Imaging features from pretreatment CT scans are associated with clinical outcomes in nonsmall-cell lung cancer patients treated with stereotactic body radiotherapy

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Abstract

Purpose:

To investigate whether imaging features from pretreatment planning CT scans are associated with overall survival (OS), recurrence-free survival (RFS), and loco-regional recurrence-free survival (LR-RFS) after stereotactic body radiotherapy (SBRT) among nonsmall-cell lung cancer (NSCLC) patients.

Patients and methods:

A total of 92 patients (median age: 73 yr) with stage I or IIA NSCLC were qualified for this study. A total dose of 50 Gy in five fractions was the standard treatment. Besides clinical characteristics, 24 “semantic” image features were manually scored based on a point scale (up to 5) and 219 computer-derived “radiomic” features were extracted based on whole tumor segmentation. Statistical analysis was performed using Cox proportional hazards model and Harrell's C-index, and the robustness of final prognostic model was assessed using tenfold cross validation by dichotomizing patients according to the survival or recurrence status at 24 months.

Results:

Two-year OS, RFS and LR-RFS were 69.95%, 41.3%, and 51.85%, respectively. There was an improvement of Harrell's C-index when adding imaging features to a clinical model. The model for OS contained the Eastern Cooperative Oncology Group (ECOG) performance status [Hazard Ratio (HR) = 2.78, 95% Confidence Interval (CI): 1.37–5.65], pleural retraction (HR = 0.27, 95% CI: 0.08–0.92), F2 (short axis × longest diameter, HR = 1.72, 95% CI: 1.21–2.44) and F186 (Hist-Energy-L1, HR = 1.27, 95% CI: 1.00–1.61); The prognostic model for RFS contained vessel attachment (HR = 2.13, 95% CI: 1.24–3.64) and F2 (HR = 1.69, 95% CI: 1.33–2.15); and the model for LR-RFS contained the ECOG performance status (HR = 2.01, 95% CI: 1.12–3.60) and F2 (HR = 1.67, 95% CI: 1.29–2.18).

Conclusions:

Imaging features derived from planning CT demonstrate prognostic value for recurrence following SBRT treatment, and might be helpful in patient stratification.

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