Establishment and verification of prediction model for benign or malignant of≤20 mm solitary pulmonary nodules
10.3760/cma.j.cn112149-20200828-01037
- VernacularTitle:≤20 mm孤立性肺结节良恶性预测模型的建立与验证
- Author:
Hua ZHONG
1
;
Anqi LI
;
Jianghe KANG
;
Jin′an WANG
;
Shaoyin DUAN
Author Information
1. 厦门大学附属中山医院影像科 361004
- Keywords:
Lung neoplasms;
Artificial intelligence;
Prediction model;
Nomogram
- From:
Chinese Journal of Radiology
2021;55(7):745-750
- CountryChina
- Language:Chinese
-
Abstract:
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.
