Abstract：
OBJECTIVE: Recent studies have indicated that radiomics may have excellent performance and clinical application prospects in the differential diagnosis of benign and malignant vertebral compression fractures (VCFs). However, multimodal magnetic resonance imaging (MRI)-based radiomics model is rarely used in the differential diagnosis of benign and malignant VCFs, and is limited to lumbar. Herein, this study intends to develop and validate MRI radiomics models for differential diagnoses of benign and malignant VCFs in patients.
METHODS: This cross-sectional study involved 151 adult patients diagnosed with VCF in The First Affiliated Hospital of Soochow University in 2016-2021. The study was conducted in three steps: (i) the original MRI images were segmented, and the region of interest (ROI) was marked out; (ii) among the extracted features, those features with Pearson\'s correlation coefficient lower than 0.9 and the top 15 with the highest variance and Lasso regression coefficient less than and more than 0 were selected; (iii) MRI images and combined data were studied by logistic regression, decision tree, random forest and extreme gradient boosting (XGBoost) models in training set and the test set (ratio of 8:2), respectively; and the models were further verified and evaluated for the differential diagnosis performance. The evaluated indexes included area under receiver (AUC) of operating characteristic curve, accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and 95% confidence intervals (CIs). The AUCs were used to assess the predictive performance of different machine learning modes for benign and malignant VCFs.
RESULTS: A total of 1144 radiomics features, and 14 clinical features were extracted. Finally, 12 radiomics features were included in the radiomics model, and 12 radiomics features with 14 clinical features were included in the combined model. In the radiomics model, the differential diagnosis performance in the logistic regression model with the AUC of 0.905 ± 0.026, accuracy of 0.817 ± 0.057, sensitivity of 0.831 ± 0.065, and negative predictive value of 0.813 ± 0.042, was superior to the other three. In the combined model, XGBoost model had the superior differential diagnosis performance with specificity (0.979 ± 0.026) and positive predictive value (0.971 ± 0.035).
CONCLUSIONS: The multimodal MRI-based radiomics model performed well in the differential diagnosis of benign and malignant VCFs, which may provide a tool for clinicians to differentially diagnose VCFs.
METHODS: This cross-sectional study involved 151 adult patients diagnosed with VCF in The First Affiliated Hospital of Soochow University in 2016-2021. The study was conducted in three steps: (i) the original MRI images were segmented, and the region of interest (ROI) was marked out; (ii) among the extracted features, those features with Pearson\'s correlation coefficient lower than 0.9 and the top 15 with the highest variance and Lasso regression coefficient less than and more than 0 were selected; (iii) MRI images and combined data were studied by logistic regression, decision tree, random forest and extreme gradient boosting (XGBoost) models in training set and the test set (ratio of 8:2), respectively; and the models were further verified and evaluated for the differential diagnosis performance. The evaluated indexes included area under receiver (AUC) of operating characteristic curve, accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and 95% confidence intervals (CIs). The AUCs were used to assess the predictive performance of different machine learning modes for benign and malignant VCFs.
RESULTS: A total of 1144 radiomics features, and 14 clinical features were extracted. Finally, 12 radiomics features were included in the radiomics model, and 12 radiomics features with 14 clinical features were included in the combined model. In the radiomics model, the differential diagnosis performance in the logistic regression model with the AUC of 0.905 ± 0.026, accuracy of 0.817 ± 0.057, sensitivity of 0.831 ± 0.065, and negative predictive value of 0.813 ± 0.042, was superior to the other three. In the combined model, XGBoost model had the superior differential diagnosis performance with specificity (0.979 ± 0.026) and positive predictive value (0.971 ± 0.035).
CONCLUSIONS: The multimodal MRI-based radiomics model performed well in the differential diagnosis of benign and malignant VCFs, which may provide a tool for clinicians to differentially diagnose VCFs.
摘要：
目的：最近的研究表明，影像组学在良恶性椎体压缩性骨折（VCFs）的鉴别诊断中具有优异的性能和临床应用前景。然而,基于多模态磁共振成像（MRI）的影像组学模型很少用于良性和恶性VCF的鉴别诊断，仅限于腰椎。在这里，本研究旨在开发和验证MRI影像组学模型,用于患者良性和恶性VCFs的鉴别诊断.
方法：本横断面研究纳入2016-2021年苏州大学附属第一医院151例确诊为VCF的成年患者。该研究分为三个步骤：（i）对原始MRI图像进行分割，并标出感兴趣区域(ROI)；(Ii)在提取的特征中，选择Pearson相关系数小于0.9，方差最大且Lasso回归系数小于和大于0的前15个特征；（iii）通过逻辑回归研究MRI图像和组合数据，决策树,训练集和测试集中的随机森林和极端梯度提升(XGBoost)模型(比率为8:2)，分别；并进一步验证和评估了模型的鉴别诊断性能。评价指标包括工作特性曲线的接收器下面积（AUC），准确度,灵敏度,特异性，负预测值(NPV)，阳性预测值(PPV)，和95%置信区间(CI)。AUC用于评估不同机器学习模式对良性和恶性VCF的预测性能。
结果：总共1144个影像组学特征，并提取14个临床特征。最后,影像组学模型中包括12个影像组学特征，合并模型中包括12个影像组学特征和14个临床特征.在影像组学模型中，logistic回归模型的鉴别诊断表现优于其他三者,AUC为0.905±0.026,准确度为0.817±0.057,灵敏度为0.831±0.065,阴性预测值为0.813±0.042.在组合模型中，XGBoost模型具有较好的鉴别诊断性能，特异性（0.979±0.026），阳性预测值（0.971±0.035）。
结论：基于多模态MRI的影像组学模型在良性和恶性VCF的鉴别诊断中表现良好，这可能为临床医生提供鉴别诊断VCF的工具。
方法：本横断面研究纳入2016-2021年苏州大学附属第一医院151例确诊为VCF的成年患者。该研究分为三个步骤：（i）对原始MRI图像进行分割，并标出感兴趣区域(ROI)；(Ii)在提取的特征中，选择Pearson相关系数小于0.9，方差最大且Lasso回归系数小于和大于0的前15个特征；（iii）通过逻辑回归研究MRI图像和组合数据，决策树,训练集和测试集中的随机森林和极端梯度提升(XGBoost)模型(比率为8:2)，分别；并进一步验证和评估了模型的鉴别诊断性能。评价指标包括工作特性曲线的接收器下面积（AUC），准确度,灵敏度,特异性，负预测值(NPV)，阳性预测值(PPV)，和95%置信区间(CI)。AUC用于评估不同机器学习模式对良性和恶性VCF的预测性能。
结果：总共1144个影像组学特征，并提取14个临床特征。最后,影像组学模型中包括12个影像组学特征，合并模型中包括12个影像组学特征和14个临床特征.在影像组学模型中，logistic回归模型的鉴别诊断表现优于其他三者,AUC为0.905±0.026,准确度为0.817±0.057,灵敏度为0.831±0.065,阴性预测值为0.813±0.042.在组合模型中，XGBoost模型具有较好的鉴别诊断性能，特异性（0.979±0.026），阳性预测值（0.971±0.035）。
结论：基于多模态MRI的影像组学模型在良性和恶性VCF的鉴别诊断中表现良好，这可能为临床医生提供鉴别诊断VCF的工具。
