Role of CT Scan and MRI in Cancer Diagnosis: A Comparative Review of Imaging Modalities for Precision Medicine

Abstract

Cancer is one of the most lethal diseases in the current scenario all over the world. Early detection may lead to better treatment and recovery. Imaging modalities such as Magnetic resonance imaging (MRI) and Computed tomography (CT) are two modern diagnostic imaging techniques that are helpful in tumor diagnosis. However, CT imaging uses ionizing radiation and soft tissue contrast is its significant limitation. In contrast, MRI provides excellent soft tissue differentiation, multiplanar imaging capabilities, and functional imaging techniques such as diffusion-weighted imaging and spectroscopy, which are increasingly valuable in characterizing tumors and guiding therapy. The method of cancer diagnosis using CT or MRI, can provide precise information about the lesion's location, size, shape, and structural changes in the surrounding tissues. Cancer staging is precisely done on positron emission tomography (PET) but the staging of cancer is possible through CT scan and MRI up to some extent, which is helpful before starting any treatment. It provides adequate treatment depending on location, size, and type of tumor. CT scan is generally preferred in the diagnosis of abdominal and calcified lesions, while MRI is better for peripheral and central nervous system (CNS) lesions. Precision in cancer diagnosis increases the probability of opting and executing a successful treatment. Recent advances such as dual-energy computed tomography (DECT), magnetic resonance spectroscopy (MRS), Positron emission tomography-computed tomography (PET-CT), and Positron emission tomography-magnetic resonance imaging (PET-MRI) have increased the precision of cancer diagnosis. ASL (arterial spin labeling), an advanced MRI technique, helps diagnose people who are allergic to contrast. It increases image acquisition resolution, allowing for even more accurate and precise diagnosis and prognosis. Also, the qualitative interpretation of cancer imaging might be greatly enhanced by the integration of artificial intelligence (AI) in radio imaging techniques. Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. Screening programs have improved survival in many tumor groupings, but risk stratification and patient selection remain major obstacles. This review highlights their complementary roles and the potential of multimodal imaging strategies to enhance diagnostic accuracy and improve clinical outcomes.