Chen Ling, Chen Runrong, Li Tao, Tang Chuyun, Li Yao, Zeng Zisan
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Department of Radiology, Liuzhou Workers Hospital, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China.
Front Neurol. 2023 Sep 26;14:1266658. doi: 10.3389/fneur.2023.1266658. eCollection 2023.
To investigate the clinical utility of multi-parameter MRI-based radiomics nomogram for predicting telomerase reverse transcriptase (TERT) promoter mutation status and prognosis in adult glioblastoma (GBM).
We retrospectively analyzed MRI and pathological data of 152 GBM patients. A total of 2,832 radiomics features were extracted and filtered from preoperative MRI images. A radiomics nomogram was created on the basis of radiomics signature (rad-score) and clinical traits. The performance of the nomogram in TERT mutation identification was assessed using receiver operating characteristic (ROC) curve, calibration curves, and clinical decision curves. Pathologically confirmed TERT mutations and risk score-based TERT mutations were employed to assess patient prognosis, respectively.
The random forest (RF) algorithm outperformed the other two algorithms, yielding the best diagnostic efficacy in differentiating TERT mutations, with area under the curve (AUC) values of 0.892 (95% CI: 0.828-0.956) and 0.824 (95% CI: 0.677-0.971) in the training set and validation sets, respectively. Furthermore, the predictive power of the radiomics nomogram constructed with the rad-score and clinical variables reached 0.916 (95%CI: 0.864, 0.968) in the training set and 0.880 (95%CI: 0.743, 1) in the validation set. Calibration curve and decision curve analysis findings further uphold the clinical application value of the radiomics nomogram. The overall survival of the high-risk subgroup was significantly shorter than that of the low-risk subgroup, which was consistent with the results of the pathologically confirmed TERT mutation group.
The radiomics nomogram could non-invasively provide promising insights for predicting TERT mutations and prognosis in GBM patients with excellent identification and calibration abilities.
探讨基于多参数磁共振成像（MRI）的影像组学列线图在预测成人胶质母细胞瘤（GBM）中端粒酶逆转录酶（TERT）启动子突变状态及预后方面的临床应用价值。
回顾性分析152例GBM患者的MRI及病理数据。从术前MRI图像中提取并筛选出共2832个影像组学特征。基于影像组学特征（rad-score）和临床特征创建影像组学列线图。采用受试者工作特征（ROC）曲线、校准曲线和临床决策曲线评估列线图在TERT突变识别中的性能。分别采用病理确诊的TERT突变和基于风险评分的TERT突变评估患者预后。
随机森林（RF）算法优于其他两种算法，在鉴别TERT突变方面具有最佳诊断效能，训练集和验证集的曲线下面积（AUC）值分别为0.892（95%CI：0.828 - 0.956）和0.824（95%CI：0.677 - 0.971）。此外，由rad-score和临床变量构建的影像组学列线图在训练集和验证集的预测能力分别达到0.916（95%CI：0.864，0.968）和0.880（95%CI：0.743，1）。校准曲线和决策曲线分析结果进一步支持了影像组学列线图的临床应用价值。高风险亚组的总生存期显著短于低风险亚组，这与病理确诊的TERT突变组结果一致。
影像组学列线图能够无创地为预测GBM患者的TERT突变及预后提供有前景的见解，具有出色的识别和校准能力。
