背景:本研究旨在基于胸部CT图像构建人工智能模型,以对良性胸腔积液(BPE)和恶性胸腔积液(MPE)进行分割和分类。
方法:共纳入918例胸腔积液患者,其中607例随机选择的病例用作训练队列,另外311例作为内部测试队列;另一个独立的外部测试队列包含362例.我们通过将3D空间加权U-Net与2D经典U-Net相结合,开发了胸腔积液分割模型(M1)。然后,使用CT体积及其3D胸腔积液面罩作为输入,建立了分类模型(M2)来识别BPE和MPE.
结果:平均骰子相似系数,Jaccard系数,精度,灵敏度,Hausdorff距离95%(HD95)和M1中的平均表面距离指标为87.6±5.0%,82.2±6.2%,99.0±1.0%,83.0±6.6%,分别为6.9±3.8和1.6±1.1,优于3DU-Net和3D空间加权U-Net。关于M2,接收器工作特性曲线下的面积,用体积浓度掩模作为输入获得的灵敏度和特异性为0.842(95%CI0.801至0.878),外部测试队列中的89.4%(95%CI84.4%至93.2%)和65.1%(95%CI57.3%至72.3%)。与其他输入模式相比,这些性能指标得到了显着改善。
结论:我们将深度学习模型应用于胸腔积液的分割,该模型在BPE和MPE的鉴别诊断中表现出令人鼓舞的性能。
This study aimed to construct artificial intelligence models based on thoracic CT images to perform segmentation and classification of benign pleural effusion (BPE) and malignant pleural effusion (MPE).
A total of 918 patients with pleural effusion were initially included, with 607 randomly selected cases used as the training cohort and the other 311 as the internal testing cohort; another independent external testing cohort with 362 cases was used. We developed a pleural effusion segmentation model (M1) by combining 3D spatially weighted U-Net with 2D classical U-Net. Then, a classification model (M2) was built to identify BPE and MPE using a CT volume and its 3D pleural effusion mask as inputs.
The average Dice similarity coefficient, Jaccard coefficient, precision, sensitivity, Hausdorff distance 95% (HD95) and average surface distance indicators in M1 were 87.6±5.0%, 82.2±6.2%, 99.0±1.0%, 83.0±6.6%, 6.9±3.8 and 1.6±1.1, respectively, which were better than those of the 3D U-Net and 3D spatially weighted U-Net. Regarding M2, the area under the receiver operating characteristic curve, sensitivity and specificity obtained with volume concat masks as input were 0.842 (95% CI 0.801 to 0.878), 89.4% (95% CI 84.4% to 93.2%) and 65.1% (95% CI 57.3% to 72.3%) in the external testing cohort. These performance metrics were significantly improved compared with those for the other input patterns.
We applied a deep learning model to the segmentation of pleural effusions, and the model showed encouraging performance in the differential diagnosis of BPE and MPE.