Abstract

Cancer continues to be a major global health challenge, driving continuous efforts to enhance oncologic imaging techniques for improved diagnosis, treatment planning, and monitoring. In this comprehensive review, we explore the significant developments in oncologic imaging over the past decade, highlighting their potential impact on cancer care. Multipara metric imaging has emerged as a powerful approach, combining different modalities to provide a more comprehensive evaluation of tumors. Techniques such as PET/CT and PET/MRI have enabled the fusion of molecular information with anatomical images, leading to enhanced sensitivity and specificity in tumor detection and staging. Radionics and radio genomics, utilizing quantitative imaging features and genetics, have paved the way for personalized medicine, aiding in treatment prediction and individualized therapy selection [1].

Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized oncologic imaging by automating the detection and characterization of tumors. AI algorithms have shown promising results in differentiating malignant from benign lesions, reducing diagnostic uncertainties, and optimizing treatment planning. Moreover, molecular imaging and targeted radiotracers offer non-invasive assessment of tumor biology, aiding in early cancer detection, therapy selection, and response monitoring [2].

Keywords: Cancer; Artificial intelligence; Machine learning; Radionics; Radio genomics