Keywords
Magnetic resonance imaging
Radiomics
Submitted : 2025-07-05
Accepted : 2025-07-20
Published : 2025-08-04
Abstract
Objective: To investigate the application of experience-based and Magnetic Resonance Imaging (MRI) radiomics in differentiating benign and malignant pulmonary nodules and masses. Methods: Sixty patients with pulmonary nodules or masses admitted from April 2024 to April 2025 were selected as study subjects. Surgical pathology was used as the gold standard to explore the differential diagnostic value of experience-based and MRI radiomics. Results: Twenty one MRI radiomic features were selected for screening, with 14 features removed and 7 remaining for normality testing. Using surgical pathology as the gold standard, among the 60 patients, 36 were benign and 24 were malignant, with detection rates of 60.00% and 40.00%, respectively. The detection rates of lobulation, vascular convergence sign, and GGO components in benign pulmonary nodules/masses were lower than those in malignant ones (P < 0.05). The radiomics formula was Radscore = Intercept + Weight (Feature) × Feature, with the calculated formula being Radscore = 0.16 - 0.32 × Sphericity + 0.11 × Mass. The AUC of the UTE Rad-score model was 0.672, which was lower than the AUC of the UTE nomogram (0.789) and the combined model (0.832) (P < 0.05). Conclusion: Experience-based and MRI radiomics can play a significant role in differentiating benign and malignant pulmonary nodules and masses, with the combined model demonstrating more prominent diagnostic value.
References
Deng H, Zheng L, Wu D, et al., 2022, Observation of X-ray and MRI Diagnostic Imaging Features of Patients with Solitary Pulmonary Nodules. Chinese Journal of CT and MRI, 20(9): 58–59.
Jiang Y, Pu D, Yu N, 2023, Research Progress of Radiomics Based on Magnetic Resonance Imaging in Lung Cancer. Journal of Magnetic Resonance Imaging, 14(7): 166–170.
Yu B, Qian W, Li L, et al., 2025, Study on the Value of MRI Multi-b-Value DWI Quantitative Parameters in Diagnosing Benign and Malignant Solid Pulmonary Nodules. Chinese Journal of CT and MRI, 23(1): 63–65, 81.
Zhang J, Xiong Z, Li Z, 2023, Research Progress and Prospects of Ultra-Short Echo Time Magnetic Resonance Imaging for Pulmonary Nodules. Journal of Magnetic Resonance Imaging, 14(1): 183–188.
Wang X, Chen J, Gu H, et al., 2023, Study on the Differential Diagnosis Efficacy of Magnetic Resonance Diffusion-Weighted Imaging for Benign and Malignant Solitary Pulmonary Nodules. China Medical Equipment, 20(10): 80–84.
Xiang L, Qin Y, Yang H, et al., 2023, Value of IVIM-DWI Global Histogram Parameters in Differentiating Benign and Malignant Solitary Pulmonary Nodules. Radiology Practice, 38(12): 1539–1547.
Liu X, Kan X, Wang Y, et al., 2025, Application Value of MRI Multi-Parameter in Differential Diagnosis of Solid Solitary Pulmonary Nodules. Journal of Clinical Radiology, 44(1): 87–94.
Hu J, Liu M, Zhao W, et al., 2023, Diagnostic Value of T1WI Star-VIBE Enhancement Combined with TWIST-VIBE Dynamic Contrast-Enhanced MRI for Pulmonary Nodules. Journal of Central South University (Medical Sciences), 48(4): 581–593.
Xia Z, Liu Z, Hu Q, et al., 2024, Construction and Validation of a Predictive Model for Lung Nodule Invasiveness Based on the XGBoost Machine Learning Algorithm: A Two-Center Study. Chinese Journal of CT and MRI, 22(8): 166–169.
Lou J, Meng S, Qin Q, et al., 2024, Exploring the Value of MRI and CT Haralick Texture Parameters in Diagnosing Benign and Malignant Pulmonary Nodules. Chinese Journal of CT and MRI, 22(7): 56–59.
Ma B, Zhu J, Chen J, et al., 2022, Discussion on the Clinical Value of MRI Examination in Differentiating the Nature of Solitary Pulmonary Nodule Lesions. Chinese Journal of CT and MRI, 20(8): 65–67.
Liu J, Jiang J, Yin J, et al., 2022, Differential Diagnosis of Benign and Malignant Pulmonary Nodules and Masses Based on Multimodal MR Radiomics. Chinese Journal of Radiology, 56(5): 542–548.