A STRUCTURED ALGORITHM FOR THE RATIONAL SELECTION OF IMAGING MODALITIES IN ONCOLOGY: A COMPARATIVE ANALYSIS OF MSCT, MRI, AND PET/CT
Abstract
This study presents the development and evaluation of a structured algorithm for the rational selection of imaging modalities in oncological patients. The algorithm integrates diagnostic performance indicators, including sensitivity, specificity, and the clinical role of imaging techniques in tumor detection, staging, and treatment monitoring.
A total of 284 patients with various tumor localizations were included in the study. The results demonstrated that the application of an algorithm-based approach significantly improves diagnostic accuracy, reduces the frequency of redundant imaging studies, and optimizes the diagnostic pathway.
The findings highlight the importance of systematic decision-making in radiological diagnostics and support the implementation of standardized algorithms in clinical oncology practice.
References
1. World Health Organization. (2020). WHO report on cancer: Setting priorities, investing wisely and providing care for all. Geneva: WHO.
2. National Comprehensive Cancer Network. (2024). Clinical Practice Guidelines in Oncology. Available at: NCCN.org.
3. European Society for Medical Oncology. (2022). ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up of cancers.
4. American College of Radiology. (2023). ACR Appropriateness Criteria®. Reston, VA.
5. Society of Nuclear Medicine and Molecular Imaging. (2022). Procedure Standard for FDG PET/CT Imaging in Oncology.
6. Fletcher J.W., Djulbegovic B., Soares H.P. et al. (2008). Recommendations on the use of 18F-FDG PET in oncology. Journal of Nuclear Medicine, 49(3), 480–508.
7. Jadvar H. (2016). FDG PET in oncology: Basics and clinical applications. Journal of Nuclear Medicine, 57(Suppl 1), 144S–149S.
8. Padhani A.R., Liu G., Koh D.M. et al. (2009). Diffusion-weighted magnetic resonance imaging as a cancer biomarker. Radiology, 252(2), 348–362.
9. Lambin P., Rios-Velazquez E., Leijenaar R. et al. (2012). Radiomics: Extracting more information from medical images. European Journal of Cancer, 48(4), 441–446.
10. Kwee T.C., Kwee R.M. (2008). Combined FDG-PET/CT for the detection of unknown primary tumors. European Radiology, 18(4), 731–744.
11. Hillner B.E., Siegel B.A., Liu D. et al. (2008). Impact of PET/CT on management of cancer patients. Journal of Clinical Oncology, 26(13), 2155–2161.
12. Eisenhauer E.A., Therasse P., Bogaerts J. et al. (2009). New response evaluation criteria in solid tumors (RECIST 1.1). European Journal of Cancer, 45(2), 228–247.
13. O’Sullivan G.J., Carty F.L., Cronin C.G. (2015). Imaging of bone metastasis: An update. World Journal of Radiology, 7(8), 202–211.
14. Brady A.P. (2017). Error and discrepancy in radiology. British Journal of Radiology, 90(1072).
15. Boland G.W.L., Lee M.J., Gazelle G.S. (2011). Clinical decision support in radiology. Radiology, 259(2), 317–323.
