Next-generation radiomic sequencing in non-small cell lung cancer: an alternative model to predict mutations from...

Non-small cell lung cancer (NSCLC) remains the most common cause of cancer-related mortality worldwide. The introduction of targeted therapies against oncogenic drivers, particularly EGFR and KRAS mutations, has significantly improved patient outcomes. However, next-generation sequencing (NGS), the current gold standard for molecular profiling, is not always accessible in routine clinical practice, emphasizing the need for noninvasive surrogate biomarkers. Radiomics has emerged as a promising imaging-based approach that extracts a large number of quantitative features from standard modalities such as [18F]FDG PET/CT. By capturing tumor heterogeneity and biological characteristics, radiomics can provide clinically relevant insights and holds potential for identifying predictive biomarkers. Recent studies suggest that CT radiomic features related to heterogeneity, texture, and shape may predict EGFR and KRAS mutation status, while the integration of metabolic parameters from [18F]FDG PET radiomics may further enhance predictive performance and offer a more comprehensive characterization of tumor biology.

To assess the putative role of [18F]FDG PET/CT radiomic features for the prediction of mutated NSCLC.

This retrospective observational study included patients with histologically confirmed NSCLC, molecularly profiled by NGS and who underwent baseline [18F]FDG PET/CT scans at Fondazione IRCCS San Gerardo dei Tintori, Monza. Tumor segmentation and radiomic feature extraction were performed on PET images using Pyradiomics, generating 766 quantitative features. Feature selection for EGFR and KRAS mutation association was conducted via repeated random subsampling and LASSO logistic regression.

Patients' histological, clinical and PET/CT imaging data were obtained from the electronic clinical database and picture archiving and communication system of IRCCS Fondazione San Gerardo dei Tintori di Monza between January 2023 and December 2024. Data from 105 patients with biopsy-proven NSCLC and available NGS and [18F]FDG PET/CT scans were analyzed to identify radiomic features from PET images associated with specific mutations. Two different PET/CT scanners were used (Discovery IQ and Discovery MI, GE Healthcare), and radiomic features were extracted using IBSI-compliant algorithms, generating 766 features per tumor. Features correlated with mutations were selected using the Discovery MI dataset (55 patients) and subsequently evaluated on the independent Discovery IQ dataset (50 patients).

No radiomic features were identified as associated with EGFR mutation in the Discovery MI dataset. Among the features correlated with KRAS mutation in the Discovery MI dataset, FBS_glcm_MCC-a measure of image texture complexity-was confirmed to be associated with KRAS mutation in the independent Discovery IQ dataset, with an AUC of 0.68, p = 0.04, and an odds ratio of 0.65.

The potential of [18F]FDG PET radiomics as a surrogate for genetic profiling in NSCLC is promising; however, these preliminary findings require further validation in larger cohorts.