Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA

Abstract

L ocally advanced, inoperable lung cancer is a challenging disease. Despite aggressive treatment with chemoradiation, local cancer recurrence is observed in approximately 50% of patients (1), and the median survival rate is only 23 to 25 months (2). Imaging genomics, otherwise known as radiogenomics, is a promising technique which can potentially improve prognostication and help guide new treatment strategies. This emerging field aims to capture the molecular subtypes and genetic underpinnings of a disease based on associations between radiologic imaging and genomic sequencing.Radiomics is a high-throughput computational technique in which mathematical features are derived from medical images. The analysis of cell-free DNA (cfDNA) is a complementary technique whereby traces of cfDNA are detectable in the bloodstream when apoptotic cells shed fragmented DNA. Using nextgeneration sequencing, cancer-specific mutations for genes implicated in the pathogenesis of lung cancer can be identified from a subset of cfDNA known as circulating tumor DNA (ctDNA). Both cfDNA and ctDNA, which are mechanistically and technologically different from tissue-based mutational assessment, are promising liquid biopsy-based markers of radiation response (3-7).This brief report provides preliminary results demonstrating the feasibility of an integrated radiomic, cfDNA, and ctDNA analysis in patients with locally advanced lung cancer. Our method is based on a prospective pilot study of patients who underwent concurrent chemoradiation in which liquid biopsies were performed during treatment. We hypothesized that cell death due to chemoradiation therapy would lead to the release of measurable cfDNA into the bloodstream, which when combined with CT radiomics and next-generation sequencing, could provide patient-specific radiogenomic expression patterns associated with prognosis. Materials and Methods Study DesignOur study design is summarized in Figure 1. All research was conducted in keeping with best clinical practice and regulatory compliance. Prospective data (images, plasma, patient outcomes) were generated from Pro00017361 (NCT00921739) approved by the

Identification of CT Imaging Phenotypes of Colorectal Liver Metastases from Radiomics Signatures—Towards Assessment of Interlesional Tumor Heterogeneity

“…Aerts et al, investigated the prognostic value of tumor phenotypes defined by radiomics for both lung cancer and head-and-neck cancer [ 17 ]. Lafata et al, analyzed the association of radiomics features with underlying tumor mutational and cfDNA patterns [ 18 ] and performed first associations with p53 status, however, only focused on primary lung tumors and not on multiple metastases. A recently published study by Starmans et al, demonstrated that radiomics and machine learning features from CT can predict histopathological tumor growth patterns in colorectal liver metastases [ 19 ].…”

What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies

“…The studies choose many different ways to focus on lung cancer. In one study [ 31 ], only late-stage patients were included. Two studies [ 29 , 51 ] only included early-stage lung cancer patients.…”

“…The studies included different subtypes of lung cancer, and small cell lung cancer was typically excluded. Only two studies allowed small cell lung cancer [ 31 , 33 ], though one [ 33 ] only had 3% cases with this subtype. One study focused on adenocarcinomas [ 51 ] and one focused on squamous cell carcinomas [ 29 ].…”

Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden

“…However, radiomic analysis facilitates characterization of complex radiogenomic interactions that can be used to study basic biology and identify prognostic markers during treatment [ 38 , 39 ]. Agnostic features have been shown to add complementary insight compared to conventional semantic features, including disease staging and tumor volume [ 40 ].…”