# Radiological, Radiomics, and Metastatic Patterns Associated with Targetable Oncogenic Drivers on CT-Scan of Newly Diagnosed NSCLC Patients: A Comprehensive Radiogenomics Review

## TL;DR

This review explores how CT scans can reveal genetic traits in lung cancer patients, potentially guiding treatment without invasive biopsies.

## Contribution

The paper provides a comprehensive review of radiogenomic patterns linking imaging features to targetable genetic drivers in NSCLC.

## Key findings

- Radiological and radiomic features correlate with specific genetic alterations in NSCLC.
- Imaging biomarkers may complement or replace biopsies in identifying targetable oncogenic drivers.
- Radiogenomics could improve non-invasive tumor characterization and treatment decisions.

## Abstract

Non-small cell lung cancer is not a single disease but a group of tumors potentially driven by different genetic alterations that strongly influence prognosis and treatment options. Identifying these alterations usually requires tissue biopsies, which are sometimes invasive, insufficient, or impossible to repeat over time. Medical imaging, particularly computed tomography scans, is routinely performed in all patients and may contain hidden qualitative and quantitative information reflecting underlying tumor biology. In this review, we summarize how common genetic drivers of non-small cell lung cancer are associated with specific patient characteristics, tumor appearances on imaging, and patterns of metastatic spread. We also discuss recent advances in quantitative imaging analysis, especially radiomics combined with artificial intelligence algorithms, which aims to extract biological information from medical images. Together, these tools could help improve non-invasive tumor characterization, guide molecular testing strategies, and support treatment decisions, especially when tissue samples are limited or when the disease evolves over time.

The management of non-small cell lung cancer (NSCLC), including lung adenocarcinomas (LUAD), has been revolutionized with the advent of precision oncology. While advanced cancers often carry poor prognosis, those harboring specific molecular alterations sensitive to targeted therapy (notably tyrosine kinase inhibitor [TKI]) have experienced improved response to treatment and survival outcomes. Consequently, detecting these alterations through molecular screening panel has become standard in several countries, although this necessitates high-quality tissue sampling to inform optimal therapeutic decisions. Oncologic imaging occupies a pivotal role in the routine care of patients, in particular at diagnosis, with a wealth of information gathered but underutilized, as medical imaging reflects the disease in its entirety at a given time point. Moreover, recent advancements in imaging quantitative analysis, including radiomics and artificial intelligence, could aid in better integration and understanding of this information that has been overlooked for years. Several radiological phenotypes (or radiophenotypes) have been linked to tumor genomic alterations, both in standard radiology relying on semantic features and metastatic patterns, and in radiomics. Ultimately, understanding the relationships between imaging and targetable genomic alterations via accurate imaging biomarkers could complement ambiguous tumor or liquid biopsy, detect emerging new alterations, and even substitute biopsy through ‘virtual biopsy’. During the past decade, there has been a surge in research focused on radiogenomic assessment of NSCLC and especially LUAD. However, due to the low prevalence of many oncogenic drivers, the scientific literature may lack clarity or present conflicting findings. This comprehensive review aims to provide a summary of the current state of this research, offering insights into the complex interplay between imaging and genomic alterations in lung adenocarcinoma.