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| Value of intranodular and perinodular radiomic features to distinguish malignant from
benign pulmonary nodules（≤2 cm） |
| ZHANG Jing1, WU Zhi-feng2, E Lin-ning2, WANG Rong-hua2, ZHANG Na2 |
| 1. Department of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
2. CT Division, Image Center, Shanxi Academy of Medical Science, Shanxi Dayi Hospital, Taiyuan 030032, China |
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Abstract Objective: To investigate the value of intranodular and perinodular radiomic features to distinguish malignant from benign pulmonary nodules(≤2 cm). Methods: A retrospective analysis of 206 patients with pulmonary nodules with noncontrast CT images, including 106 malignant patients and 100 benign patients, was performed by two radiologists with 3 and 10 years of chest CT imaging experience, respectively, to evaluate without pathological results. At the same time, radiomic analysis was performed on 206 cases of nodules, and VOIs were delineated by 3D slicer, radiomic features were extracted by Analysis-Kinetics(A.K.) analysis software. The Lasso-logistic regression was performed to select features, and to establish lung nodule models, models of combining intranodular with perinodular 5 mm, 10 mm, and 15 mm tissue, respectively. ROC curve analysis and Delong test was used to compare the diagnostic efficacy among models and radiologists. Results: The AUC values of the two radiologists were 0.81 and 0.69. In the validation group, the AUC value of the lung nodule model was 0.82, and the AUC values of combining intranodular with perinodular 5 mm, 10 mm, and 15 mm tissue models were 0.88, 0.76, and 0.82. Except for the combining intranodular with perinodular 10 mm tissue model, the efficacy of the other models was higher than that of the physician group, but there was no significant difference in efficacy between the models(DeLong test, P>0.05). Conclusion: For lung nodules ≤2 cm, the models based on intranodular and perinodular radiomic features can improve the ability to distinguish benign and malignant nodules.
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Received: 18 December 2019
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