Name
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 similar to 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1038/s41598-018-27569-w | SCIENTIFIC REPORTS |
Field | DocType | Volume |
Lung cancer,Population,Pattern recognition,Lung,Convolutional neural network,Computer science,Malignancy,Artificial intelligence,Computed tomography,Deep learning,Radiology | Journal | 8 |
Issue | ISSN | Citations |
1 | 2045-2322 | 5 |
PageRank | References | Authors |
0.63 | 10 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jason Causey | 1 | 5 | 0.63 |
| Junyu Zhang | 2 | 6 | 4.70 |
| Shiqian Ma | 3 | 1068 | 63.48 |
| Bo Jiang | 4 | 62 | 5.55 |
| Jake Qualls | 5 | 5 | 0.63 |
| David G. Politte | 6 | 29 | 8.46 |
| Fred Prior | 7 | 32 | 3.51 |
| Shuzhong Zhang | 8 | 2808 | 181.66 |
| Xiuzhen Huang | 9 | 442 | 26.16 |