Imaging genotyping of functional signaling pathways in lung squamous cell carcinoma using a radiomics approach

Abstract

Imaging features can be useful for identifying distinct genomic differences and have predictive power for certain phenotypes attributed to genomic mutations. We aimed to identify predictive imaging biomarkers that underpin genomic alterations and clinical outcomes in lung squamous cell carcinoma (SQCC) using a radiomics approach. In 57 patients with lung SQCC who underwent preoperative computed tomography (CT) and whole-exome DNA sequencing, 63 quantitative imaging features were extracted from CT and 73 clinicoradiological features including imaging features were classified into 8 categories: clinical, global, histogram-based, lung cancer-specific, shape, local, regional, and emphysema. Mutational profiles for core signaling pathways of lung SQCC were classified into five categories: redox stress, apoptosis, proliferation, differentiation, and chromatin remodelers. Range and right lung volume was significantly associated with alternation of apoptosis and proliferation pathway (p = 0.03, and p = 0.03). Energy was associated with the redox stress pathway (p = 0.06). None of the clinicoradiological features showed any significant association with the alteration of differentiation and chromatin remodelers pathway. This study showed that radiomic features indicating five different functional pathways of lung SQCC were different form one another. Radiomics approaches to lung SQCC have the potential to noninvasively predict alterations in core signaling pathways and clinical outcome.

Figure 1

(A) Distribution of alteration percentage of signaling pathways, and (B) number of alteration of pathway per patients in lung SQCC.

Figure 2

Five functional signaling pathways and genes used for analysis of lung SQCC (Kim et al. J Clin Oncol 2013).

Figure 3

Extracting quantitative imaging features from CT images. (A) CT images were obtained on full inspiration. (B) Tumors were segmented by drawing regions of interest (ROIs) that traced tumor edges on all axial images. (C) Quantitative image features were extracted from within defined tumor contours on CT images to quantify features that were global, histogram-based, shape, lung cancer-specific, local, regional, or emphysema. (D) Associations among quantitative image features, clinical data, and gene expression data were analyzed.