Knowledge-Based Analysis for Mortality Prediction From CT Images

Low-Dose CT (LDCT) can significantly improve the accuracy of lung cancer diagnosis and thus reduce cancer deaths compared to chest X-ray. The lung cancer risk population is also at high risk of other deadly diseases, for instance, cardiovascular diseases. Therefore, predicting the all-cause mortality risks of this population is of great importance. This paper introduces a knowledge-based analytical method using deep convolutional neural network (CNN) for all-cause mortality prediction. The underlying approach combines structural image features extracted from CNNs, based on LDCT volume at different scales, and clinical knowledge obtained from quantitative measurements, to predict the mortality risk of lung cancer screening subjects. The proposed method is referred as Knowledge-based Analysis of Mortality Prediction Network (KAMP-Net). It constitutes a collaborative framework that utilizes both imaging features and anatomical information, instead of completely relying on automatic feature extraction. Our work demonstrates the feasibility of incorporating quantitative clinical measurements to assist CNNs in all-cause mortality prediction from chest LDCT images. The results of this study confirm that radiologist defined features can complement CNNs in performance improvement. The experiments demonstrate that KAMP-Net can achieve a superior performance when compared to other methods.

Authors

• Hengtao Guo
• Uwe Kruger
• Ge Wang University of Science and Technology Beijing (USTB)
• Mannudeep K. Kalra
• Pingkun Yan Rensselaer Polytechnic Institute

Keywords

• Feature extraction
• Lung
• Cancer
• Computed tomography
• Biomedical measurement
• Deep learning
• Predictor Of Mortality
• Knowledge-based Analysis
• Mortality Rate
• Neural Network
• Lung Cancer
• Risk Of Mortality
• Quantitative Measures
• Convolutional Neural Network
• Deep Neural Network
• Image Features
• Clinical Measures
• Deep Convolutional Neural Network
• Clinical Knowledge
• Low-dose Computed Tomography
• Lung Cancer Screening
• Neural Network Prediction
• Support Vector Machine
• Automatic Method
• Support Vector Machine Classifier
• Coronary Artery Calcium
• Support Vector Machine Model
• Cardiac Patches
• Coronary Artery Calcium Score
• Left Anterior Descending
• Combined Probability
• Fully-connected Layer
• Hounsfield Units
• Training Loss
• Left Coronary Artery
• low-dose CT
• mortality risk
• machine learning and deep learning
• Early Detection of Cancer
• Humans
• Image Processing, Computer-Assisted
• Lung Neoplasms
• Tomography, X-Ray Computed