Huang Lixuan, Yang Zongxiang, Zeng Zisan, Ren Hao, Jiang Muliang, Hu Yao, Xu Yifan, Zhang Huiting, Ma Kun, Long Liling
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Department of Radiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Front Neurol. 2023 Mar 16;14:1135978. doi: 10.3389/fneur.2023.1135978. eCollection 2023.
This study was conducted to develop and validate a radiomics-clinics combined model-based magnetic resonance imaging (MRI) radiomics and clinical features for the early prediction of radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC).
This retrospective study was conducted using data from 130 patients with NPC (80 patients with and 50 patients without RTLI) who received radiotherapy. Cases were assigned randomly to training ( = 91) and testing ( = 39) datasets. Data on 168 medial temporal lobe texture features were extracted from T1WI, T2WI, and T1WI-CE MRI sequences obtained at the end of radiotherapy courses. Clinics, radiomics, and radiomics-clinics combined models (based on selected radiomics signatures and clinical factors) were constructed using machine learning software. Univariate logistic regression analysis was performed to identify independent clinical factors. The area under the ROC curve (AUC) was performed to evaluate the performance of three models. A nomogram, decision curves, and calibration curves were used to assess the performance of the combined model.
Six texture features and three independent clinical factors associated significantly with RTLI were used to build the combined model. The AUCs for the combined and radiomics models were 0.962 [95% confidence interval (CI), 0.9306-0.9939] and 0.904 (95% CI, 0.8431-0.9651), respectively, for the training cohort and 0.947 (95% CI, 0.8841-1.0000) and 0.891 (95% CI, 0.7903-0.9930), respectively, for the testing cohort. All of these values exceeded those for the clinics model (AUC = 0.809 and 0.713 for the training and testing cohorts, respectively). Decision curve analysis showed that the combined model had a good corrective effect.
The radiomics-clinics combined model developed in this study showed good performance for predicting RTLI in patients with NPC.
本研究旨在开发并验证一种基于影像组学与临床特征相结合的模型，该模型基于磁共振成像（MRI）影像组学和临床特征，用于早期预测鼻咽癌（NPC）患者放射性颞叶损伤（RTLI）。
本回顾性研究使用了130例接受放疗的NPC患者的数据（80例发生RTLI，50例未发生RTLI）。病例被随机分配到训练集（n = 91）和测试集（n = 39）。在放疗疗程结束时从T1WI、T2WI和T1WI-CE MRI序列中提取168个内侧颞叶纹理特征数据。使用机器学习软件构建临床、影像组学以及影像组学-临床特征相结合的模型（基于选定的影像组学特征和临床因素）。进行单因素逻辑回归分析以确定独立的临床因素。采用ROC曲线下面积（AUC）评估三种模型的性能。使用列线图、决策曲线和校准曲线评估联合模型的性能。
使用与RTLI显著相关的六个纹理特征和三个独立临床因素构建联合模型。训练队列中，联合模型和影像组学模型的AUC分别为0.962[95%置信区间（CI），0.9306 - 0.9939]和0.904（95%CI，0.8431 - 0.9651），测试队列中分别为0.947（95%CI，0.8841 - 1.0000）和0.891（95%CI，0.7903 - 0.9930）。所有这些值均超过临床模型的值（训练队列和测试队列的AUC分别为0.809和0.713）。决策曲线分析表明联合模型具有良好的校正效果。
本研究中开发的影像组学-临床特征相结合的模型在预测NPC患者RTLI方面表现良好。
