Comparative analysis of imaging modalities for pancreatic cancer detection: A comprehensive review of diagnostic Performance

Introduction: Pancreatic cancer continues to pose a significant clinical challenge due to its typically late diagnosis and associated poor survival outcomes. While various imaging techniques are currently employed for tumor identification and staging, a thorough comparison of their diagnostic performance is essential to enhance clinical decision-making. Methods: This comprehensive review analyzes the diagnostic performance of major imaging modalities for pancreatic cancer detection, including computed tomography (CT), magnetic resonance imaging (MRI), endoscopic ultrasound (EUS), positron emission tomography (PET/CT), and conventional ultrasonography. Performance metrics, including sensitivity, specificity, and diagnostic accuracy, were extracted from recent meta-analyses and comparative studies. Results: Meta-analytic data reveals MRI achieves the highest sensitivity (93%) and diagnostic accuracy (90%) for pancreatic cancer detection, followed closely by CT with 90% sensitivity and 89% diagnostic accuracy. Endoscopic ultrasound with advanced techniques demonstrates exceptional performance with 97.5% sensitivity and 93.7% accuracy, particularly for small lesions. Positron emission tomography with computed tomography shows variable performance with sensitivity ranging from 78–97% but offers unique advantages for staging and treatment monitoring. Conventional ultrasonography demonstrates high specificity (94%) but limited sensitivity (88%). Conclusion: While multiple imaging modalities show comparable overall diagnostic performance, each offers distinct clinical advantages. MRI provides optimal primary tumor detection with superior soft tissue contrast, EUS excels in evaluating small lesions and obtaining tissue samples, CT remains the most accessible staging modality, and PET/CT offers unique metabolic information. The integration of advanced techniques including dual-energy CT and diffusion-weighted MRI may further improve detection of challenging cases. A multimodal approach tailored to individual patient characteristics and clinical scenarios optimizes diagnostic accuracy and treatment planning.