Guo Rui, Yan Shi, Wang Fei, Su Hua, Xie Qing, Zhao Wei, Yang Zhi, Li Nan, Yu Jiangyuan
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), National Medical Products Administration (NPMA) Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China.
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Surgery II, Peking University Cancer Hospital and Institute, Beijing, China.
Front Oncol. 2022 Oct 24;12:1017618. doi: 10.3389/fonc.2022.1017618. eCollection 2022.
This study aimed to evaluate the F-FDG PET/CT in differentiating lung metastasis(LM) from primary lung cancer(LC) in patients with colorectal cancer (CRC).
A total of 120 CRC patients (80 male, 40 female) who underwent F-FDG PET/CT were included. The diagnosis of primary lung cancer or lung metastasis was based on histopathology The patients were divided into a training cohort and a validation cohort randomized 1:1. Independent risk factors were extracted through the clinical information and F-FDG PET/CT imaging characteristics of patients in the validation cohort, and then a diagnostic model was constructed and a nomograms was made. ROC curve, calibration curve, cutoff, sensitivity, specificity, and accuracy were used to evaluate the prediction performance of the diagnostic model.
One hundred and twenty Indeterminate lung lesions (ILLs) (77 lung metastasis, 43 primary lung cancer) were analyzed. No significant difference in clinical characteristics and imaging features between the training and the validation cohorts ( > 0. 05). Using uni-/multivariate analysis, pleural tags and contour were identified as independent predictors. These independent predictors were used to establish a diagnostic model with areas under the receiver operating characteristic curves (AUCs) of 0.92 and 0.89 in the primary and validation cohorts, respectively. The accuracy rate of the diagnostic model for differentiating LM from LC were higher than that of subjective diagnosis ( < 0.05).
Pleural tags and contour were identified as independent predictors. The diagnostic model of ILLs in patients with CRC could help differentiate between LM and LC.
本研究旨在评估F-FDG PET/CT在鉴别结直肠癌（CRC）患者的肺转移（LM）与原发性肺癌（LC）方面的作用。
纳入120例行F-FDG PET/CT检查的CRC患者（男性80例，女性40例）。原发性肺癌或肺转移的诊断基于组织病理学。患者按1:1随机分为训练队列和验证队列。通过验证队列患者的临床信息和F-FDG PET/CT影像特征提取独立危险因素，然后构建诊断模型并绘制列线图。采用ROC曲线、校准曲线、截断值、敏感性、特异性和准确性评估诊断模型的预测性能。
分析了120个肺内不确定病变（ILLs）（77个肺转移，43个原发性肺癌）。训练队列和验证队列之间的临床特征和影像特征无显著差异（>0.05）。采用单因素/多因素分析，胸膜征和轮廓被确定为独立预测因素。这些独立预测因素用于建立诊断模型，在原发性队列和验证队列中，受试者操作特征曲线（AUC）下面积分别为0.92和0.89。该诊断模型区分LM与LC的准确率高于主观诊断（<0.05）。
胸膜征和轮廓被确定为独立预测因素。CRC患者ILLs的诊断模型有助于区分LM和LC。
