Application of Experience-Based and MRI Radiomics in Differential Diagnosis of Benign and Malignant Pulmonary Nodules and Masses

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.

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Publication Details

Journal
Journal of Clinical and Nursing Research
Published
2025-08-04
DOI
https://doi.org/10.26689/jcnr.v9i7.11429
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Application of Experience-Based and MRI Radiomics in Differential Diagnosis of Benign and Malignant Pulmonary Nodules and Masses

Yechun Zhang, Kuntian Tang
Journal of Clinical and Nursing Research
Radiomics and Machine Learning in Medical Imaging
article

Application of Experience-Based and MRI Radiomics in Differential Diagnosis of Benign and Malignant Pulmonary Nodules and Masses

Yechun Zhang, Kuntian Tang
article en

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.

Journal of Clinical and Nursing ResearchVol. 9(7)
Jiangsu University (CN), First People's Hospital of Kunshan (CN)
Openalex Percentile: Top 72%
Radiomics and Machine Learning in Medical Imaging
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