MO‐A‐BRD‐04: Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non‐Small Cell Lung Cancer

Abstract

Purpose:To determine whether pretreatment CT texture features can improve patient risk stratification beyond conventional prognostic factors (CPFs) in stage III non‐small cell lung cancer (NSCLC).Methods:We retrospectively reviewed 91 patients with stage III NSCLC treated with definitive chemoradiation. All patients underwent a pretreatment diagnostic contrast enhanced CT (CE‐CT) followed by a 4D‐CT for treatment simulation. We used the average (average‐CT) and expiratory (T50‐CT) images from the 4D‐CT along with the CE‐CT for texture extraction. Histogram, gradient, co‐occurrence, gray‐tone difference, and filtration based techniques were used for texture feature extraction. Penalized Cox regression implementing cross‐validation was used for covariate selection and modeling. Models incorporating texture features from the 3 image types and CPFs were compared to models incorporating CPFs alone for overall survival (OS), local‐regional control (LRC), and freedom from distant metastases (FFDM). Predictive Kaplan‐Meier curves were generated using leave‐one‐out cross‐validation. Patients were stratified based on their predicted outcome being above/below the median. Reproducibility of texture features was evaluated using test‐retest scans from independent patients and quantified using concordance correlation coefficients (CCC). We compared models incorporating the reproducibility seen on test‐retest scans to our original models and determined the classification accuracy.Results:Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone in terms of OS (p=0.046), LRC (p=0.01), and FFDM (p=0.005). The average CCC was 0.89, 0.91, and 0.67 for texture features extracted from the average‐CT, T50‐CT, and CE‐CT, respectively. Incorporating reproducibility within our models yielded 80.4 (SD=3.7), 78.3 (SD=4.0), and 78.8 (SD=3.9) percent classification accuracy in terms of OS, LRC, and FFDM, respectively.Conclusions:Pretreatment tumor texture may provide prognostic information beyond what is obtained from CPFs. Reproducibility of CE‐CT appears inferior to average‐CT and T50‐CT; however model classification accuracy rates of approximately 80% were still achieved.