基于高恶性肿瘤患病率人群的 F-FDG PET/CT 预测模型在实性肺结节良恶性评估中的建立与验证。
Development and Validation of a F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based on a Population With High Prevalence of Malignancy.
机构信息
Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, PR China; Southern Medical University, Guangzhou, PR China.
Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, PR China.
出版信息
Clin Lung Cancer. 2020 Jan;21(1):47-55. doi: 10.1016/j.cllc.2019.07.014. Epub 2019 Aug 6.
PURPOSE
To develop a prediction model based on F-fludeoxyglucose (F-FDG) positron emission tomography/computed tomography (PET/CT) for solid pulmonary nodules (SPNs) with high malignant probability.
PATIENTS AND METHODS
We retrospectively reviewed the records of CT-undetermined SPNs, which were further evaluated by PET/CT between January 2008 and December 2015. A total of 312 cases were included as a training set and 159 as a validation set. Logistic regression was applied to determine independent predictors, and a mathematical model was deduced. The area under the receiver operating characteristic curve (AUC) was compared to other models. Model fitness was assessed based on the American College of Chest Physicians guidelines.
RESULTS
There were 215 (68.9%) and 127 (79.9%) malignant lesions in the training and validation sets, respectively. Eight independent predictors were identified: age [odds ratio (OR) = 1.030], male gender (OR = 0.268), smoking history (OR = 2.719), lesion diameter (OR = 1.067), spiculation (OR = 2.530), lobulation (OR = 2.614), cavity (OR = 2.847), and standardized maximum uptake value of SPNs (OR = 1.229). Our AUCs (training set, 0.858; validation set, 0.809) was better than those of previous models (Mayo: 0.685, P = .0061; Peking University People's Hospital: 0.646, P = .0180; Herder: 0.708, P = .0203; Zhejiang University: 0.757, P = .0699). The C index of the nomogram was 0.858. Our model reduced the diagnosis of indeterminate nodules (26.4% vs. 79.2%, 53.5%, 39.6%, and 34.0%, respectively) while improved sensitivity (81.3% vs. 16.4%, 49.2%, 62.5%, and 68.0%, respectively) and accuracy (65.4% vs. 16.4%, 39.6%, 52.8%, and 58.5%, respectively).
CONCLUSION
Our model could permit accurate diagnoses and may be recommended to identify malignant SPNs with high malignant probability, as our data pertain to a very high-prevalence cohort only.
目的
建立基于 F-氟代脱氧葡萄糖（F-FDG）正电子发射断层扫描/计算机断层扫描（PET/CT）的预测模型，用于评估具有高恶性概率的肺部实性结节（SPN）。
方法
我们回顾性分析了 2008 年 1 月至 2015 年 12 月期间 CT 不确定的 SPN 患者的记录，这些患者进一步通过 PET/CT 进行了评估。共纳入 312 例作为训练集，159 例作为验证集。应用逻辑回归确定独立预测因子，并推导出数学模型。比较受试者工作特征曲线（ROC）下面积（AUC）与其他模型的差异。根据美国胸科医师学会指南评估模型拟合度。
结果
训练集和验证集中分别有 215 例（68.9%）和 127 例（79.9%）恶性病变。确定了 8 个独立的预测因子：年龄[比值比（OR）=1.030]、男性（OR=0.268）、吸烟史（OR=2.719）、病变直径（OR=1.067）、分叶（OR=2.530）、不规则性（OR=2.614）、空洞（OR=2.847）和 SPN 的标准化最大摄取值（OR=1.229）。我们的 AUC（训练集为 0.858，验证集为 0.809）优于之前的模型（Mayo：0.685，P=0.0061；北京大学人民医院：0.646，P=0.0180；Herder：0.708，P=0.0203；浙江大学：0.757，P=0.0699）。列线图的 C 指数为 0.858。我们的模型减少了不确定结节的诊断（分别为 26.4%、79.2%、53.5%、39.6%和 34.0%），同时提高了敏感性（分别为 81.3%、16.4%、49.2%、62.5%和 68.0%）和准确性（分别为 65.4%、16.4%、39.6%、52.8%和 58.5%）。
结论
我们的模型可以进行准确的诊断，可能有助于识别具有高恶性概率的恶性 SPN，但我们的数据仅适用于高患病率的队列。
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