A model of malignant risk prediction for solitary pulmonary nodules on 18F‐FDG PET/CT: Building and estimating
Background To develop a model of malignant risk prediction of solitary pulmonary nodules (SPNs) using metabolic characteristics of lesions. Methods A total of 362 patients who underwent PET/CT imaging from January 2013 to July 2017 were analyzed. Differences in the clinical and imaging characteristi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-05-01
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| Series: | Thoracic Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/1759-7714.13375 |
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| Summary: | Background To develop a model of malignant risk prediction of solitary pulmonary nodules (SPNs) using metabolic characteristics of lesions. Methods A total of 362 patients who underwent PET/CT imaging from January 2013 to July 2017 were analyzed. Differences in the clinical and imaging characteristics were analyzed between patients with benign SPNs and those with malignant SPNs. Risk factors were screened by multivariate nonconditional logistic regression analysis. The self‐verification of the model was performed by receiver operating characteristic (ROC) curve analysis, and out‐of‐group verification was performed by k‐fold cross‐validation. Results There were statistically significant differences in age, maximum standardized uptake value (SUVmax), size, lobulation, spiculation, pleural traction, vessel connection, calcification, presence of vacuoles, and emphysema between patients with benign nodules and those with malignant nodules (all P < 0.05). The risk factors for malignant nodules included age, SUVmax, size, lobulation, calcification and vacuoles. The logistic regression model was as follows: P = l/(1 + e‐x), x = − 5.583 + 0.039 × age + 0.477 × SUVmax + 0.139 × size + 1.537 × lobulation – 1.532 × calcification + 1.113 × vacuole. The estimated area under the curve (AUC) for the model was 0.915 (95% CI: 0.883–0.947), the sensitivity was 89.7%, and the specificity was 78.9%. K‐fold cross‐validation showed that the training accuracy was 0.899 ± 0.011, and the predictive accuracy was 0.873 ± 0.053. Conclusions The risk factors for malignant nodules included age, SUVmax, size, lobulation, calcification and vacuoles. After verification, the model has satisfactory accuracy, and it may assist clinics make appropriate treatment decisions. |
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| ISSN: | 1759-7706 1759-7714 |