Abstract
This study investigated the associations between image features extracted from tumor 18F-fluorodeoxyglucose (FDG) uptake and genetic alterations in patients with lung cancer. A total of 137 patients (age, 62.7 ± 10.2 years) who underwent FDG positron emission tomography/computed tomography (PET/CT) and targeted deep sequencing analysis for a tumor lesion, comprising 61 adenocarcinoma (ADC), 31 squamous cell carcinoma (SQCC), and 45 small cell lung cancer (SCLC) patients, were enrolled in this study. From the tumor lesions, 86 image features were extracted, and 381 genes were assessed. PET features were associated with genetic mutations: 41 genes with 24 features in ADC; 35 genes with 22 features in SQCC; and 43 genes with 25 features in SCLC (FDR < 0.05). Clusters based on PET features showed an association with alterations in oncogenic signaling pathways: Cell cycle and WNT signaling pathways in ADC (p = 0.023, p = 0.035, respectively); Cell cycle, p53, and WNT in SQCC (p = 0.045, 0.009, and 0.029, respectively); and TGFβ in SCLC (p = 0.030). In addition, SUVpeak and SUVmax were associated with a mutation of the TGFβ signaling pathway in ADC (FDR = 0.001, < 0.001). In this study, PET image features had significant associations with alterations in genes and oncogenic signaling pathways in patients with lung cancer.
Highlights
Radiogenomics, merging medical imaging data and genomic information, has great potential in the era of personalized medicine[1,2,3,4]
We have here investigated the relationships between metabolic image features and genomic alterations, including oncogenic signaling pathways, from the dataset of our institution
The limitations of a heterogeneous dataset and incomplete methodology have hampered our ability to generalize our findings, some results are consistent with those of previous studies indicating that the FDG PET-based radiogenomic approach has the potential to play a significant role in cancer research and clinical practice
Summary
Radiogenomics, merging medical imaging data and genomic information, has great potential in the era of personalized medicine[1,2,3,4]. A known association between image features and genetic alterations has a potential as a useful additional information to improve decision making of biopsies, which could create new, accessible management strategies for patients with c ancer[4,6,10]. Despite the potential of radiogenomics-based FDG PET images, only a few studies have focused on the relationships between FDG uptake and genetic alterations in patients with lung c ancer[12,13,14,15], and the study subjects and PET image features of the few studies that have been done were not sufficient to fully elucidate the association between FDG PET imaging and genomic information[12,13,14,15]. We investigated the associations between features extracted from tumor FDG uptake and genetic alterations in patients with lung cancer
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