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Objective:To establish and verify the prediction model of benign or malignant of solitary pulmonary nodules (SPNs≤20 mm) based on artificial intelligence.Methods:Totally 338 SPNs (≤20 mm) from 279 patients, confirmed by operation and pathology, were selected in Zhongshan Hospital Xiamen University from November 2018 to May 2020. Clinical data (age, gender, smoking history, individual and family history of malignancy), image features (maximum diameter, minimum diameter, solid proportion, volume, lobulation sign, burr sign, vacuole sign, cavity sign, pleural indentation sign, and radiomic features (maximum CT value, minimum CT value, average CT value, median CT value, CT value standard deviation, skewness, peak, energy, entropy) were analyzed retrospectively. All the data of patients were randomly divided into training set (271 SPNs) and test set (67 SPNs). In the training set, the clinical, image features and radiomic features were first selected by the least absolute shrinkage and selection operator (LASSO) regression, then the independent risk factors of SPN (≤20 mm) were screened out by multi-variate logistic regression analysis, and the nomogram prediction models were constructed. Finally, the data of test set were used to verify the prediction model by the ROC curve and calibration curve (CC).Results:In the training set of 271 SPNs, 81 SPNs were benign and 190 malignant. After analysis of LASSO regression and multi-factor logistics regression, the independent predictors of benign or malignant SPN were age, gender, largest diameter, vacuole sign and solid proportion. The prediction model was P=e x/(1+e x), x=-2.583+0.027×age+1.519×gender+0.127×maximum diameter-2.132×solid proportion+1.720×vacuole sign. The results of the model showed that the area under curve (AUC) of ROC was 0.850, and the sensitivity was 73.7%, specificity was 82.7% and accuracy was 82.3%. In the test set of 67 SPNs, 22 SPNs were benign and 45 malignant. The results showed that the AUC of ROC was 0.882, and the sensitivity was 82.2%, specificity was 81.8% and accuracy was 85.1%. The calibration nomogram of prediction model showed that CC from training set or test set well coincided with its individual ideal curve ( Ptraining=0.688, Ptest=0.618). Conclusion:Prediction model of benign or malignant SPN ≤20 mm is established based on AI; it can obtain the prediction probability and has good diagnostic efficiency.
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BackgroundThe corona-virus disease 2019 (COVID-19) pandemic has caused a serious public health risk. Compared with conventional high-resolution CT (C-HRCT, matrix 512), ultra-high resolution CT (U-HRCT, matrix 1024) can increase the effective pixel per unit volume by about 4 times. Our study is to evaluate the value of target reconstruction of U-HRCT in the accurate diagnosis of COVID-19. MethodsA total of 13 COVID-19 cases, 44 cases of other pneumonias, and 6 cases of ground-glass nodules were retrospectively analyzed. The data were categorized into groups A (C-HRCT) and B (U-HRCT), following which iDose4-3 and iDose4-5 were used for target reconstruction, respectively. CT value, noise, and signal-to-noise ratio (SNR) in different reconstructed images were measured. Two senior imaging doctors scored the image quality and the structure of the lesions on a 5-point scale. Chi-square test, variance analysis, and binarylogistic regression analysis were used for statistical analysis. ResultsU-HRCT image can reduce noise and improve SNR with an increase of the iterative reconstruction level. The SNR of U-HRCT image was lower than that of the C-HRCT image of the same iDose4level, and the noise of U-HRCT was higher than that of C-HRCT image; the difference was statistically significant (P< 0.05). Logistic regression analysis showed thatperipleural distribution, thickening of blood vessels and interlobular septum, and crazy-paving pattern were independent indictors of the COVID-19 on U-HRCT. U-HRCT was superior to C-HRCT in showing the blood vessels, bronchial wall, and interlobular septum in the ground-glass opacities; the difference was statistically significant (P < 0.05). ConclusionsPeripleural distribution, thickening of blood vessels and interlobular septum, and crazy-paving pattern on U-HRCT are favorable signs for COVID-19. U-HRCT is superior to C-HRCT in displaying the blood vessels, bronchial walls, and interlobular septum for evaluating COVID-19.
