Radiomics and machine learning assessment of multiparametric FDG-PET/MRI for evaluation of prediction of early treatment response to immune checkpoint therapy in patients with Non-Small Cell Lung Cancer (NSCLC).

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The new paradigm of cancer treatment, by means of immune checkpoint inhibition therapy (ICI-TX), has revealed a significant improvement in overall survival and progression-free survival of patients with solid tumors. Large, unseen-of gains in overall survival have been demonstrated in tumors with proven PD1 efficacy, comprising lung, head and neck, gastric as well as bladder cancer. Nevertheless, only approximately 20 % of patients have been shown to respond, while 80 % of patients fail to benefit. While cancer treatment has been elevated to superior success levels when compared to conventional treatment, imaging for therapy monitoring is still restricted to basic conventional methods, failing to assess early treatment response to enable early and sufficient differentiation between responders and non-responders causing delays in much-needed treatment changes as well as ineffective and unnecessary treatment costs. Over the past few years a number of studies have demonstrated the benefit of the combined assessment of morphological and metabolic changes in patients with lung cancer undergoing chemo-/ radiation therapy. The study results underline the predictive power of PET/CT parameters for prediction of progression-free survival, overall survival as well as identification of patients at risk of treatment failure, enabling early treatment adjustment. Recent investigations on 18F-FDG PET/CT for early prediction of response to immune checkpoint therapy in patients with advanced melanoma have underlined the predictive power of the combined assessment of metabolic and morphologic parameters for ICI-TX assessment. While PET/CT enables the combined analysis of metabolic and morphologic parameters, simultaneous multiparametric PET/MR imaging uplifts the assessment of potential imaging biomarkers to a multitude based on the exploration of perfusion and functional MR parameters. Simultaneous PET/MRI has been shown to provide a powerful multiparametric imaging platform for image-based profiling of tumor biology, potentially identifying tumor heterogeneity and phenotypes as well as evolving therapy resistance in disease progression. These parameters range from simple assessment of tumor size or volume, its geometric shape, tumor texture analysis, tumor cellularity and vascularization to tissue oxygenation, detection of certain metabolites and quantitative assessment of tumor metabolism. This kind of comprehensive consideration of a large number of quantitative image features for tumor phenotyping is often referred to as radiomics. Bound to the nature of radiomics analysis in collecting an extensive volume of data machine learning algorithms are needed for computational identification and feature extraction. Hence, the aim of our study is to evaluate radiomics and machine learning-guided assessment of multiparametric FDG-PET/MRI for prediction of early treatment respons