Prognostic performance of radiomic data to predict distant metastases and overall survival in patients with early stage (I/II) non-small cell lung cancer (NSCLC)

肺癌是世界上癌症死亡的主要原因，其中非小细胞肺癌（NSCLC）是最常见的亚型。尽管在诊断和治疗方面有所改进，但NSCLC的预后仍然很差。只有患有早期疾病的患者才能根据手术切除进行根治性治疗。然而，即使在完全和成功切除相同的肿瘤阶段，观察到广泛的生存期。在这种情况下，越来越明显的是，每种肿瘤都与不同的表型特征相关，这是导致不同治疗反应的原因。这导致了个性化医疗的概念，其目的是针对患者的个体需求定制治疗策略。因此，个体肿瘤表型可以作为一种决策工具，用于估计个体对不同治疗方法的反应，目的是提高总体生存率和生活质量。肺癌通常使用医学成像诊断，最常见的是计算机断层扫描（CT）。诊断基于放射科医生根据不同成像特征的主观图像解读，由于观察者之间的差异性，存在错误诊断的风险。因此，有必要对肿瘤组织进行活检以确定最终诊断。然而，活检仅允许采集少量组织样本，这可能无法全面捕获肿瘤内的分子变异，从而存在低估肿瘤侵袭性的风险。因此，基于数学算法非侵入性地提取客观图像特征以描述肿瘤表型的定量成像分析变得越来越重要。这种分析成像数据的新方法被称为放射组学。有越来越多的证据表明，放射组学可能有潜力作为一个具有成本效益的和非侵入性的生物标志物的个性化医疗提供有关肿瘤分期，转移潜力，治疗反应和总生存期的预后信息。早期NSCLC患者特别适合评价放射组学对非侵入性肿瘤分析的预后影响，因为CT图像和组织病理学分析可常规用于这些患者。此外，如上所述，尽管相同疾病阶段的手术切除是成功的，但众所周知但迄今为止无法预测的是，这些患者中的某个亚群将经历复发性疾病，这表明需要精准医学。因此，拟定研究的目的是研究放射组学数据在早期NSCLC患者中的预后性能，旨在确定预测性图像特征，以估计转移潜力和总生存期。这不仅对个性化医疗非常重要，而且从跨学科和社会经济的角度来看也非常重要。

Lung cancer is the leading cause of cancer deaths in the world with non-small cell lung cancer (NSCLC) being the most commonly observed subtype. Despite improvements in diagnosis and treatment, prognosis of NSCLC remains poor. Only patients with early-stage disease are treated with curative intent based on surgical resection. However, even after complete and successful resection of the same tumor stage, a wide spectrum of survival periods is observed. In this context, it has become increasingly evident that each tumor is associated with distinct phenotypic characteristics, which are responsible for different treatment responses. This has led to concept of personalized medicine with the aim to tailor treatment strategies towards the individual needs of the patient. Thus, the individual tumor phenotype may serve as a decision-making tool for estimating the individual response to different treatment approaches with the aim to improve overall survival and quality of life. Lung cancer is routinely diagnosed using medical imaging, most commonly computed tomography (CT). Diagnosis is based on the subjective image interpretation of the radiologist according to distinct imaging features with the risk for false diagnosis due to inter-observer variability. Therefore, biopsy of tumor tissue is necessary to establish a final diagnosis. However, biopsy only allows for acquiring small samples of tissue, which may fail to comprehensively capture the molecular variations within tumors, thus posing the risk for underestimating tumor aggressiveness. Therefore, quantitative imaging analysis has gained increasing importance with the aim to non-invasively extract objective image features to describe the tumor phenotype based on mathematical algorithms. This new way of analyzing imaging data has become known as radiomics. There is growing evidence that radiomics may have the potential to serve as a cost-effective and non- invasive biomarker for personalized medicine by providing prognostic information regarding tumor stage, metastatic potential, treatment response and overall survival. Patients with early-stage NSCLC are particularly suitable to evaluate the prognostic impact of radiomics for non-invasive tumor profiling, as CT images and histopathological analysis are routinely available for these patients. Moreover, as mentioned above, despite successful surgical resection of the same disease stage it is well known but not predictable so far, that a certain subpopulation of these patients will experience recurrent disease indicating the need for precision medicine. Therefore, the purpose of the proposed study is to investigate the prognostic performance of radiomic data in patients with early-stage NSCLC with the aim to identify predictive image features to estimate the metastatic potential and overall survival. This is of great importance not only for personalized medicine but also from an interdisciplinary and socioeconomic point of view.