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Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based on a Population With High Prevalence of Malignancy.
Clin Lung Cancer. 2020 Jan; 21(1):47-55.CL

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.

Authors+Show Affiliations

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.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; 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; 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; 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.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.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.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.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31474376

Citation

Guo, Hao-Yue, et al. "Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based On a Population With High Prevalence of Malignancy." Clinical Lung Cancer, vol. 21, no. 1, 2020, pp. 47-55.
Guo HY, Lin JT, Huang HH, et al. Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based on a Population With High Prevalence of Malignancy. Clin Lung Cancer. 2020;21(1):47-55.
Guo, H. Y., Lin, J. T., Huang, H. H., Gao, Y., Yan, M. R., Sun, M., Xu, W. P., Yan, H. H., Zhong, W. Z., & Yang, X. N. (2020). Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based on a Population With High Prevalence of Malignancy. Clinical Lung Cancer, 21(1), 47-55. https://doi.org/10.1016/j.cllc.2019.07.014
Guo HY, et al. Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based On a Population With High Prevalence of Malignancy. Clin Lung Cancer. 2020;21(1):47-55. PubMed PMID: 31474376.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based on a Population With High Prevalence of Malignancy. AU - Guo,Hao-Yue, AU - Lin,Jun-Tao, AU - Huang,Hao-Hua, AU - Gao,Yuan, AU - Yan,Mei-Ru, AU - Sun,Ming, AU - Xu,Wei-Ping, AU - Yan,Hong-Hong, AU - Zhong,Wen-Zhao, AU - Yang,Xue-Ning, Y1 - 2019/08/06/ PY - 2019/05/11/received PY - 2019/07/27/revised PY - 2019/07/31/accepted PY - 2019/9/3/pubmed PY - 2019/9/3/medline PY - 2019/9/3/entrez KW - ACCP KW - Decision analysis KW - Diagnose KW - Lung cancer KW - Nomogram SP - 47 EP - 55 JF - Clinical lung cancer JO - Clin Lung Cancer VL - 21 IS - 1 N2 - 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. SN - 1938-0690 UR - https://www.unboundmedicine.com/medline/citation/31474376/Development_and_Validation_of_a_18F-FDG_PET/CT-Based_Clinical_Prediction_Model_for_Estimating_Malignancy_in_Solid_Pulmonary_Nodules_Based_on_a_Population_With_High_Prevalence_of_Malignancy L2 - https://linkinghub.elsevier.com/retrieve/pii/S1525-7304(19)30227-X DB - PRIME DP - Unbound Medicine ER -
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