Hao-Yue Guo1, Jun-Tao Lin2, Hao-Hua Huang1, Yuan Gao1, Mei-Ru Yan1, Ming Sun1, Wei-Ping Xu2, Hong-Hong Yan2, Wen-Zhao Zhong2, Xue-Ning Yang3. 1. 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. 2. 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. 3. 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. Electronic address: yangxuening@gdph.org.cn.
Abstract
PURPOSE: To develop a prediction model based on 18F-fludeoxyglucose (18F-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.
PURPOSE: To develop a prediction model based on 18F-fludeoxyglucose (18F-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.