Guo Y, Zhu H, Chen X, Qin S, Liu F G
Department of Nuclear Medicine, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing100730, China.
Zhonghua Yi Xue Za Zhi. 2025 Jan 28;105(4):284-290. doi: 10.3760/cma.j.cn112137-20240708-01547.
To establish and validate a nomogram based on clinical characteristics and metabolic parameters derived from F-fluorodeoxyglucose positron emission tomography and computed tomography (F-FDG PET/CT) for prediction of high-grade patterns (HGP) in invasive lung adenocarcinoma. The clinical and PET/CT image data of 311 patients who were confirmed invasive lung adenocarcinoma and underwent pre-treatment F-FDG PET/CT scan in Beijing Hospital between October 2017 and March 2022 were retrospectively collected. The enrolled patients were divided into HGP group (196 patients) and non-HGP group (115 patients) according to the presence and absence of HGP. The data were divided into training set and validation set at 7∶3 ratio using R statistical software and simple random allocation. A nomogram prediction model was constructed in training set. The area under the curve (AUC) of receiver operating characteristic (ROC) was depicted in the training and validation set respectively for assessing the prediction efficacy. The goodness of fit, consistency between predicted and observed probability and clinical usefulness of the model were evaluated by Hosmer-Lemeshow test, calibration curve and decision curve analysis (DCA). The age of 311 patients were (65.6±10.9) years and included 148 males (47.6%). In training set of 217 patients, 141 (65.0%) contained HGP while in validation set of 94 patients, 55 (58.5%) contained HGP. Gender in training set, serum carcino-embryonic antigen (CEA) in validation set, smoking history, clinical stage, cytokeratin fragments (CYFRA21-1), maximum standardized uptake value (SUV), mean standardized uptake value (SUV), metabolic tumor volume (MTV), total lesion glycolysis (TLG) and maximum diameter (D) in both sets showed significant differences between HGP and non-HGP groups (all <0.05). The variables integrated in the model were gender, clinical stage, CYFRA21-1, SUV and TLG. The AUC (95%) of the ROC curve in training and validation set were 0.888 (0.844-0.932) and 0.925 (0.872-0.977), the sensitivity and specificity were 85.1%, 79.0% and 83.6%, 89.7%, respectively. The model showed good goodness of fit (training set: χ=8.247, =0.410, validation set: χ=1.636, =0.990). Calibration curve and DCA also indicated good consistency and clinical net benefit of the nomogram model. The nomogram model based on clinical features and metabolic parameters derived from F-FDG PET/CT could effectively predict the presence of HGP in invasive lung adenocarcinoma and be beneficial to treatment planning.
基于临床特征和从氟脱氧葡萄糖正电子发射断层扫描和计算机断层扫描（F-FDG PET/CT）得出的代谢参数建立并验证列线图，以预测浸润性肺腺癌的高级别模式（HGP）。回顾性收集2017年10月至2022年3月期间在北京医院确诊为浸润性肺腺癌并接受治疗前F-FDG PET/CT扫描的311例患者的临床和PET/CT图像数据。根据是否存在HGP将纳入的患者分为HGP组（196例）和非HGP组（115例）。使用R统计软件并通过简单随机分配以7∶3的比例将数据分为训练集和验证集。在训练集中构建列线图预测模型。分别在训练集和验证集中描绘受试者工作特征（ROC）曲线下面积（AUC），以评估预测效能。通过Hosmer-Lemeshow检验、校准曲线和决策曲线分析（DCA）评估模型的拟合优度、预测概率与观察概率之间的一致性以及临床实用性。311例患者的年龄为（65.6±10.9）岁，其中男性148例（47.6%）。在217例患者的训练集中，141例（65.0%）包含HGP，而在94例患者的验证集中，55例（58.5%）包含HGP。训练集中的性别、验证集中的血清癌胚抗原（CEA）、吸烟史、临床分期、细胞角蛋白片段（CYFRA21-1）、最大标准化摄取值（SUV）、平均标准化摄取值（SUV）、代谢肿瘤体积（MTV）、总病灶糖酵解（TLG）和两组中的最大直径（D）在HGP组和非HGP组之间均显示出显著差异（均<0.05）。纳入模型的变量为性别、临床分期、CYFRA21-1、SUV和TLG。训练集和验证集中ROC曲线的AUC（95%）分别为0.888（0.844-0.932）和0.925（0.872-0.977），敏感性和特异性分别为85.1%、79.0%和83.6%、89.7%。该模型显示出良好的拟合优度（训练集：χ=8.247，=0.410，验证集：χ=1.636，=0.990）。校准曲线和DCA也表明列线图模型具有良好的一致性和临床净效益。基于临床特征和从F-FDG PET/CT得出的代谢参数的列线图模型可以有效预测浸润性肺腺癌中HGP的存在，并有利于治疗计划。
