title: Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma.
abstract: Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short- and long-term survivors. We experimented with several pretrained CNNs and several feature selection strategies. The best previously reported accuracy when using traditional quantitative features was 77.5% (area under the curve [AUC], 0.712), which was achieved by a decision tree classifier. The best reported accuracy from transfer learning and deep features was 77.5% (AUC, 0.713) using a decision tree classifier. When extracted deep neural network features were combined with traditional quantitative features, we obtained an accuracy of 90% (AUC, 0.935) with the 5 best post-rectified linear unit features extracted from a vgg-f pretrained CNN and the 5 best traditional features. The best results were achieved with the symmetric uncertainty feature ranking algorithm followed by a random forests classifier.
authors.affiliation: Department of Computer Science and Engineering, University of South Florida, Tampa, Florida.
authors.forename: Rahul
authors.initials: R
authors.lastname: Paul
authors.affiliation: Department of Computer Science and Engineering, University of South Florida, Tampa, Florida.
authors.forename: Samuel H
authors.initials: SH
authors.lastname: Hawkins
authors.affiliation: Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.
authors.forename: Yoganand
authors.initials: Y
authors.lastname: Balagurunathan
authors.affiliation: Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida; Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.
authors.forename: Matthew B
authors.initials: MB
authors.lastname: Schabath
authors.affiliation: Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Gillies
authors.affiliation: Department of Computer Science and Engineering, University of South Florida, Tampa, Florida.
authors.forename: Lawrence O
authors.initials: LO
authors.lastname: Hall
authors.affiliation: Department of Computer Science and Engineering, University of South Florida, Tampa, Florida.
authors.forename: Dmitry B
authors.initials: DB
authors.lastname: Goldgof
keywords: adenocarcinoma; computed tomography; deep features; deep neural network; lung cancer; pre-trained CNN; symmetric uncertainty; transfer learning