MRI radiomics-based long-term evaluation and identification of imaging biomarkers for growth prediction of distinct nodular lesions in plexiform neurofibromas in NF1

1型神经纤维瘤病（NF 1）是一种罕见的常染色体显性遗传肿瘤易感综合征，由神经纤维素基因缺陷引起。受影响的患者有很高的风险发展为周围神经鞘瘤，其中丛状神经纤维瘤（PNF）和独特的结节性病变（DNL）是特别的临床相关性。PNF可以生长到很大的尺寸（>体重的20%），并且可以导致患者的严重发病率。DNL可以在PNF内生长或作为单一病变生长。DNL的生长引起了人们对转化为癌前非典型神经纤维瘤（ANF）的关注。与恶性肿瘤不同，ANF作为恶性神经鞘肿瘤的前体，在先前切除后不显示局部复发，也不显示转移的能力。早期发现这种转化过程对这些患者的结局至关重要，因此对于NF 1患者的风险适应性患者护理至关重要。基于图像的特征已经被报道有希望指示恶性转化，但是难以由人类观察者以一致和客观的方式提取和量化这样的成像生物标志物。该限制可以通过自动标准化提取NF 1患者的纵向图像数据中的成像特征，即通过放射组学分析来解决。然而，以前的放射组学NF 1研究仅在小NF 1患者队列（N<79）中使用单个时间点数据进行横断面研究。因此，该项目的目的是使用独特的纵向全身（WB）-MRI数据集，在这种罕见疾病中使用第一种基于放射学的长期MRI方法，确定用于NF 1患者PNF中DNL发展和生长预测的成像生物标志物。该项目的入选标准是根据NIH标准诊断为NF 1，并且可进行≥ 2次WB MRI检查。249例患者的WB MRI涵盖了18年的观察期（2003 - 2021年），可用于长期放射组学研究。WB MRI检查的总数约为1.300（相当于每例患者平均5.2次）。该项目旨在回答以下问题：（1）NF 1患者中DNL和PNF的放射组学特征是否不同，并且可以自动分类？(2)不同生长模式（线性与非线性体积生长，高/低生长速率）的DNL和PNF放射组学特征表达是否不同？(3)放射组学能否作为一种预后性影像学参数预测PNF和DNL的生长？(4)有全基因缺陷的NF 1患者的放射组学特征是否与无主要缺失的患者不同（基因型-表型相关性）？(5)放射组学和遗传信息能否结合起来进一步改善PNF和DNL的发育或生长预测？理想情况下，研究的放射组学方法将能够识别非侵入性预后成像生物标志物，以实现NF 1患者的个体化风险分层。

Neurofibromatosis type 1 (NF1) is a rare autosomal-dominantly inherited tumor predisposition syndrome caused by defects in the neurofibromin gene. Affected patients have a high risk to develop peripheral nerve sheath tumors, of which plexiform neurofibromas (PNF) and distinct nodular lesions (DNL) are of particular clinical relevance. PNF can grow to large sizes (>20% of body weight) and can cause severe morbidity of patients. DNL can grow inside of PNF or as singular lesions. Growth of DNL raises concern for transformation into a pre-malignant atypical neurofibroma (ANF). Unlike malignant tumors, ANFs as precursors to malignant nerve sheath tumors do not show local recurrence after previous resection nor the ability to metastasize. Early detection of such a transformation process is crucial for the outcome of these patients and therefore essential for risk-adapted patient care of NF1 patients. Image-based features have been reported to be promising to indicate the malignant transformation, but it is difficult to extract and quantify such imaging biomarkers in a consistent and objective manner by human observers. This limitation can be addressed by automated standardized extraction of imaging features, that is, by a radiomics analysis, in longitudinal image data of NF1 patients. However, previous radiomics NF1 studies were only performed as cross-sectional studies using single time-point data in small NF1 patient cohorts (N<79). Therefore, the purpose of the project is to identify imaging biomarkers for the development and growth prediction of DNL in PNF in NF1 patients using the first long-term radiomics-based MRI approach in this rare disease using a unique longitudinal whole-body (WB)-MRI data set. Inclusion criteria for the project are diagnosis of NF1 according to the NIH criteria and availability of ≥ 2 WB MRI examinations. WB MRIs of 249 patients covering an observation period of 18 years (2003 - 2021) are available for the long-term radiomics study. The total number of WB MRI examinations is about 1.300 (corresponding to a mean of 5.2 per patient). The project aims to answer the following questions: (1) Do radiomics signatures of DNL and PNF in NF1 patients differ and can be automatically classified? (2) Do radiomics feature expressions of DNL and PNF with different growth patterns (linear vs. non-linear volume growth, high / low growth rates) differ? (3) Can radiomics predict the growth of PNF and DNL as a prognostic imaging parameter? (4) Do radiomics signatures of NF1 patients with whole gene defects differ from those without major deletions (genotype-phenotype correlation)? (5) Can radiomics and genetic information be combined to further improve PNF and DNL development or growth prediction? Ideally, the investigated radiomics approach will enable the identification of non-invasive prognostic imaging biomarkers towards an individualized risk stratification of NF1 patients.