Document Type
Article
Publication Date
12-2018
Keywords
early detection, lung cancer screening, National Lung Screening Trial, quantitative imaging, Radiomics
Digital Object Identifier (DOI)
https://doi.org/10.1002/cam4.1852
Abstract
Background: Current guidelines for lung cancer screening increased a positive scan threshold to a 6 mm longest diameter. We extracted radiomic features from baseline and follow‐up screens and performed size‐specific analyses to predict lung cancer incidence using three nodule size classes (<6 mm [small], 6‐16 mm [intermediate], and ≥16 mm [large]).
Methods: We extracted 219 features from baseline (T0) nodules and 219 delta features which are the change from T0 to first follow‐up (T1). Nodules were identified for 160 incidence cases diagnosed with lung cancer at T1 or second follow‐up screen (T2) and for 307 nodule‐positive controls that had three consecutive positive screens not diagnosed as lung cancer. The cases and controls were split into training and test cohorts; classifier models were used to identify the most predictive features.
Results: The final models revealed modest improvements for baseline and delta features when compared to only baseline features. The AUROCs for small‐ and intermediate‐sized nodules were 0.83 (95% CI 0.76‐0.90) and 0.76 (95% CI 0.71‐0.81) for baseline‐only radiomic features, respectively, and 0.84 (95% CI 0.77‐0.90) and 0.84 (95% CI 0.80‐0.88) for baseline and delta features, respectively. When intermediate and large nodules were combined, the AUROC for baseline‐only features was 0.80 (95% CI 0.76‐0.84) compared with 0.86 (95% CI 0.83‐0.89) for baseline and delta features.
Conclusions: We found modest improvements in predicting lung cancer incidence by combining baseline and delta radiomics. Radiomics could be used to improve current size‐based screening guidelines.
Rights Information
This work is licensed under a Creative Commons Attribution 4.0 License.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Cancer Medicine, v. 7, issue 12, p. 6340-6356
Scholar Commons Citation
Cherezov, Dmitry; Hawkins, Samuel H.; Goldgof, Dmitry B.; Hall, Lawrence O.; Liu, Ying; Li, Qian; Balagurunathan, Yoganand; Gillies, Robert J.; and Schabath, Matthew B., "Delta Radiomic Features Improve Prediction for Lung Cancer Incidence: A Nested Case–Control Analysis of the National Lung Screening Trial" (2018). Computer Science and Engineering Faculty Publications. 111.
https://digitalcommons.usf.edu/esb_facpub/111
