Liu Y, Li C, Guo J, Liu Y
Graduate School, Chinese PLA General Hospital, Beijing 100853, China.
Department of Thoracic Surgery of First Medical Center, Chinese PLA General Hospital, Beijing 100853, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2024 Feb 20;44(2):397-404. doi: 10.12122/j.issn.1673-4254.2024.02.23.
To evaluate the performance of a clinical-radiomics model for differentiating focal organizing pneumonia (FOP) and lung adenocarcinoma (LUAD).
We retrospectively analyzed the data of 60 patients with FOP confirmed by postoperative pathology at the First Medical Center of the Chinese PLA General Hospital from January, 2019 to December, 2022, who were matched with 120 LUAD patients using propensity score matching in a 1∶2 ratio. The independent risk factors for FOP were identified by logistic regression analysis of the patients' clinical data. The cohort was divided into a training set (144 patients) and a test set (36 patients) by random sampling. Python 3.7 was used for extracting 1835 features from CT image data of the patients. The radiographic features and clinical data were used to construct the model, whose performance was validated using ROC curves in both the training and test sets. The diagnostic efficacy of the model for FOP and LUAD was evaluated and a diagnostic nomogram was constructed.
Statistical analysis revealed that an history of was an independent risk factor for FOP (=0.016), which was correlated with none of the hematological findings ( > 0.05). Feature extraction and dimensionality reduction in radiomics yielded 30 significant labels for distinguishing the two diseases. The top 3 most discriminative radiomics labels were GraylevelNonUniformity, SizeZoneNonUniformity and shape-Sphericity. The clinical-radiomics model achieved an AUC of 0.909 (95% : 0.855-0.963) in the training set and 0.901 (95% : 0.803-0.999) in the test set. The model showed a sensitivity of 85.4%, a specificity of 83.5%, and an accuracy of 84.0% in the training set, as compared with 94.7%, 70.6%, and 83.3% in the test set, respectively.
The clinical-radiomics nomogram model shows a good performance for differential diagnosis of FOP and LUAD and may help to minimize misdiagnosis-related overtreatment and improve the patients' outcomes.
评估一种临床影像组学模型鉴别局灶性机化性肺炎（FOP）和肺腺癌（LUAD）的性能。
我们回顾性分析了2019年1月至2022年12月在中国人民解放军总医院第一医学中心经术后病理确诊的60例FOP患者的数据，这些患者与120例LUAD患者采用倾向得分匹配法按1∶2的比例进行匹配。通过对患者临床数据进行逻辑回归分析确定FOP的独立危险因素。通过随机抽样将队列分为训练集（144例患者）和测试集（36例患者）。使用Python 3.7从患者的CT图像数据中提取1835个特征。利用影像特征和临床数据构建模型，并在训练集和测试集中使用ROC曲线验证其性能。评估该模型对FOP和LUAD的诊断效能并构建诊断列线图。
统计分析显示，[此处原文缺失具体内容]是FOP的独立危险因素（P = 0.016），且与血液学检查结果均无相关性（P>0.05）。影像组学中的特征提取和降维产生了30个区分这两种疾病的显著标签。最具鉴别力的前3个影像组学标签是灰度非均匀性、大小区域非均匀性和形状 - 球形度。临床影像组学模型在训练集中的AUC为0.909（95%CI：0.855 - 0.963），在测试集中为0.901（95%CI：0.803 - 0.999）。该模型在训练集中的敏感性为85.4%，特异性为83.5%，准确性为84.0%，而在测试集中分别为94.7%、70.6%和83.3%。
临床影像组学列线图模型在FOP和LUAD的鉴别诊断中表现良好，可能有助于最大限度减少与误诊相关的过度治疗并改善患者预后。
