Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer

Abstract

Objective The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC). Methods In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F] FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n = 74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalised linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence. Results Standardised uptake value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule predicting the outcome resulted in an area under the curve (AUC) of 0.87. Conclusions Radiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology.

A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT

“…Other literatures reported that the combination of multiple radiomic features, which is often defined as the radiomic signature (RS), may hold higher prognostic value than the single feature [16,17]. In addition, RS combined with clinical or genomic data can produce robust and improved medical decision-making [18,19]. However, to the best of our knowledge, the RS based on [ 18 F]FDG PET/CT for prognosis assessment of DLBCL has not yet been described.…”

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

“…There were eight studies addressing lung, head, and neck cancer (see complete data in Table S2 in Supplementary Materials ). All of the studies used CT-derived radiomic features and two studies [ 32 , 34 ] also had PET FDG-derived radiomic features. Though there was a high proportion with a decent number of included patients (24–837, mean 310), most studies had few patients with genetic data and only one study had data on multiple genes for more than 100 patients [ 33 ].…”

“…The handling of genetic data in the studies on lung cancer can be divided into three subgroups. Two studies [ 28 , 32 ] focused solely on the EGFR mutation , three studies [ 31 , 33 , 34 ] looked for correlations with multiple single genes and three studies [ 28 , 29 , 51 ] focused on correlation to gene pathway alteration.…”

A Novel Gene Signature Associated With “E2F Target” Pathway for Predicting the Prognosis of Prostate Cancer

“…MXD3 has been shown to predict poor prognosis in clear cell renal cell carcinoma ( Zhang et al, 2021 ) and hepatocellular carcinoma ( Xu et al, 2019 ), as well as having been indicated as a new molecular targeted site to treat neuroblastoma ( Yoshida et al, 2020 ). PLK1 ( Iliaki et al, 2021 ), EPHK10 , and KIF4A also play important roles as prognostic indicators or as targeted therapy sites for the progression of multiple tumors, such as pancreatic cancer ( Zhu et al, 2021 ), esophageal squamous cell carcinoma ( Li et al, 2021 ), bladder cancer ( Zheng et al, 2021 ), and lung cancer ( Kirienko et al, 2021 ). Furthermore, EPHA10 has already been approved as a potential therapeutic target of prostate cancer ( Nagano et al, 2014 ), and high EPHA10 expression correlated with lymph node metastasis of breast cancer ( Nagano et al, 2013 )—the type of malignant behavior that usually predicts a poor prognosis.…”