Integrating CT and MRI in Cancer Staging and Treatment Monitoring: Challenges and Innovations

Authors

  • Urooj Fatima Department of Radiodiagnosis, Paramedical College, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India Author
  • Saqib Zameer Department of Microbiology, Paramedical College, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India Author
  • Zeeshan Akram Department of Radiodiagnosis, Paramedical College, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India Author
  • Mohd Faraz Department of Radiodiagnosis, Paramedical College, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India Author
  • Areeb Daniyal Department of Radiodiagnosis, Paramedical College, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India Author
  • Zarrin Anwar Department of Radiodiagnosis, Paramedical College, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India Author
  • Kashif Abbas Department of Zoology, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, Uttar Pradesh, India Author
  • Mudassir Alam Indian Biological Sciences and Research Institute (IBRI), Noida, India Author

DOI:

https://doi.org/10.67224/ioasdjmps.2025.v02i01.010

Keywords:

Computed tomography, Magnetic resonance imaging, Cancer staging, Diffusion-weighted imaging, Hybrid imaging

Abstract

Imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) are widely used to identify and monitor cancer patients. Although CT and MRI are based on structural or anatomical abnormalities to identify disease, some advanced MRI techniques can identify cancer by analyzing chemical changes occurring within the tumor. Radiological early diagnosis makes timely action possible, essential for improving treatment results. Innovative methods including Multimodal imaging methods, artificial intelligence (AI), and hybrid approaches like PET-CT and PET-MRI are the results of advancements in cancer imaging. Some challenges are faced during MRI scanning like the duration of the scan, claustrophobia, and risk of injury because of any metallic implant. All these methods and advancements improve the quality of the treatment plan and elevate the accuracy of the diagnosis.

References

• Alam, F., & Rahman, S. U. (2019). Challenges and Solutions in Multimodal Medical Image Subregion Detection and Registration. Journal of Medical Imaging and Radiation Sciences, 50(1), 24–30. https://doi.org/10.1016/j.jmir.2018.06.001

• Alanazi, M. M. F. (2024). Advancements in hybrid imaging techniques: Enhancing diagnostic accuracy with PET/MRI and PET/CT. International Journal of Health Sciences, 8(S1), 1800–1811. https://doi.org/10.53730/ijhs.v8nS1.15396

• Asamura, H., Nishimura, K. K., Giroux, D. J., Chansky, K., Hoering, A., Rusch, V., Rami-Porta, R., & Members of the IASLC Staging and Prognostic Factors Committee and of the Advisory Boards, and Participating Institutions. (2023). IASLC Lung Cancer Staging Project: The New Database to Inform Revisions in the Ninth Edition of the TNM Classification of Lung Cancer. Journal of Thoracic Oncology: Official Publication of the International Association for the Study of Lung Cancer, 18(5), 564–575. https://doi.org/10.1016/j.jtho.2023.01.088

• Ashby, K., Adams, B. N., & Shetty, M. (2025). Appropriate Magnetic Resonance Imaging Ordering. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK565857/

• Bai, J.-W., Qiu, S.-Q., & Zhang, G.-J. (2023). Molecular and functional imaging in cancer-targeted therapy: Current applications and future directions. Signal Transduction and Targeted Therapy, 8(1), 89. https://doi.org/10.1038/s41392-023-01366-y

• Bruno, F., Arrigoni, F., Mariani, S., Splendiani, A., Di Cesare, E., Masciocchi, C., & Barile, A. (2019). Advanced magnetic resonance imaging (MRI) of soft tissue tumors: Techniques and applications. La Radiologia Medica, 124(4), 243–252. https://doi.org/10.1007/s11547-019-01035-7

• Cobos Alonso, J., Fontenla-Martínez, C., Concepción Aramendía, L., Bernabé García, J. M., & Arenas-Jiménez, J. J. (2024). Introduction to iodinated contrasts: Properties, intravenous administration and distribution throughout the body. Radiología (English Edition), 66, S3–S14. https://doi.org/10.1016/j.rxeng.2024.03.010

• Cui, B., Guo, K., & Lu, J. (2023). Introduction to Positron Emission Tomography/Magnetic Resonance (PET/MR) Imaging. In J. Lu & G. Zhao (Eds.), PET/MR: Functional and Molecular Imaging of Neurological Diseases and Neurosciences (pp. 1–12). Springer Nature. https://doi.org/10.1007/978-981-19-9902-4_1

• Dudhe, S. S., Mishra, G., Parihar, P., Nimodia, D., & Kumari, A. (n.d.). Radiation Dose Optimization in Radiology: A Comprehensive Review of Safeguarding Patients and Preserving Image Fidelity. Cureus, 16(5), e60846. https://doi.org/10.7759/cureus.60846

• Eidex, Z., Ding, Y., Wang, J., Abouei, E., Qiu, R. L. J., Liu, T., Wang, T., & Yang, X. (2024). Deep learning in MRI-guided radiation therapy: A systematic review. Journal of Applied Clinical Medical Physics, 25(2), e14155. https://doi.org/10.1002/acm2.14155

• Ermongkonchai, T., Khor, R., Wada, M., Lau, E., Xing, D. T., & Ng, S. P. (2023). A review of diffusion-weighted magnetic resonance imaging in head and neck cancer patients for treatment evaluation and prediction of radiation-induced xerostomia. Radiation Oncology, 18(1), 20. https://doi.org/10.1186/s13014-022-02178-0

• Farjah, F., Barta, J. A., Wood, D. E., Rivera, M. P., Osarogiagbon, R. U., Smith, R. A., Mullett, T. W., Rosenthal, L. S., Henderson, L. M., Detterbeck, F. C., & Silvestri, G. A. (2024). The American Cancer Society National Lung Cancer Roundtable strategic plan: Promoting guideline‐concordant lung cancer staging. Cancer, 130(24), 4167–4176. https://doi.org/10.1002/cncr.34627

• Filippone, F., Boudagga, Z., Frattini, F., Fortuna, G. F., Razzini, D., Tambasco, A., Menardi, V., Balbiano di Colcavagno, A., Carriero, S., Gambaro, A. C. L., & Carriero, A. (2024). Contrast Enhancement in Breast Cancer: Magnetic Resonance vs. Mammography: A 10-Year Systematic Review. Diagnostics, 14(21), 2400. https://doi.org/10.3390/diagnostics14212400

• Gaillard, F. (n.d.). MR spectroscopy | Radiology Reference Article | Radiopaedia.org. Radiopaedia. https://doi.org/10.53347/rID-1630

• Gignac, P. M., Aceves, V., Baker, S., Barnes, J. J., Bell, J., Boyer, D., Cunningham, D., Carlo, F. D., Chase, M. H., Cohen, K. E., Colbert, M., Cree, T. D., Daza, J., Dickinson, E., DeLeon, V., Dougan, L., Duffy, F., Dunham, C., Early, C. M., … Zobek, C. M. (2024). The role of networks to overcome large-scale challenges in tomography: The non-clinical tomography users research network. Tomography of Materials and Structures, 5, 100031. https://doi.org/10.1016/j.tmater.2024.100031

• Gillies, R. J., & Schabath, M. B. (2020). Radiomics Improves Cancer Screening and Early Detection. Cancer Epidemiology, Biomarkers & Prevention, 29(12), 2556–2567. https://doi.org/10.1158/1055-9965.EPI-20-0075

• Grégoire, V., Guckenberger, M., Haustermans, K., Lagendijk, J. J. W., Ménard, C., Pötter, R., Slotman, B. J., Tanderup, K., Thorwarth, D., van Herk, M., & Zips, D. (2020). Image guidance in radiation therapy for better cure of cancer. Molecular Oncology, 14(7), 1470–1491. https://doi.org/10.1002/1878-0261.12751

• Hermena, S., & Young, M. (2025). CT-scan Image Production Procedures. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK574548/

• Hossain, M. B., Shinde, R. K., Oh, S., Kwon, K.-C., & Kim, N. (2024). A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction. Sensors, 24(3), Article 3. https://doi.org/10.3390/s24030753

• Hsieh, J. (2024). Synthetization of high-dose images using low-dose CT scans. Medical Physics, 51(1), 113–125. https://doi.org/10.1002/mp.16833

• Hu, R., Li, T., Yang, Y., Tian, Y., & Zhang, L. (2021). NMR-Based Metabolomics in Cancer Research. Advances in Experimental Medicine and Biology, 1280, 201–218. https://doi.org/10.1007/978-3-030-51652-9_14

• Kapoor, M., & Kasi, A. (2025). PET Scanning. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK559089/

• Khalifa, M., & Albadawy, M. (2024). AI in diagnostic imaging: Revolutionising accuracy and efficiency. Computer Methods and Programs in Biomedicine Update, 5, 100146. https://doi.org/10.1016/j.cmpbup.2024.100146

• Koetzier, L. R., Mastrodicasa, D., Szczykutowicz, T. P., van der Werf, N. R., Wang, A. S., Sandfort, V., van der Molen, A. J., Fleischmann, D., & Willemink, M. J. (2023). Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects. Radiology, 306(3), e221257. https://doi.org/10.1148/radiol.221257

• Kuber, R., KirdatPatil, P. P., Dhande, A., Mane, R., & Kumar, P. (2024). Magnetic Resonance Imaging (MRI) Evaluation and Classification of Vascular Malformations. Cureus, 16(8), e67475. https://doi.org/10.7759/cureus.67475

• Lauri, H. (2017). High-resolution CT of the lungs: Indications and diagnosis. Duodecim; Laaketieteellinen Aikakauskirja, 133(6), 549–556.

• Leskinen, S., Singha, S., Mehta, N. H., Quelle, M., Shah, H. A., & D’Amico, R. S. (2024). Applications of Functional Magnetic Resonance Imaging to the Study of Functional Connectivity and Activation in Neurological Disease: A Scoping Review of the Literature. World Neurosurgery, 189, 185–192. https://doi.org/10.1016/j.wneu.2024.06.003

• Li, X., Huang, W., & Holmes, J. H. (2024). Dynamic Contrast-Enhanced (DCE) MRI. Magnetic Resonance Imaging Clinics of North America, 32(1), 47–61. https://doi.org/10.1016/j.mric.2023.09.001

• Maniaci, A., Lavalle, S., Gagliano, C., Lentini, M., Masiello, E., Parisi, F., Iannella, G., Cilia, N. D., Salerno, V., Cusumano, G., & La Via, L. (2024). The Integration of Radiomics and Artificial Intelligence in Modern Medicine. Life, 14(10), Article 10. https://doi.org/10.3390/life14101248

• Moleyar-Narayana, P., Leslie, S. W., & Ranganathan, S. (2025). Cancer Screening. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK563138/

• Musafargani, S., Ghosh, K. K., Mishra, S., Mahalakshmi, P., Padmanabhan, P., & Gulyás, B. (2018). PET/MRI: A frontier in era of complementary hybrid imaging. European Journal of Hybrid Imaging, 2(1), 12. https://doi.org/10.1186/s41824-018-0030-6

• Olsson, L. E., Johansson, M., Zackrisson, B., & Blomqvist, L. K. (2019). Basic concepts and applications of functional magnetic resonance imaging for radiotherapy of prostate cancer. Physics and Imaging in Radiation Oncology, 9, 50–57. https://doi.org/10.1016/j.phro.2019.02.001

• Owens, T. C., Anton, N., & Attia, M. F. (2023). CT and X-ray contrast agents: Current clinical challenges and the future of contrast. Acta Biomaterialia, 171, 19–36. https://doi.org/10.1016/j.actbio.2023.09.027

• Patel, P. R., & De Jesus, O. (2025). CT Scan. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK567796/

• (PDF) MRI LIMITATIONS: THE MAIN ASPECTS AND RESOLVING TECHNIQUES. (2024). ResearchGate. https://www.researchgate.net/publication/346498912_MRI_LIMITATIONS_THE_MAIN_ASPECTS_AND_RESOLVING_TECHNIQUES

• Peeters, R., & Sunaert, S. (2022). Clinical BOLD fMRI and DTI: Artifacts, Tips, and Tricks. In C. Stippich (Ed.), Clinical Functional MRI: Presurgical Functional Neuroimaging (pp. 407–439). Springer International Publishing. https://doi.org/10.1007/978-3-030-83343-5_12

• Pesapane, F., Suter, M. B., Rotili, A., Penco, S., Nigro, O., Cremonesi, M., Bellomi, M., Jereczek-Fossa, B. A., Pinotti, G., & Cassano, E. (2020). Will traditional biopsy be substituted by radiomics and liquid biopsy for breast cancer diagnosis and characterisation? Medical Oncology (Northwood, London, England), 37(4), 29. https://doi.org/10.1007/s12032-020-01353-1

• Petralia, G., Summers, P. E., Agostini, A., Ambrosini, R., Cianci, R., Cristel, G., Calistri, L., & Colagrande, S. (2020). Dynamic contrast-enhanced MRI in oncology: How we do it. La Radiologia Medica, 125(12), 1288–1300. https://doi.org/10.1007/s11547-020-01220-z

• Pinto-Coelho, L. (2023). How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering, 10(12), 1435. https://doi.org/10.3390/bioengineering10121435

• Reed, M. B., Ponce de León, M., Vraka, C., Rausch, I., Godbersen, G. M., Popper, V., Geist, B. K., Komorowski, A., Nics, L., Schmidt, C., Klug, S., Langsteger, W., Karanikas, G., Traub-Weidinger, T., Hahn, A., Lanzenberger, R., & Hacker, M. (2023). Whole-body metabolic connectivity framework with functional PET. NeuroImage, 271, 120030. https://doi.org/10.1016/j.neuroimage.2023.120030

• Rong, J., & Liu, Y. (2024). Advances in medical imaging techniques. BMC Methods, 1(1), 10. https://doi.org/10.1186/s44330-024-00010-7

• Rosen, R. D., & Sapra, A. (2025). TNM Classification. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK553187/

• Sanghavi, P. S., & Jankharia, B. G. (2019). Applications of dual energy CT in clinical practice: A pictorial essay. The Indian Journal of Radiology & Imaging, 29(3), 289–298. https://doi.org/10.4103/ijri.IJRI_241_19

• Sayed, M., Knapp, K. M., Fulford, J., Heales, C., & Alqahtani, S. J. (2023). The principles and effectiveness of X-ray scatter correction software for diagnostic X-ray imaging: A scoping review. European Journal of Radiology, 158, 110600. https://doi.org/10.1016/j.ejrad.2022.110600

• Schiavina, R., Bianchi, L., Borghesi, M., Dababneh, H., Chessa, F., Pultrone, C. V., Angiolini, A., Gaudiano, C., Porreca, A., Fiorentino, M., De Groote, R., D’Hondt, F., De Naeyer, G., Mottrie, A., & Brunocilla, E. (2018). MRI Displays the Prostatic Cancer Anatomy and Improves the Bundles Management Before Robot-Assisted Radical Prostatectomy. Journal of Endourology, 32(4), 315–321. https://doi.org/10.1089/end.2017.0701

• Shah, D., Gehani, A., Mahajan, A., & Chakrabarty, N. (2023). Advanced Techniques in Head and Neck Cancer Imaging: Guide to Precision Cancer Management. Critical Reviews in Oncogenesis, 28(2), 45–62. https://doi.org/10.1615/CritRevOncog.2023047799

• Shetty, A. (n.d.). Positron emission tomography | Radiology Reference Article | Radiopaedia.org. Radiopaedia. https://doi.org/10.53347/rID-29716

• Sodickson, A. D., Keraliya, A., Czakowski, B., Primak, A., Wortman, J., & Uyeda, J. W. (2021). Dual energy CT in clinical routine: How it works and how it adds value. Emergency Radiology, 28(1), 103–117. https://doi.org/10.1007/s10140-020-01785-2

• The Role of Imaging in Radiation Therapy Planning: Past, Present, and Future—Pereira—2014—BioMed Research International—Wiley Online Library. (n.d.). Retrieved January 26, 2025, from https://onlinelibrary.wiley.com/doi/10.1155/2014/231090?msockid=2009e20144c36ea92430f6ad450c6f51

• Tolonen, A., Pakarinen, T., Sassi, A., Kyttä, J., Cancino, W., Rinta-Kiikka, I., Pertuz, S., & Arponen, O. (2021). Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review. European Journal of Radiology, 145, 109943. https://doi.org/10.1016/j.ejrad.2021.109943

• van Timmeren, J. E., Cester, D., Tanadini-Lang, S., Alkadhi, H., & Baessler, B. (2020). Radiomics in medical imaging—“How-to” guide and critical reflection. Insights into Imaging, 11(1), 91. https://doi.org/10.1186/s13244-020-00887-2

• Volz, L., Korte, J., Martire, M. C., Zhang, Y., Hardcastle, N., Durante, M., Kron, T., & Graeff, C. (2024). Opportunities and challenges of upright patient positioning in radiotherapy. Physics in Medicine and Biology, 69(18). https://doi.org/10.1088/1361-6560/ad70ee

• Woźniak, M., Płoska, A., Siekierzycka, A., Dobrucki, L. W., Kalinowski, L., & Dobrucki, I. T. (2022). Molecular Imaging and Nanotechnology-Emerging Tools in Diagnostics and Therapy. International Journal of Molecular Sciences, 23(5), 2658. https://doi.org/10.3390/ijms23052658.

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Published

2025-03-15

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Vol. 2 No. 1 (2025): IOASDJMPS

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Review Articles

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Copyright (c) 2025 Urooj Fatima, Saqib Zameer, Zeeshan Akram, Mohd Faraz, Areeb Daniyal, Zarrin Anwar, Kashif Abbas, Mudassir Alam (Author)

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Urooj Fatima, Saqib Zameer, Zeeshan Akram, Mohd Faraz, Areeb Daniyal, Zarrin Anwar, Kashif Abbas, & Mudassir Alam. (2025). Integrating CT and MRI in Cancer Staging and Treatment Monitoring: Challenges and Innovations. IOASD Journal of Medical and Pharmaceutical Sciences, 2(1), 44-50. https://doi.org/10.67224/ioasdjmps.2025.v02i01.010