individually validated in other settings; CNNs have been previously used to successfully classify lung lesions as either malignant or benign and RSFs have been used to model overall survival times in severe coronary artery disease. This is the first study examining progression prediction for malignant lung lesions using a CNN with CT images and subsequently utilizing a RSF with the CNN extracted features to predict overall survival. This work would provide a novel method of precisely assessing progression risk and overall survival for patients with lung cancer. In addition, results obtained using this methodology will provide a basis for incorporation of similar machine learning approaches in other non-classification tasks that involve continuous outcomes.
Aim 1: Develop a Convolutional Neural Network (CNN) to accurately generate binary progression predictions from CT imaging for patients with detected malignant lung lesions.
Aim 2: Extract deeper features from CT imaging using the CNN and utilize them to develop a Random Survival Forest model to model overall survival for patients.
Gang Cheng, M.D., University of Pennsylvania
Joseph Mammarappallil, M.D., Ph.D., Duke University
Yi Li, M.D., M.Sc, Fox Chase Cancer Center