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

Jason L. Causey, Junyu Zhang, Shiqian Ma, Bo Jiang, Jake A. Qualls, David G. Politte, Fred Prior, Shuzhong Zhang, Xiuzhen Huang

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

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.

Original languageEnglish (US)
Article number9286
JournalScientific reports
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2018

Bibliographical note

Funding Information:
This work was partially supported by National Institute of Health NCI grant U01CA187013, and National Science Foundation with grant number 1452211, 1553680, and 1723529, National Institute of Health grant R01LM012601, as well as partially supported by National Institute of Health grant from the National Institute of General Medical Sciences (P20GM103429). B.J. was partially supported by the National Natural Science Foundation of China with grant number 11401364. S.M. was partially supported by a startup package from Department of Mathematics at UC Davis. S.Z. was partially supported by National Science Foundation with grant number 1462408 and 1723529.

Publisher Copyright:
© 2018 The Author(s).

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