Paper Info
Title
Towards radiologist-level cancer risk assessment in CT lung screening using deep learning.
Abstract
Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined. Recently, it has been demonstrated that screening those at high-risk for lung cancer low-dose computed tomography (CT) of the chest can significantly reduce this death rate. The process of evaluating a chest CT scan involves the identification of nodules that are contained within a scan as well as the evaluation of the likelihood that a nodule is malignant based on its imaging characteristics. This has motivated researchers to develop image analysis research tools, such as nodule detectors and nodule classifiers that can assist radiologists to make accurate assessments of the patient cancer risk. In this work, we propose a two-stage framework that can assess the lung cancer risk associated with a low-dose chest CT scan. At the first stage, our framework employs a nodule detector; while in the second stage, we use both the image area around the nodules and nodule features as inputs to a neural network that estimates the malignancy risk of the whole CT scan. The proposed approach: (a) has better performance than the PanCan Risk Model, a widely accepted method for cancer malignancy assessment, achieving around 7% better Area Under Curve score in two independent datasets we have employed; (b) has comparable performance to radiologists in estimating cancer risk at patient level; (c) employs a novel multi-instance weakly-labeled approach to train the deep learning network that requires confirmed cancer diagnosis only at the patient level (not at the nodule level); and (d) employs a large number of lung CT scans (more than 8000) from heterogeneous data sources (NLST, LHMC, and Kaggle competition data) to validate and compare model performance. AUC scores for our model, evaluated against confirmed cancer diagnosis, range between 82% to 90%.
Year
Venue
Field
2018
arXiv: Computer Vision and Pattern Recognition
Lung cancer,Data set,Lung cancer screening,Computer science,Risk assessment,Malignancy,Radiology,National Lung Screening Trial,Cancer,Mortality rate
DocType
Volume
Citations 
Journal
abs/1804.01901
0
PageRank 
References 
Authors
0.34
0
13
Name
Order
Citations
PageRank
Stojan Trajanovski100.68
Dimitrios Mavroeidis200.68
Christine Leon Swisher331.07
Binyam Gebrekidan Gebre4486.12
Bas Veeling500.34
Rafael Wiemker624429.84
Tobias Klinder721622.50
Amir M. Tahmasebi8609.66
Shawn M. Regis900.68
Christoph Wald1043.23
Brady J. McKee1100.68
Heber MacMahon1220231.61
Homer Pien1381.29