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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
- Source :
- Clinical Lung Cancer. 21:47-55
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
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.
- Subjects :
- 0301 basic medicine
Pulmonary and Respiratory Medicine
Cancer Research
education.field_of_study
medicine.medical_specialty
medicine.diagnostic_test
Receiver operating characteristic
business.industry
Population
Odds ratio
Nomogram
medicine.disease
Malignancy
Logistic regression
03 medical and health sciences
030104 developmental biology
0302 clinical medicine
Oncology
Positron emission tomography
030220 oncology & carcinogenesis
medicine
Radiology
Lung cancer
education
business
Subjects
Details
- ISSN :
- 15257304
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- Clinical Lung Cancer
- Accession number :
- edsair.doi...........6b968cb272d3c2d872f9ba06cb740153