Toward radiomics for assessment of response to systemic therapies in lung cancer

Abstract

This editorial comment explains recent developments in radiomics regarding the use of quantitative imaging biomarkers to predict lung cancer sensitivity to a variety of cancer therapies. Tumor response assessment has been a crucial component guiding cancer treatment. Evaluation of treatment response was standardized and classically based on measuring changes in tumor lesion size. Recent breakthroughs in artificial intelligence pave the way for the use of radiomics in tumor response assessment. Such objective techniques would bring a remarkable transformation to conventional methods, which can be inherently subjective. Successful implementation of these technologies would allow for faster and more accurate predictions of treatment efficacy, which will be critical to the advancement of personalized medicine.

Keywords: computed tomography; immunotherapy; lung cancer; positron emission tomography; prognosis.

Figure 1. Toward radiomics for assessment of response to systemic therapies in lung cancer.

(A) Multiple imaging modalities can characterize tumor imaging phenotypes such as CT scan, MRI, and PET. Multiparametric imaging offers unique opportunities to extend the image-based tumor analyses to more holistic characteristics. (B) left. In “Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics,” the authors demonstrated that change over serial radiographic measurements in radiomics features deciphering tumor volume, invasion of tumor boundaries, or spatial tumor heterogeneity predicted tumor sensitivity to treatment, offering an approach that could enhance clinical decision-making to continue systemic therapies and forecast overall survival. (B) right. A fundamental breakthrough would be integrating multiparametric imaging data into the radiomic framework, such as imaging features extracted from functional MRI and 18F-FDG PET. (C) Machine-learning approaches can unravel among these imaging features, new imaging biomarkers predicting tumor sensitivity to treatment.