Stake Jacob, Spiekers Christine, Akkurt Burak Han, Heindel Walter, Brix Tobias, Mannil Manoj, Musigmann Manfred
University Clinic for Radiology, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany.
Institute of Medical Informatics, University of Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany.
Diagnostics (Basel). 2024 May 21;14(11):1070. doi: 10.3390/diagnostics14111070.
In this study, we sought to evaluate the capabilities of radiomics and machine learning in predicting seropositivity in patients with suspected autoimmune encephalitis (AE) from MR images obtained at symptom onset. In 83 patients diagnosed with AE between 2011 and 2022, manual bilateral segmentation of the amygdala was performed on pre-contrast T2 images using 3D Slicer open-source software. Our sample of 83 patients contained 43 seropositive and 40 seronegative AE cases. Images were obtained at our tertiary care center and at various secondary care centers in North Rhine-Westphalia, Germany. The sample was randomly split into training data and independent test data. A total of 107 radiomic features were extracted from bilateral regions of interest (ROIs). Automated machine learning (AutoML) was used to identify the most promising machine learning algorithms. Feature selection was performed using recursive feature elimination (RFE) and based on the determination of the most important features. Selected features were used to train various machine learning algorithms on 100 different data partitions. Performance was subsequently evaluated on independent test data. Our radiomics approach was able to predict the presence of autoantibodies in the independent test samples with a mean AUC of 0.90, a mean accuracy of 0.83, a mean sensitivity of 0.84 and a mean specificity of 0.82, with Lasso regression models yielding the most promising results. These results indicate that radiomics-based machine learning could be a promising tool in predicting the presence of autoantibodies in suspected AE patients. Given the implications of seropositivity for definitive diagnosis of suspected AE cases, this may expedite diagnostic workup even before results from specialized laboratory testing can be obtained. Furthermore, in conjunction with recent publications, our results indicate that characterization of AE subtypes by use of radiomics may become possible in the future, potentially allowing physicians to tailor treatment in the spirit of personalized medicine even before laboratory workup is completed.
在本研究中，我们试图评估放射组学和机器学习从症状发作时获得的磁共振图像预测疑似自身免疫性脑炎（AE）患者血清学阳性的能力。在2011年至2022年期间诊断为AE的83例患者中，使用3D Slicer开源软件在对比前T2图像上对杏仁核进行手动双侧分割。我们的83例患者样本包括43例血清学阳性和40例血清学阴性AE病例。图像在我们的三级护理中心以及德国北莱茵-威斯特法伦州的各个二级护理中心获取。样本被随机分为训练数据和独立测试数据。从双侧感兴趣区域（ROI）提取了总共107个放射组学特征。使用自动机器学习（AutoML）来识别最有前景的机器学习算法。使用递归特征消除（RFE）并基于最重要特征的确定进行特征选择。选择的特征用于在100个不同的数据分区上训练各种机器学习算法。随后在独立测试数据上评估性能。我们的放射组学方法能够在独立测试样本中预测自身抗体的存在，平均曲线下面积（AUC）为0.90，平均准确率为0.83，平均灵敏度为0.84，平均特异性为0.82，套索回归模型产生了最有前景的结果。这些结果表明，基于放射组学的机器学习可能是预测疑似AE患者自身抗体存在的一种有前景的工具。鉴于血清学阳性对疑似AE病例明确诊断的影响，这甚至可能在获得专门实验室检测结果之前加快诊断检查。此外，结合最近的出版物，我们的结果表明，未来使用放射组学对AE亚型进行特征描述可能成为可能，这可能使医生甚至在实验室检查完成之前就能根据个性化医疗的精神调整治疗方案。
