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

Hyungjin Kim,Dae Seog Heo,Bhumsuk Keam,Miso Kim,Sang Joon Park,Sang Joon Park,Jin Mo Goo,Jin Mo Goo,Chang Min Park,Chang Min Park,Dong-Wan Kim,Tae Min Kim,Fan Yang

doi:10.1371/journal.pone.0187500

Hyungjin Kim, Dae Seog Heo + Show 11 more

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https://doi.org/10.1371/journal.pone.0187500

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Abstract

PurposeTo 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.MethodsIn 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.ResultsRoundness (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).ConclusionsRadiomic 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.

Highlights

Lung cancer is the leading cause of cancer death worldwide and non-small cell lung cancer (NSCLC) is the largest subgroup, occupying about 85% of cases [1]
Radiomics for prognosis prediction in lung adenocarcinoma patients treated with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs)
Radiomic features enable progression-free survival (PFS) estimation in EGFR mutant adenocarcinoma patients treated with first-line EGFR TKIs

Summary

Introduction

Lung cancer is the leading cause of cancer death worldwide and non-small cell lung cancer (NSCLC) is the largest subgroup, occupying about 85% of cases [1]. The OPTIMAL study compared erlotinib with chemotherapy as a first-line treatment in Asian patients which demonstrated that EGFR TKI could significantly prolong progression-free survival (PFS) (median PFS 13.1 months versus 4.6 months) [3]. Despite their dramatic initial responses and prolonged survival, all of the patients eventually developed resistance to EGFR TKI [1]. The median PFS after treatment with a first-generation EGFR TKI in patients with EGFR mutations is typically less than one year [1]. Prediction of PFS in these patients is significant as the predicted survival before the initiation of therapy may guide the aggressiveness of treatment, or may help to prepare for additional treatment options, at the estimated time of acquiring resistance

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