Bologna Marco, Corino Valentina, Tenconi Chiara, Facchinetti Nadia, Calareso Giuseppina, Iacovelli Nicola, Cavallo Anna, Alfieri Salvatore, Cavalieri Stefano, Fallai Carlo, Valdagni Riccardo, Rancati Tiziana, Trama Annalisa, Licitra Lisa, Orlandi Ester, Mainardi Luca
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1152-1155. doi: 10.1109/EMBC44109.2020.9176565.
The purpose of this study was to establish a methodology and technology for the development of an MRI-based radiomic signature for prognosis of overall survival (OS) in nasopharyngeal cancer from non-endemic areas. The signature was trained using 1072 features extracted from the main tumor in T1-weighted and T2-weighted images of 142 patients. A model with 2 radiomic features was obtained (RAD model). Tumor volume and a signature obtained by training the model on permuted survival data (RADperm model) were used as a reference. A 10-fold cross-validation was used to validate the signature. Harrel's C-index was used as performance metric. A statistical comparison of the RAD, RADperm and volume was performed using Wilcoxon signed rank tests. The C-index for the RAD model was higher compared to the one of the RADperm model (0.69±0.08 vs 0.47±0.05), which ensures absence of overfitting. Also, the signature obtained with the RAD model had an improved C-index compared to tumor volume alone (0.69±0.08 vs 0.65±0.06), suggesting that the radiomic signature provides additional prognostic information.
本研究的目的是建立一种方法和技术，用于开发基于磁共振成像（MRI）的影像组学特征，以预测非流行地区鼻咽癌的总生存期（OS）。使用从142例患者的T1加权和T2加权图像中的主要肿瘤提取的1072个特征对该特征进行训练。获得了一个具有2个影像组学特征的模型（RAD模型）。将肿瘤体积和通过对置换生存数据训练该模型获得的特征（RADperm模型）用作参考。采用10倍交叉验证来验证该特征。使用Harrel's C指数作为性能指标。使用Wilcoxon符号秩检验对RAD、RADperm和体积进行统计比较。与RADperm模型相比，RAD模型的C指数更高（0.69±0.08对0.47±0.05），这确保了不存在过拟合。此外，与单独的肿瘤体积相比，RAD模型获得的特征具有更高的C指数（0.69±0.08对0.65±0.06），表明影像组学特征提供了额外的预后信息。
