Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial.

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Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial.

Cancer Med. 2018 Dec 01;:

Authors: Cherezov D, Hawkins SH, Goldgof DB, Hall LO, Liu Y, Li Q, Balagurunathan Y, Gillies RJ, Schabath MB

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.

PMID: 30507033 [PubMed - as supplied by publisher]

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