PDF(Pubmed)
Abstract：
OBJECTIVE: To evaluate the performance of a clinical-radiomics model for differentiating focal organizing pneumonia (FOP) and lung adenocarcinoma (LUAD).
METHODS: 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.
RESULTS: Statistical analysis revealed that an history of was an independent risk factor for FOP (P=0.016), which was correlated with none of the hematological findings (P > 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% CI: 0.855-0.963) in the training set and 0.901 (95% CI: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: Statistical analysis revealed that an history of was an independent risk factor for FOP (P=0.016), which was correlated with none of the hematological findings (P > 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% CI: 0.855-0.963) in the training set and 0.901 (95% CI: 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.
CONCLUSIONS: 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.
摘要：
目的：评估临床影像组学模型在区分局灶性机化性肺炎（FOP）和肺腺癌（LUAD）方面的性能。
方法：回顾性分析1月解放军总医院第一医学中心经术后病理证实的60例FOP患者的临床资料，2019年12月,2022年，他们与120名LUAD患者进行了匹配，使用倾向评分匹配，比例为1∶2。通过对患者临床数据的logistic回归分析，确定了FOP的独立危险因素。通过随机抽样将队列分为训练集（144名患者）和测试集（36名患者）。Python3.7用于从患者的CT图像数据中提取1835个特征。影像学特征和临床数据用于构建模型，在训练集和测试集中使用ROC曲线验证了其性能。评估了该模型对FOP和LUAD的诊断功效，并构建了诊断列线图。
结果：统计分析显示病史是FOP的独立危险因素（P=0.016），与血液学检查结果无相关性(P>0.05)。影像组学中的特征提取和降维产生了30种用于区分两种疾病的重要标记。前3个最具鉴别力的放射组学标签是灰度不均匀性，SizeZoneNononUniformityandshape-Sphericity.临床影像组学模型在训练集中实现了0.909（95％CI：0.855-0.963）的AUC，在测试集中实现了0.901（95％CI：0.803-0.999）。该模型的灵敏度为85.4%，特异性为83.5%，训练集中的准确率为84.0%，与94.7%相比，70.6%,和83.3%的测试集，分别。
结论：临床-影像组学列线图模型对FOP和LUAD的鉴别诊断表现良好，可能有助于减少误诊相关的过度治疗并改善患者预后。
方法：回顾性分析1月解放军总医院第一医学中心经术后病理证实的60例FOP患者的临床资料，2019年12月,2022年，他们与120名LUAD患者进行了匹配，使用倾向评分匹配，比例为1∶2。通过对患者临床数据的logistic回归分析，确定了FOP的独立危险因素。通过随机抽样将队列分为训练集（144名患者）和测试集（36名患者）。Python3.7用于从患者的CT图像数据中提取1835个特征。影像学特征和临床数据用于构建模型，在训练集和测试集中使用ROC曲线验证了其性能。评估了该模型对FOP和LUAD的诊断功效，并构建了诊断列线图。
结果：统计分析显示病史是FOP的独立危险因素（P=0.016），与血液学检查结果无相关性(P>0.05)。影像组学中的特征提取和降维产生了30种用于区分两种疾病的重要标记。前3个最具鉴别力的放射组学标签是灰度不均匀性，SizeZoneNononUniformityandshape-Sphericity.临床影像组学模型在训练集中实现了0.909（95％CI：0.855-0.963）的AUC，在测试集中实现了0.901（95％CI：0.803-0.999）。该模型的灵敏度为85.4%，特异性为83.5%，训练集中的准确率为84.0%，与94.7%相比，70.6%,和83.3%的测试集，分别。
结论：临床-影像组学列线图模型对FOP和LUAD的鉴别诊断表现良好，可能有助于减少误诊相关的过度治疗并改善患者预后。
