Bologna Marco, Corino Valentina, Calareso Giuseppina, Tenconi Chiara, Alfieri Salvatore, Iacovelli Nicola Alessandro, Cavallo Anna, Cavalieri Stefano, Locati Laura, Bossi Paolo, Romanello Domenico Attilio, Ingargiola Rossana, Rancati Tiziana, Pignoli Emanuele, Sdao Silvana, Pecorilla Mattia, Facchinetti Nadia, Trama Annalisa, Licitra Lisa, Mainardi Luca, Orlandi Ester
Department of Electronics, Information and Bioengineering (DEIB) Politecnico di Milano, 20133 Milan, Italy.
Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Cancers (Basel). 2020 Oct 13;12(10):2958. doi: 10.3390/cancers12102958.
Advanced stage nasopharyngeal cancer (NPC) shows highly variable treatment outcomes, suggesting the need for independent prognostic factors. This study aims at developing a magnetic resonance imaging (MRI)-based radiomic signature as a prognostic marker for different clinical endpoints in NPC patients from non-endemic areas. A total 136 patients with advanced NPC and available MRI imaging (T1-weighted and T2-weighted) were selected. For each patient, 2144 radiomic features were extracted from the main tumor and largest lymph node. A multivariate Cox regression model was trained on a subset of features to obtain a radiomic signature for overall survival (OS), which was also applied for the prognosis of other clinical endpoints. Validation was performed using 10-fold cross-validation. The added prognostic value of the radiomic features to clinical features and volume was also evaluated. The radiomics-based signature had good prognostic power for OS and loco-regional recurrence-free survival (LRFS), with C-index of 0.68 and 0.72, respectively. In all the cases, the addition of radiomics to clinical features improved the prognostic performance. Radiomic features can provide independent prognostic information in NPC patients from non-endemic areas.
晚期鼻咽癌（NPC）的治疗结果差异很大，这表明需要独立的预后因素。本研究旨在开发一种基于磁共振成像（MRI）的放射组学特征，作为非流行地区NPC患者不同临床终点的预后标志物。共选择了136例晚期NPC患者且有可用的MRI成像（T1加权和T2加权）。对于每位患者，从主要肿瘤和最大淋巴结中提取2144个放射组学特征。在一部分特征子集上训练多变量Cox回归模型，以获得总生存期（OS）的放射组学特征，该特征也用于其他临床终点的预后评估。使用10倍交叉验证进行验证。还评估了放射组学特征对临床特征和体积的附加预后价值。基于放射组学的特征对OS和局部区域无复发生存期（LRFS）具有良好的预后能力，C指数分别为0.68和0.72。在所有病例中，将放射组学添加到临床特征中可改善预后性能。放射组学特征可为非流行地区的NPC患者提供独立的预后信息。
