Dufau Julie, Bouhamama Amine, Leporq Benjamin, Malaureille Lison, Beuf Olivier, Gouin François, Pilleul Franck, Marec-Berard Perrine
Institut d'hématologie et d'oncologie pédiatrique, 1, place Professeur Joseph-Renaut, 69008 Lyon, France.
Université de Lyon, CREATIS (CNRS UMR 5220, Inserm U1206, INSA-Lyon, UJM Saint-Étienne, UCB Lyon1), 69621 Villeurbanne, France; Centre de lutte contre le cancer Léon Bérard, département de radiologie, 28, rue Laennec, 69008 Lyon, France.
Bull Cancer. 2019 Nov;106(11):983-999. doi: 10.1016/j.bulcan.2019.07.005. Epub 2019 Oct 3.
Osteosarcoma is the most common malignant bone tumor before 25 years of age. Response to neoadjuvant chemotherapy determines continuation of treatment and is also a powerful prognostic factor. There are currently no reliable ways to evaluate it early. The aim is to develop a method to predict the chemotherapy response using radiomics from pre-treatment MRI.
Clinical characteristics and MRI of patients treated for local or metastatic osteosarcoma were collected retrospectively in the Rhône-Alpes region, from 2007 to 2016. On initial MRI exams, each tumor was segmented by expert radiologist and 87 radiomic features were extracted automatically. Univariate analysis was performed to assess each feature's association with histological response following neoadjuvante chemotherapy. To distinguish good histological responder from poor, we built predictive models based on support vector machines. Their classification performance was assessed with the area under operating characteristic curve receiver (AUROC) from test data.
The analysis focused on the MRIs of 69 patients, 55.1% (38/69) of whom were good histological responders. The model obtained by support vector machines from initial MRI radiomic data had an AUROC of 0.98, a sensitivity of 100% (IC 95% [100%-100%]) and specificity of 86% (IC 95% [59.7%-111%]).
Radiomic based on MRI data would predict the chemotherapy response before treatment initiation, in patients treated for osteosarcoma.
骨肉瘤是25岁之前最常见的恶性骨肿瘤。对新辅助化疗的反应决定了治疗的延续，也是一个重要的预后因素。目前尚无早期评估它的可靠方法。目的是开发一种利用治疗前MRI的放射组学来预测化疗反应的方法。
回顾性收集2007年至2016年在罗纳-阿尔卑斯地区接受局部或转移性骨肉瘤治疗的患者的临床特征和MRI。在初次MRI检查时，由专业放射科医生对每个肿瘤进行分割，并自动提取87个放射组学特征。进行单变量分析以评估每个特征与新辅助化疗后组织学反应的相关性。为了区分组织学反应良好者和不良者，我们基于支持向量机建立了预测模型。根据测试数据的操作特征曲线下面积（AUROC）评估其分类性能。
分析聚焦于69例患者的MRI，其中55.1%（38/69）为组织学反应良好者。通过支持向量机从初始MRI放射组学数据获得的模型的AUROC为0.98，灵敏度为100%（95%置信区间[100%-~100%]），特异性为8%（95%置信区间[59.7%-111%]）。
基于MRI数据的放射组学可在骨肉瘤患者治疗开始前预测化疗反应。
