RESEARCH ON THE APPLICATION OF THE MAXIMUM INTENSITY PROJECTION (MIP) ALGORITHM IN CHEST COMPUTED TOMOGRAPHY FOR THE DETECTION AND FOLLOW-UP OF SMALL SOLID PULMONARY NODULES
Main Article Content
Abstract
Background: Detection of small solid pulmonary nodules (<5 mm) on chest CT remains challenging due to their subtle appearance and risk of being overlooked on thin-section images. Maximum Intensity Projection (MIP) reconstruction enhances high-density structures and may improve detection.
Objective: To evaluate the role of MIP at different slab thicknesses in detecting and monitoring small solid pulmonary nodules (<5 mm).
Methods: A prospective study was conducted on 55 patients with solid pulmonary nodules <5 mm at the National Lung Hospital from June 2023 to October 2025. CT datasets were reconstructed into four image sets: MIP0 (axial), MIP5, MIP10, and MIP15. Two radiologists independently reviewed the images. The number, size, location, and morphological features of nodules were analyzed and compared using statistical tests.
Results: A total of 129 nodules were analyzed. MIP significantly increased nodule detection compared with MIP0, with MIP5 and MIP10 showing statistically significant differences (p<0.001). No significant difference was found between MIP5 and MIP10 (p>0.05), although MIP10 showed a trend toward higher detection. MIP15 reduced detection performance. Interobserver agreement was high (p>0.05). Most nodules were located in the lower lobes and had smooth margins. During follow-up, 16 nodules (12.4%) increased in size, some of which were confirmed as malignant or premalignant lesions.
Conclusion: MIP significantly improves the detection of small pulmonary nodules, with optimal performance at 5–10 mm slab thickness, particularly MIP10. Follow-up findings indicate that a subset of small nodules may progress, highlighting the importance of early detection and periodic surveillance.
Keywords
pulmonary nodule, maximum-intensity-projection, lung cancer.
Article Details
References
2. Güleryüz Kızıl P, et al. Diagnostic importance of maximum intensity projection technique in the identification of small pulmonary nodules with computed tomography. Tuberk Toraks. 2020;68(1):35-42.
3. Gould MK, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 suppl):e93S-e120S.
4. Valencia R, et al. Value of axial and coronal maximum intensity projection (MIP) images in the detection of pulmonary nodules by multislice spiral CT: comparison with axial 1-mm and 5-mm slices. Eur Radiol. 2006;16(2):325-332.
5. Zheng S, et al. Deep learning-based pulmonary nodule detection: effect of slab thickness in maximum intensity projections at the nodule candidate detection stage. Comput Methods Programs Biomed.2020;196:105620.
6. Ocaña-Tienda B, Eroles-Simó A, Pérez-Beteta J, Arana E, Pérez-García VM. Growth dynamics of lung nodules: implications for classification in lung cancer screening. Cancer Imaging. 2024;24(1):113.
7. MacMahon H, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284(1):228-243.
8. Riely GJ, Wood DE, Ettinger DS, et al. Non–small cell lung cancer, version 4.2024, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2024;22(4):249-274.