Comparative Efficacy Of Imaging Modalities In Staging And Monitoring Head And Neck Cancers: Ct, Mri, Pet, And Ai-Augmented Approaches

Abstract

HN cancers (HNCs) are an assorted group of malignancies, including staging with much scrutiny and monitoring that accommodate treatment planning in a better tailored method that determines patient outcomes. Bulky and traditional types of imaging modalities, including computer tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), offer distinct and distinct imaging benefits, but shortcomings still exist in the delineation of soft tissues, the detection of distant metastases, and economic reliability. Recent developments in the field of artificial intelligence (AI) and machine learning (ML) have added AI-augmented imaging as an auxiliary tool that possesses the potential to increase tumor detection, segmentation, and prognostic modeling. In this article, the above features of CT, MRI, PET, and AI-enhanced imaging are critically evaluated and compared to their diagnostic performance, clinical utility, and economics regarding HNCs staging and monitoring. Peer-reviewed clinical studies, meta-analyses, and practice guidelines are studied synthesized to determine strengths and weaknesses as well as the most effective integration methods for each modality. The results indicate that a multimodal platform, which would be aided by AI-enhanced analytics, could be able to provide the best diagnostic accuracy and long-term cost-effectiveness in modern oncologic practice.

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