Liu Chen-Xi, Heng Li-Jun, Han Yu, Wang Sheng-Zhong, Yan Lin-Feng, Yu Ying, Ren Jia-Liang, Wang Wen, Hu Yu-Chuan, Cui Guang-Bin
Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China.
Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, China.
Front Oncol. 2021 Jul 7;11:640375. doi: 10.3389/fonc.2021.640375. eCollection 2021.
To explore the usefulness of texture signatures based on multiparametric magnetic resonance imaging (MRI) in predicting the subtypes of growth hormone (GH) pituitary adenoma (PA).
Forty-nine patients with GH-secreting PA confirmed by the pathological analysis were included in this retrospective study. Texture parameters based on T1-, T2-, and contrast-enhanced T1-weighted images (T1C) were extracted and compared for differences between densely granulated (DG) and sparsely granulated (SG) somatotroph adenoma by using two segmentation methods [region of interest 1 (ROI), excluding the cystic/necrotic portion, and ROI, containing the whole tumor]. Receiver operating characteristic (ROC) curve analysis was performed to determine the differentiating efficacy.
Among 49 included patients, 24 were DG and 25 were SG adenomas. Nine optimal texture features with significant differences between two groups were obtained from ROI. Based on the ROC analyses, T1WI signatures from ROI achieved the highest diagnostic efficacy with an AUC of 0.918, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 85.7, 72.0, 100.0, 100.0, and 77.4%, respectively, for differentiating DG from SG. Comparing with the T1WI signature, the T1C signature obtained relatively high efficacy with an AUC of 0.893. When combining the texture features of T1WI and T1C, the radiomics signature also had a good performance in differentiating the two groups with an AUC of 0.908. In addition, the performance got in all the signatures from ROI was lower than those in the corresponding signature from ROI
Texture signatures based on MR images may be useful biomarkers to differentiate subtypes of GH-secreting PA patients.
探讨基于多参数磁共振成像（MRI）的纹理特征在预测生长激素（GH）垂体腺瘤（PA）亚型中的应用价值。
本回顾性研究纳入49例经病理分析确诊为分泌GH的PA患者。采用两种分割方法[感兴趣区域1（ROI），不包括囊性/坏死部分，以及ROI，包含整个肿瘤]，提取基于T1加权、T2加权和对比增强T1加权图像（T1C）的纹理参数，并比较密集颗粒型（DG）和稀疏颗粒型（SG）生长激素腺瘤之间的差异。进行受试者操作特征（ROC）曲线分析以确定鉴别效能。
49例纳入患者中，24例为DG腺瘤，25例为SG腺瘤。从ROI获得了9个两组间有显著差异的最佳纹理特征。基于ROC分析，ROI的T1WI特征诊断效能最高，AUC为0.918，区分DG与SG的准确性、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）分别为85.7%、72.0%、100.0%、100.0%和77.4%。与T1WI特征相比，T1C特征的效能相对较高，AUC为0.893。当结合T1WI和T1C的纹理特征时，影像组学特征在区分两组方面也有良好表现，AUC为0.908。此外，ROI所有特征的表现均低于相应的包含整个肿瘤的ROI特征。
基于MR图像的纹理特征可能是区分分泌GH的PA患者亚型的有用生物标志物。
