Zhao Jiabi, Sun Lin, Sun Ke, Wang Tingting, Wang Bin, Yang Yang, Wu Chunyan, Sun Xiwen
Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
Department of Radiation Medicine, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, China.
Front Oncol. 2021 Nov 9;11:759840. doi: 10.3389/fonc.2021.759840. eCollection 2021.
To establish a CT-based radiomics nomogram model for classifying pulmonary cryptococcosis (PC) and lung adenocarcinoma (LAC) in patients with a solitary pulmonary solid nodule (SPSN) and assess its differentiation ability.
A total of 213 patients with PC and 213 cases of LAC (matched based on age and gender) were recruited into this retrospective research with their clinical characteristics and radiological features. High-dimensional radiomics features were acquired from each mask delineated by radiologists manually. We adopted the max-relevance and min-redundancy (mRMR) approach to filter the redundant features and retained the relevant features at first. Then, we used the least absolute shrinkage and operator (LASSO) algorithms as an analysis tool to calculate the coefficients of features and remove the low-weight features. After multivariable logistic regression analysis, a radiomics nomogram model was constructed with clinical characteristics, radiological signs, and radiomics score. We calculated the performance assessment parameters, such as sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV), in various models. The receiver operating characteristic (ROC) curve analysis and the decision curve analysis (DCA) were drawn to visualize the diagnostic ability and the clinical benefit.
We extracted 1,130 radiomics features from each CT image. The 24 most significant radiomics features in distinguishing PC and LAC were retained, and the radiomics signature was constructed through a three-step feature selection process. Three factors-maximum diameter, lobulation, and pleural retraction-were still statistically significant in multivariate analysis and incorporated into a combined model with radiomics signature to develop the predictive nomogram, which showed excellent classification ability. The area under curve (AUC) yielded 0.91 (sensitivity, 80%; specificity, 83%; accuracy, 82%; NPV, 80%; PPV, 83%) and 0.89 (sensitivity, 81%; specificity, 83%; accuracy, 82%; NPV, 81%; PPV, 82%) in training and test cohorts, respectively. The net reclassification indexes (NRIs) were greater than zero ( < 0.05). The Delong test showed a significant difference ( < 0.0001) between the AUCs from the clinical model and the nomogram.
The radiomics technology can preoperatively differentiate PC and lung adenocarcinoma. The nomogram-integrated CT findings and radiomics feature can provide more clinical benefits in solitary pulmonary solid nodule diagnosis.
建立基于CT的影像组学列线图模型，用于对孤立性肺实性结节（SPSN）患者的肺隐球菌病（PC）和肺腺癌（LAC）进行分类，并评估其鉴别能力。
本回顾性研究纳入了213例PC患者和213例LAC患者（根据年龄和性别匹配），收集其临床特征和影像学特征。由放射科医生手动勾勒每个感兴趣区，从中提取高维影像组学特征。首先采用最大相关最小冗余（mRMR）方法筛选冗余特征，保留相关特征。然后，使用最小绝对收缩和选择算子（LASSO）算法作为分析工具计算特征系数，去除低权重特征。经过多变量逻辑回归分析，构建了包含临床特征、影像学征象和影像组学评分的影像组学列线图模型。我们计算了各种模型的性能评估参数，如敏感性、特异性、准确性、阴性预测值（NPV）和阳性预测值（PPV）。绘制了受试者工作特征（ROC）曲线分析图和决策曲线分析（DCA）图，以直观显示诊断能力和临床获益。
我们从每张CT图像中提取了1130个影像组学特征。保留了区分PC和LAC的24个最显著的影像组学特征，并通过三步特征选择过程构建了影像组学特征标签。在多变量分析中，最大直径、分叶和胸膜凹陷这三个因素仍具有统计学意义，并被纳入到与影像组学特征标签的联合模型中，以开发预测列线图，该列线图显示出优异的分类能力。训练队列和测试队列的曲线下面积（AUC）分别为0.91（敏感性80%；特异性83%；准确性82%；NPV 80%；PPV 83%）和0.89（敏感性81%；特异性83%；准确性82%；NPV 81%；PPV 82%）。净重新分类指数（NRI）大于零（<0.05）。德龙检验显示临床模型和列线图的AUC之间存在显著差异（<0.0001）。
影像组学技术可在术前鉴别PC和肺腺癌。整合了CT表现和影像组学特征的列线图在孤立性肺实性结节诊断中可提供更多临床获益。
