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Abstract：
UNASSIGNED: With the development of imaging technology, an increasing number of pulmonary nodules have been found. Some pulmonary nodules may gradually grow and develop into lung cancer, while others may remain stable for many years. Accurately predicting the growth of pulmonary nodules in advance is of great clinical significance for early treatment. The purpose of this study was to establish a predictive model using radiomics and to study its value in predicting the growth of pulmonary nodules.
UNASSIGNED: According to the inclusion and exclusion criteria, 228 pulmonary nodules in 228 subjects were included in the study. During the one-year follow-up, 69 nodules grew larger, and 159 nodules remained stable. All the nodules were randomly divided into the training group and validation group in a proportion of 7:3. For the training data set, the t test, Chi-square test and Fisher exact test were used to analyze the sex, age and nodule location of the growth group and stable group. Two radiologists independently delineated the ROIs of the nodules to extract the radiomics characteristics using Pyradiomics. After dimension reduction by the LASSO algorithm, logistic regression analysis was performed on age and ten selected radiological features, and a prediction model was established and tested in the validation group. SVM, RF, MLP and AdaBoost models were also established, and the prediction effect was evaluated by ROC analysis.
UNASSIGNED: There was a significant difference in age between the growth group and the stable group (P < 0.05), but there was no significant difference in sex or nodule location (P > 0.05). The interclass correlation coefficients between the two observers were > 0.75. After dimension reduction by the LASSO algorithm, ten radiomic features were selected, including two shape-based features, one gray-level-cooccurence-matrix (GLCM), one first-order feature, one gray-level-run-length-matrix (GLRLM), three gray-level-dependence-matrix (GLDM) and two gray-level-size-zone-matrix (GLSZM). The logistic regression model combining age and radiomics features achieved an AUC of 0.87 and an accuracy of 0.82 in the training group and an AUC of 0.82 and an accuracy of 0.84 in the verification group for the prediction of nodule growth. For nonlinear models, in the training group, the AUCs of the SVM, RF, MLP and boost models were 0.95, 1.0, 1.0 and 1.0, respectively. In the validation group, the AUCs of the SVM, RF, MLP and boost models were 0.81, 0.77, 0.81, and 0.71, respectively.
UNASSIGNED: In this study, we established several machine learning models that can successfully predict the growth of pulmonary nodules within one year. The logistic regression model combining age and imaging parameters has the best accuracy and generalization. This model is very helpful for the early treatment of pulmonary nodules and has important clinical significance.
UNASSIGNED: According to the inclusion and exclusion criteria, 228 pulmonary nodules in 228 subjects were included in the study. During the one-year follow-up, 69 nodules grew larger, and 159 nodules remained stable. All the nodules were randomly divided into the training group and validation group in a proportion of 7:3. For the training data set, the t test, Chi-square test and Fisher exact test were used to analyze the sex, age and nodule location of the growth group and stable group. Two radiologists independently delineated the ROIs of the nodules to extract the radiomics characteristics using Pyradiomics. After dimension reduction by the LASSO algorithm, logistic regression analysis was performed on age and ten selected radiological features, and a prediction model was established and tested in the validation group. SVM, RF, MLP and AdaBoost models were also established, and the prediction effect was evaluated by ROC analysis.
UNASSIGNED: There was a significant difference in age between the growth group and the stable group (P < 0.05), but there was no significant difference in sex or nodule location (P > 0.05). The interclass correlation coefficients between the two observers were > 0.75. After dimension reduction by the LASSO algorithm, ten radiomic features were selected, including two shape-based features, one gray-level-cooccurence-matrix (GLCM), one first-order feature, one gray-level-run-length-matrix (GLRLM), three gray-level-dependence-matrix (GLDM) and two gray-level-size-zone-matrix (GLSZM). The logistic regression model combining age and radiomics features achieved an AUC of 0.87 and an accuracy of 0.82 in the training group and an AUC of 0.82 and an accuracy of 0.84 in the verification group for the prediction of nodule growth. For nonlinear models, in the training group, the AUCs of the SVM, RF, MLP and boost models were 0.95, 1.0, 1.0 and 1.0, respectively. In the validation group, the AUCs of the SVM, RF, MLP and boost models were 0.81, 0.77, 0.81, and 0.71, respectively.
UNASSIGNED: In this study, we established several machine learning models that can successfully predict the growth of pulmonary nodules within one year. The logistic regression model combining age and imaging parameters has the best accuracy and generalization. This model is very helpful for the early treatment of pulmonary nodules and has important clinical significance.
摘要：
UNASSIGNED：随着成像技术的发展，越来越多的肺结节被发现。一些肺结节可能会逐渐生长并发展为肺癌，而其他人可能会保持稳定多年。提前准确预测肺结节的生长对早期治疗具有重要的临床意义。目的利用影像组学建立预测模型,研究其在预测肺结节生长中的价值。
UNASSIGNED：根据纳入和排除标准，228名受试者的228个肺结节被纳入研究。在为期一年的后续行动中，69个结节长大，159个结核保持稳定。将所有结节按7:3的比例随机分为训练组和验证组。对于训练数据集，t检验,采用卡方检验和Fisher精确检验进行性别分析,生长组和稳定组的年龄和结节位置。两名放射科医生独立地描绘了结节的ROI，以使用Pyradiomics提取影像组学特征。在通过LASSO算法降维后，对年龄和10个选定的放射学特征进行逻辑回归分析，建立预测模型并在验证组中进行测试。SVM,射频,还建立了MLP和AdaBoost模型，并通过ROC分析评价预测效果。
UNASSIGNED：生长组和稳定组之间的年龄差异显着（P<0.05），性别、结节部位差异无统计学意义(P>0.05)。两个观察者之间的类间相关系数>0.75。在通过LASSO算法降维后，选择了十个放射学特征，包括两个基于形状的特征，一个灰度共生矩阵(GLCM)，一个一阶特征，一个灰度游程长度矩阵(GLRLM)，三个灰度级相关矩阵（GLDM）和两个灰度级大小区域矩阵（GLSZM）。结合年龄和影像组学特征的逻辑回归模型在训练组中实现了0.87的AUC和0.82的准确性，在验证组中实现了0.82的AUC和0.84的准确性，用于预测结节生长。对于非线性模型，在训练组里,SVM的AUC，射频,MLP和增强模型分别为0.95、1.0、1.0和1.0。在验证组中，SVM的AUC，射频,MLP和增强模型分别为0.81、0.77、0.81和0.71。
未经批准：在这项研究中，我们建立了几个机器学习模型，可以成功预测一年内肺结节的生长。年龄和成像参数相结合的logistic回归模型具有最佳的准确性和泛化性。该模型对肺结节的早期治疗具有重要的临床意义。
UNASSIGNED：根据纳入和排除标准，228名受试者的228个肺结节被纳入研究。在为期一年的后续行动中，69个结节长大，159个结核保持稳定。将所有结节按7:3的比例随机分为训练组和验证组。对于训练数据集，t检验,采用卡方检验和Fisher精确检验进行性别分析,生长组和稳定组的年龄和结节位置。两名放射科医生独立地描绘了结节的ROI，以使用Pyradiomics提取影像组学特征。在通过LASSO算法降维后，对年龄和10个选定的放射学特征进行逻辑回归分析，建立预测模型并在验证组中进行测试。SVM,射频,还建立了MLP和AdaBoost模型，并通过ROC分析评价预测效果。
UNASSIGNED：生长组和稳定组之间的年龄差异显着（P<0.05），性别、结节部位差异无统计学意义(P>0.05)。两个观察者之间的类间相关系数>0.75。在通过LASSO算法降维后，选择了十个放射学特征，包括两个基于形状的特征，一个灰度共生矩阵(GLCM)，一个一阶特征，一个灰度游程长度矩阵(GLRLM)，三个灰度级相关矩阵（GLDM）和两个灰度级大小区域矩阵（GLSZM）。结合年龄和影像组学特征的逻辑回归模型在训练组中实现了0.87的AUC和0.82的准确性，在验证组中实现了0.82的AUC和0.84的准确性，用于预测结节生长。对于非线性模型，在训练组里,SVM的AUC，射频,MLP和增强模型分别为0.95、1.0、1.0和1.0。在验证组中，SVM的AUC，射频,MLP和增强模型分别为0.81、0.77、0.81和0.71。
未经批准：在这项研究中，我们建立了几个机器学习模型，可以成功预测一年内肺结节的生长。年龄和成像参数相结合的logistic回归模型具有最佳的准确性和泛化性。该模型对肺结节的早期治疗具有重要的临床意义。
