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Sci Rep. 2018 Jun 18;8(1):9286. doi: 10.1038/s41598-018-27569-w.

Highly accurate model for prediction of lung nodule malignancy with CT scans.

Author information

1
Department of Computer Science, Arkansas State University, Jonesboro, Arkansas, 72467, United States of America.
2
The UALR/UAMS Joint Graduate Program in Bioinformatics, Little Rock, Arkansas, 72204, United States of America.
3
Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota, 55455, United States of America.
4
Department of Mathematics, University of California, Davis, California, 95616, United States of America.
5
Research Center for Management Science and Data Analytics, School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, China.
6
Mallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri, 63110, United States of America.
7
Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, 72205, United States of America. fwprior@uams.edu.
8
Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota, 55455, United States of America. zhangs@umn.edu.
9
Department of Computer Science, Arkansas State University, Jonesboro, Arkansas, 72467, United States of America. xhuang@astate.edu.
10
The UALR/UAMS Joint Graduate Program in Bioinformatics, Little Rock, Arkansas, 72204, United States of America. xhuang@astate.edu.

Abstract

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX .

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