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 and Materials 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 reproducibility. Results Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone for 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 reproducibility in terms of OS, LRC, and FFDM, respectively. Conclusions Pretreatment tumor texture may provide prognostic information beyond what is obtained from CPFs. Models incorporating feature reproducibility achieved classification rates of ~80%. External validation would be required to establish texture as a prognostic factor.

Early-Stage Non–Small Cell Lung Cancer: Quantitative Imaging Characteristics of18F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis

“…In particular, the texture feature was the cluster shade of the Gaussian filtered image within the Laws feature group (Gauss cluster shade), To the best of our knowledge, we are the first researchers to report on the investigation of quantitative FDG PET image features (ie, radiomics) for predicting distant metastasis in early-stage NSCLC after SABR. Authors of other recent studies (24,25) have addressed early-stage NSCLC. Another strength of our study was that we focused on patients with early-stage NSCLC only and identified predictors that stratify patients of similar clinical stages, whereas authors of many previous studies included patients of mixed stages and did not address specifically whether the predictors add important information early-stage NSCLC by using a quantitative radiomic approach.…”

Stage III Non–Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors

“…Recently, studies have suggested that the examination of spatial heterogeneity with computed tomography (CT) and the use of SUVs within solid tumors (particularly NSCLC) may provide prognostic information (8)(9)(10)(11)(12). Although these studies have generated compelling early evidence that heterogeneous tumors may lead to inferior outcomes, they have lacked adjustment for known prognostic factors and the use of proper validation techniques.…”

Can radiomics features be reproducibly measured from CBCT images for patients with non‐small cell lung cancer?

“…In the context of tumor analysis, univariate or multivariate models using these features have typically been built to diagnose lesions, [13][14][15] identify secondary effects, 16 or predict outcome. 6,12 Recent publications have demonstrated that a wide variety of radiomics features may predict NSCLC patient outcomes when extracted from computed tomography (CT), 6,12,17 contrast-enhanced CT, 18,19 or positron emission tomography (PET) [20][21][22] images. However, no studies have yet examined whether there is a potential for imaging features extracted from cone-beam CT (CBCT) images to be useful.…”