Computer-Aided Nodule Assessment and Risk Yield (CANARY) may facilitate non-invasive prediction of EGFR mutation status in lung adenocarcinomas

Abstract

Computer-Aided Nodule Assessment and Risk Yield (CANARY) is quantitative imaging analysis software that predicts the histopathological classification and post-treatment disease-free survival of patients with adenocarcinoma of the lung. CANARY characterizes nodules by the distribution of nine color-coded texture-based exemplars. We hypothesize that quantitative computed tomography (CT) analysis of the tumor and tumor-free surrounding lung facilitates non-invasive identification of clinically-relevant mutations in lung adenocarcinoma. Comprehensive analysis of targetable mutations (50-gene-panel) and CANARY analysis of the preoperative (≤3 months) high resolution CT (HRCT) was performed for 118 pulmonary nodules of the adenocarcinoma spectrum surgically resected between 2006-2010. Logistic regression with stepwise variable selection was used to determine predictors of mutations. We identified 140 mutations in 106 of 118 nodules. TP53 (n = 48), KRAS (n = 47) and EGFR (n = 15) were the most prevalent. The combination of Y (Yellow) and G (Green) exemplars, fibrosis within the surrounding lung and smoking status were the best discriminators for an EGFR mutation (AUC 0.77 and 0.87, respectively). None of the EGFR mutants expressing TP53 (n = 5) had a good prognosis based on CANARY features. No quantitative features were significantly associated with KRAS mutations. Our exploratory analysis indicates that quantitative CT analysis of a nodule and surrounding lung may noninvasively predict the presence of EGFR mutations in pulmonary nodules of the adenocarcinoma spectrum.

Figure 1

(a–d) Example of nodule characterization by CANARY in which the (a) user selects a seed in the center of the nodule guided by x, y and z axis, (b) a mask is generated encompassing the nodule’s volume in which CANARY analysis (c) assigns each voxel the color code of the closest exemplar which is also represented by a glyph (d) displaying the relative proportion of each exemplar within a nodule.

Figure 2

Representation in red of the tumor-free surrounding lung for an adenocarcinoma in the right middle lobe. The area highlighted in red was analyzed by CALIPER for low attenuation and fibrosis shown in the axial, coronal and sagittal planes. Each nodule underwent analysis of the tumor-free surrounding lung characteristics in this manner.

Figure 3

CANARY glyphs representing each unique nodule demonstrate proportionate representation of each CANARY exemplar. Glyphs are arranged by mutation status from left to right in order of parametric signatures correlating with progressively more invasive histopathology. Wild type denotes WT status for both EGFR and KRAS.

Figure 4

(a–d) Kaplan Meier survival curves depict likelihood of survival by (a) CANARY prognosis (p = 0.002), (b) presence of EGFR mutation (p = 0.26), (c) presence of KRAS mutation (p = 0.48) or (d) presence of any driver mutation (p = 0.78).