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).

通过免疫检查点抑制疗法（ICI-TX）的癌症治疗新模式显示，实体瘤患者的总生存期和无进展生存期显著改善。在已证实PD 1疗效的肿瘤中，包括肺癌、头颈癌、胃癌和膀胱癌，总生存期有了巨大的、未见的改善。然而，只有大约20%的患者被证明有反应，而80%的患者未能受益。虽然与常规治疗相比，癌症治疗已被提升到上级成功水平，但用于治疗监测的成像仍限于基本的常规方法，未能评估早期治疗反应以实现反应者和非反应者之间的早期和充分区分，从而导致急需的治疗变化的延迟以及无效和不必要的治疗成本。在过去的几年中，许多研究已经证明了接受化疗/放疗的肺癌患者的形态学和代谢变化的联合评估的益处。研究结果强调了PET/CT参数在预测无进展生存期、总生存期以及识别有治疗失败风险的患者方面的预测能力，从而实现早期治疗调整。最近对18F-FDG PET/CT用于早期预测晚期黑色素瘤患者对免疫检查点治疗的反应的研究强调了代谢和形态学参数联合评估用于ICI-TX评估的预测能力。虽然PET/CT能够对代谢和形态学参数进行组合分析，但同时多参数PET/MR成像基于对灌注和功能性MR参数的探索将潜在成像生物标志物的评估提升到众多。同步PET/MRI已被证明为基于图像的肿瘤生物学分析提供了强大的多参数成像平台，可能识别肿瘤异质性和表型以及疾病进展中的治疗耐药性。这些参数的范围从肿瘤大小或体积的简单评估、其几何形状、肿瘤纹理分析、肿瘤细胞结构和血管化到组织氧合、某些代谢物的检测和肿瘤代谢的定量评估。这种综合考虑大量定量图像特征进行肿瘤表型分型的方法通常被称为放射组学。受放射组学分析收集大量数据的性质的限制，需要机器学习算法来进行计算识别和特征提取。因此，我们研究的目的是评估放射组学和机器学习指导的多参数FDG-PET/MRI评估，以预测早期治疗反应。

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