PDF(Pubmed)
Abstract：
Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model have the potential to improve its diagnostic capabilities. The purpose of study was to evaluate the performance of deep learning model in distinguishing tuberculosis (TB) nodules and lung cancer (LC) based on deep learning features, radiomic features, and clinical information.
Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics-DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1-score metrics.
The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82-0.88), sensitivity of 0.85 (0.80-0.88), and specificity of 0.84 (0.83-0.87), performing better than the other models.
The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC.
Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics-DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1-score metrics.
The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82-0.88), sensitivity of 0.85 (0.80-0.88), and specificity of 0.84 (0.83-0.87), performing better than the other models.
The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC.
摘要：
背景：放射学诊断模型通常只考虑单个维度的信息，导致其诊断准确性和可靠性受到限制。将多个维度的信息集成到深度学习模型中有可能提高其诊断能力。研究的目的是评估基于深度学习特征的深度学习模型在区分结核病（TB）结节和肺癌（LC）中的性能。放射学特征，和临床信息。
方法：收集了97例LC患者和77例TB结节患者的正电子发射断层扫描（PET）和计算机断层扫描（CT）图像数据。使用pyradiogomics平台从PET和CT成像中提取了一百个放射学特征，通过残差神经网络方法获得2048个深度学习特征。四个模型包括传统机器学习模型，以放射学特征作为输入(传统放射学)，具有图像特征单独输入的深度学习模型(深度卷积神经网络[DCNN])，具有放射学特征和深度学习特征的两个输入的深度学习模型(放射学-DCNN)和具有放射学特征和深度学习特征和临床信息的输入的深度学习模型(集成模型)。使用曲线下面积（AUC）评估模型，灵敏度,准确度,特异性，和F1分数指标。
结果：TB结节和LC的分类结果表明，集成模型实现了0.84（0.82-0.88）的AUC，灵敏度为0.85（0.80-0.88），特异性为0.84（0.83-0.87），比其他模型表现更好。
结论：发现整合模型是诊断结核结节和实体LC的最佳分类模型。
方法：收集了97例LC患者和77例TB结节患者的正电子发射断层扫描（PET）和计算机断层扫描（CT）图像数据。使用pyradiogomics平台从PET和CT成像中提取了一百个放射学特征，通过残差神经网络方法获得2048个深度学习特征。四个模型包括传统机器学习模型，以放射学特征作为输入(传统放射学)，具有图像特征单独输入的深度学习模型(深度卷积神经网络[DCNN])，具有放射学特征和深度学习特征的两个输入的深度学习模型(放射学-DCNN)和具有放射学特征和深度学习特征和临床信息的输入的深度学习模型(集成模型)。使用曲线下面积（AUC）评估模型，灵敏度,准确度,特异性，和F1分数指标。
结果：TB结节和LC的分类结果表明，集成模型实现了0.84（0.82-0.88）的AUC，灵敏度为0.85（0.80-0.88），特异性为0.84（0.83-0.87），比其他模型表现更好。
结论：发现整合模型是诊断结核结节和实体LC的最佳分类模型。
