Baysal Begumhan, Eser Mehmet Bilgin, Dogan Mahmut Bilal, Kursun Muhammet Arif
Istanbul Goztepe Prof. Dr. Suleyman Yalcin City Hospital, Clinic of Radiology, Istanbul, Turkey.
Medeni Med J. 2022 Mar 18;37(1):36-43. doi: 10.4274/MMJ.galenos.2022.58538.
This study aims to develop neural networks to detect hormone secretion profiles in the pituitary adenomas based on T2 weighted magnetic resonance imaging (MRI) radiomics.
This retrospective model-development study included a cohort of patients with pituitary adenomas (n=130) from January 2015 to January 2020 in one tertiary center. The mean age was 46.49±13.69 years, and 76/130 (58.46%) were women. Three observers segmented lesions on coronal T2 weighted MRI, and an interrater agreement was evaluated using the Dice coefficient. Predictors were determined as radiomics features (n=851). Feature selection was based on intraclass correlation coefficient, coefficient variance, variance inflation factor, and LASSO regression analysis. Outcomes were identified as 7 hormone secretion profiles [non-functioning pituitary adenoma, growth hormone-secreting adenomas, prolactinomas, adrenocorticotropic hormone-secreting adenomas, pluri-hormonal secreting adenomas (PHA), follicle-stimulating hormone and luteinizing hormone-secreting adenomas, and thyroid-stimulating hormone adenomas]. A multivariable diagnostic prediction model was developed with artificial neural networks (ANN) for 7 outcomes. ANN performance was presented as an area under the receiver operating characteristic curve (AUC) and accepted as successful if the AUC was >0.85 and p-value was <0.01.
The performance of the ANN distinguishing prolactinomas from other adenomas was validated (AUC=0.95, p<0.001, sensitivity: 91%, and specificity: 98%). The model distinguishing PHA had the lowest AUC (AUC=0.74 and p<0.001). The AUC values for the other five ANN were >0.85 and p values were <0.001.
This study was successful in training neural networks that could differentiate the hormone secretion profile of pituitary adenomas.
本研究旨在开发神经网络，基于T2加权磁共振成像（MRI）放射组学检测垂体腺瘤中的激素分泌谱。
这项回顾性模型开发研究纳入了2015年1月至2020年1月在一家三级中心的一组垂体腺瘤患者（n = 130）。平均年龄为46.49±13.69岁，130例中有76例（58.46%）为女性。三名观察者在冠状位T2加权MRI上对病变进行分割，并使用Dice系数评估观察者间的一致性。预测因子被确定为放射组学特征（n = 851）。特征选择基于组内相关系数、系数方差、方差膨胀因子和LASSO回归分析。结果被确定为7种激素分泌谱[无功能性垂体腺瘤、生长激素分泌性腺瘤、催乳素瘤、促肾上腺皮质激素分泌性腺瘤、多激素分泌性腺瘤（PHA）、促卵泡激素和促黄体生成素分泌性腺瘤以及促甲状腺激素腺瘤]。使用人工神经网络（ANN）针对7种结果开发了多变量诊断预测模型。ANN的性能以受试者工作特征曲线下面积（AUC）表示，如果AUC>0.85且p值<0.01，则认为模型成功。
ANN区分催乳素瘤与其他腺瘤的性能得到验证（AUC = 0.95，p<0.001，敏感性：91%，特异性：98%）。区分PHA的模型AUC最低（AUC = 0.74且p<0.001）。其他五个ANN的AUC值>0.85且p值<0.001。
本研究成功训练出了能够区分垂体腺瘤激素分泌谱的神经网络。
