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

Abstract

Objectives 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. Generalized linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence.Results Standardized 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 in predicting the outcome resulted in an Area Under the Curve (AUC) of 0.87.Conclusions: Radiogenomic data provided clinically relevant information in NCSCL patients, regarding the histotype, aggressiveness, and progression. Gene expression may provide additional valuable information to guide patient management. The application of ML allows to increase the efficacy of radiogenomic analysis and provide novel insights into cancer biology.

A Novel Radiogenomics Biomarker Based on Hypoxic-Gene Subset: Accurate Survival and Prognostic Prediction of Renal Clear Cell Carcinoma

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PET/CT radiomics and machine learning enable non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma

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