PDF(Pubmed)
Abstract：
UNASSIGNED: This study aimed to investigate the clinical application of 18F-FDG PET radiomics features for temporal lobe epilepsy and to create PET radiomics-based machine learning models for differentiating temporal lobe epilepsy (TLE) patients from healthy controls.
UNASSIGNED: A total of 347 subjects who underwent 18F-FDG PET scans from March 2014 to January 2020 (234 TLE patients: 25.50 ± 8.89 years, 141 male patients and 93 female patients; and 113 controls: 27.59 ± 6.94 years, 48 male individuals and 65 female individuals) were allocated to the training (n = 248) and test (n = 99) sets. All 3D PET images were registered to the Montreal Neurological Institute template. PyRadiomics was used to extract radiomics features from the temporal regions segmented according to the Automated Anatomical Labeling (AAL) atlas. The least absolute shrinkage and selection operator (LASSO) and Boruta algorithms were applied to select the radiomics features significantly associated with TLE. Eleven machine-learning algorithms were used to establish models and to select the best model in the training set.
UNASSIGNED: The final radiomics features (n = 7) used for model training were selected through the combinations of the LASSO and the Boruta algorithms with cross-validation. All data were randomly divided into a training set (n = 248) and a testing set (n = 99). Among 11 machine-learning algorithms, the logistic regression (AUC 0.984, F1-Score 0.959) model performed the best in the training set. Then, we deployed the corresponding online website version (https://wane199.shinyapps.io/TLE_Classification/), showing the details of the LR model for convenience. The AUCs of the tuned logistic regression model in the training and test sets were 0.981 and 0.957, respectively. Furthermore, the calibration curves demonstrated satisfactory alignment (visually assessed) for identifying the TLE patients.
UNASSIGNED: The radiomics model from temporal regions can be a potential method for distinguishing TLE. Machine learning-based diagnosis of TLE from preoperative FDG PET images could serve as a useful preoperative diagnostic tool.
UNASSIGNED: A total of 347 subjects who underwent 18F-FDG PET scans from March 2014 to January 2020 (234 TLE patients: 25.50 ± 8.89 years, 141 male patients and 93 female patients; and 113 controls: 27.59 ± 6.94 years, 48 male individuals and 65 female individuals) were allocated to the training (n = 248) and test (n = 99) sets. All 3D PET images were registered to the Montreal Neurological Institute template. PyRadiomics was used to extract radiomics features from the temporal regions segmented according to the Automated Anatomical Labeling (AAL) atlas. The least absolute shrinkage and selection operator (LASSO) and Boruta algorithms were applied to select the radiomics features significantly associated with TLE. Eleven machine-learning algorithms were used to establish models and to select the best model in the training set.
UNASSIGNED: The final radiomics features (n = 7) used for model training were selected through the combinations of the LASSO and the Boruta algorithms with cross-validation. All data were randomly divided into a training set (n = 248) and a testing set (n = 99). Among 11 machine-learning algorithms, the logistic regression (AUC 0.984, F1-Score 0.959) model performed the best in the training set. Then, we deployed the corresponding online website version (https://wane199.shinyapps.io/TLE_Classification/), showing the details of the LR model for convenience. The AUCs of the tuned logistic regression model in the training and test sets were 0.981 and 0.957, respectively. Furthermore, the calibration curves demonstrated satisfactory alignment (visually assessed) for identifying the TLE patients.
UNASSIGNED: The radiomics model from temporal regions can be a potential method for distinguishing TLE. Machine learning-based diagnosis of TLE from preoperative FDG PET images could serve as a useful preoperative diagnostic tool.
摘要：
■本研究旨在研究18F-FDGPET影像组学特征在颞叶癫痫中的临床应用，并创建基于PET影像组学的机器学习模型，以区分颞叶癫痫（TLE）患者与健康对照。
■从2014年3月至2020年1月，共有347名受试者接受了18F-FDGPET扫描（234名TLE患者：25.50±8.89岁，141名男性患者和93名女性患者；和113名对照：27.59±6.94岁，48个男性个体和65个女性个体)被分配到训练(n=248)和测试(n=99)组。所有3DPET图像均与蒙特利尔神经学研究所模板配准。PyRadiomics用于从根据自动解剖标记（AAL）图集分割的颞区提取影像组学特征。应用最小绝对收缩和选择算子（LASSO）和Boruta算法来选择与TLE显着相关的影像组学特征。使用11种机器学习算法来建立模型并在训练集中选择最佳模型。
■用于模型训练的最终影像组学特征（n=7）是通过LASSO和Boruta算法的组合进行交叉验证来选择的。将所有数据随机分为训练集(n=248)和测试集(n=99)。在11种机器学习算法中，逻辑回归(AUC0.984,F1-Score0.959)模型在训练集中表现最好.然后，我们部署了相应的在线网站版本(https://wane199。shinyapps.io/TLE_Classification/),为方便起见，显示LR模型的详细信息。调整后的逻辑回归模型在训练集和测试集中的AUC分别为0.981和0.957。此外，校准曲线显示了用于识别TLE患者的令人满意的比对(视觉评估).
■来自时间区域的影像组学模型可能是区分TLE的潜在方法。根据术前FDGPET图像对TLE进行基于机器学习的诊断可以作为有用的术前诊断工具。
■从2014年3月至2020年1月，共有347名受试者接受了18F-FDGPET扫描（234名TLE患者：25.50±8.89岁，141名男性患者和93名女性患者；和113名对照：27.59±6.94岁，48个男性个体和65个女性个体)被分配到训练(n=248)和测试(n=99)组。所有3DPET图像均与蒙特利尔神经学研究所模板配准。PyRadiomics用于从根据自动解剖标记（AAL）图集分割的颞区提取影像组学特征。应用最小绝对收缩和选择算子（LASSO）和Boruta算法来选择与TLE显着相关的影像组学特征。使用11种机器学习算法来建立模型并在训练集中选择最佳模型。
■用于模型训练的最终影像组学特征（n=7）是通过LASSO和Boruta算法的组合进行交叉验证来选择的。将所有数据随机分为训练集(n=248)和测试集(n=99)。在11种机器学习算法中，逻辑回归(AUC0.984,F1-Score0.959)模型在训练集中表现最好.然后，我们部署了相应的在线网站版本(https://wane199。shinyapps.io/TLE_Classification/),为方便起见，显示LR模型的详细信息。调整后的逻辑回归模型在训练集和测试集中的AUC分别为0.981和0.957。此外，校准曲线显示了用于识别TLE患者的令人满意的比对(视觉评估).
■来自时间区域的影像组学模型可能是区分TLE的潜在方法。根据术前FDGPET图像对TLE进行基于机器学习的诊断可以作为有用的术前诊断工具。
