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The prognostic value of CT radiomic features for patients with pulmonary adenocarcinoma treated with EGFR tyrosine kinase inhibitors.

The prognostic value of CT radiomic features for patients with pulmonary adenocarcinoma treated with EGFR tyrosine kinase inhibitors.
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Kim H, Park CM, Keam B, Park SJ, Kim M, Kim TM, Kim DW, Heo DS, Goo JM,


Kim H, Park CM, Keam B, Park SJ, Kim M, Kim TM, Kim DW, Heo DS, Goo JM, (click to view)

Kim H, Park CM, Keam B, Park SJ, Kim M, Kim TM, Kim DW, Heo DS, Goo JM,

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PloS one 2017 11 0312(11) e0187500 doi 10.1371/journal.pone.0187500
Abstract
PURPOSE
To determine if the radiomic features on CT can predict progression-free survival (PFS) in epidermal growth factor receptor (EGFR) mutant adenocarcinoma patients treated with first-line EGFR tyrosine kinase inhibitors (TKIs) and to identify the incremental value of radiomic features over conventional clinical factors in PFS prediction.

METHODS
In this institutional review board-approved retrospective study, pretreatment contrast-enhanced CT and first follow-up CT after initiation of TKIs were analyzed in 48 patients (M:F = 23:25; median age: 61 years). Radiomic features at baseline, at 1st first follow-up, and the percentage change between the two were determined. A Cox regression model was used to predict PFS with nonredundant radiomic features and clinical factors, respectively. The incremental value of radiomic features over the clinical factors in PFS prediction was also assessed by way of a concordance index.

RESULTS
Roundness (HR: 3.91; 95% CI: 1.72, 8.90; P = 0.001) and grey-level nonuniformity (HR: 3.60; 95% CI: 1.80, 7.18; P<0.001) were independent predictors of PFS. For clinical factors, patient age (HR: 2.11; 95% CI: 1.01, 4.39; P = 0.046), baseline tumor diameter (HR: 1.03; 95% CI: 1.01, 1.05; P = 0.002), and treatment response (HR: 0.46; 95% CI: 0.24, 0.87; P = 0.017) were independent predictors. The addition of radiomic features to clinical factors significantly improved predictive performance (concordance index; combined model = 0.77, clinical-only model = 0.69, P<0.001). CONCLUSIONS
Radiomic features enable PFS estimation in EGFR mutant adenocarcinoma patients treated with first-line EGFR TKIs. Radiomic features combined with clinical factors provide significant improvement in prognostic performance compared with using only clinical factors.

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