Comparative Analysis of Radiation Dose in CT vs. MRI Protocols in Oncology

Thesis Abstract

In oncologic imaging, the juxtaposition of computed tomography (CT) and magnetic resonance imaging (MRI) protocols raises critical questions about diagnostic efficacy balanced against cumulative radiation exposure and patient safety, particularly for longitudinal staging and treatment planning. This study addresses the problem of determining how CT and MRI protocols compare in terms of radiation dose, diagnostic performance, and clinical impact for oncology patients, with a focus on dose reduction opportunities without compromising lesion detectability and staging accuracy. The aim is to quantify and compare radiation doses between CT and MRI sequences commonly employed in oncology workflows, identify factors influencing dose optimization, and evaluate the trade-offs between image quality and diagnostic confidence across cancer types. Specific objectives include (1) to quantify effective dose and organ-equivalent doses across standard CT oncologic protocols and MRI sequences; (2) to assess diagnostic performance metrics such as sensitivity, specificity, and localization accuracy for CT and MRI in thoracic, abdominal, and pelvic malignancies; (3) to model the relationship between imaging parameters (e.g., CT tube current–time product, kVp, MRI field strength, and sequence parameters) and diagnostic outcomes using multivariate regression; (4) to explore institutional variability in imaging protocols and their impact on dose and diagnostic yield; and (5) to formulate evidence-based recommendations for protocol optimization that minimize radiation exposure without sacrificing diagnostic efficacy. The study adopts a cross-sectional, multi-centre design combining quantitative dose measurements, retrospective diagnostic performance analysis, and qualitative process evaluation guided by Social Cognitive Theory and the Health Belief Model to understand clinician decision-making around imaging choices. A mixed-methods approach will be employed. Quantitative data will be collected from 15 oncology centres, including 600 CT examinations and 400 corresponding MRI studies performed for initial staging and follow-up across lung, liver, pancreas, and colorectal cancer cohorts over a 24-month period. Dose metrics will be extracted from dose-tracking software, including CTDIvol, DLP, and estimated effective dose, while MRI data will document sequence types, repetition times, and field strength. Diagnostic performance will be evaluated against reference standards established by histopathology, interval imaging, and clinical follow-up. Statistical analyses will involve paired comparison tests for dose metrics, receiver operating characteristic (ROC) analysis to compare modality performance, and multivariate linear and logistic regression to identify predictors of diagnostic accuracy and dose optimization. Subgroup analyses will investigate cancer site, scanner model, and contrast-enhancement use. Qualitative data will be gathered via semi-structured interviews with radiologists and technologists (n=25) to elucidate barriers and facilitators to protocol optimization, with thematic analysis performed to extract salient themes. Expected findings include (1) higher mean effective doses associated with CT protocols relative to MRI, with substantial inter-centre variability; (2) non-inferiority of MRI in specific anatomical regions for lesion detection and staging when using optimized sequences, and superior soft-tissue contrast in liver and brain metastasis assessment; (3) statistically significant associations between imaging parameter optimization and maintained diagnostic performance while achieving dose reductions of up to 40% in selected CT protocols; (4) identified workflows and policy gaps contributing to dose heterogeneity, validated by qualitative insights. The study aims to contribute to knowledge by providing a robust, evidence-based framework for protocol selection and optimization in oncologic imaging, reconciling diagnostic accuracy with patient safety, and informing dose reference levels and reimbursement considerations. The theoretical contribution lies in integrating implementation science with imaging science to model how clinician attitudes, perceived risk, and system-level constraints influence adherence to dose-optimization strategies. Practical implications include actionable recommendations for standardised, site-specific MRI-first pathways where appropriate, CT protocol refinement guidelines (kVp, automatic exposure control, iterative reconstruction), and decision-support tools to facilitate risk-benefit assessments at the point of imaging. The main conclusion anticipates that MRI, when appropriately sequence-optimized and used in conjunction with CT selectively, can achieve comparable diagnostic outcomes with substantially lower radiation exposure for several oncologic indications. Recommendations will emphasize protocol harmonisation, continuing education for radiology teams, and the development of integrated dose-tracking dashboards to monitor adherence and foster continuous quality improvement across cancer centres.