Artificial Intelligence for Automated Tumor Segmentation and Treatment Response Assessment

Abstract

Cancer remains a leading cause of mortality worldwide, underscoring the urgent need for continuous innovation in diagnostic and therapeutic strategies. Medical imaging is a cornerstone of modern oncology, supporting tumor detection, delineation, staging, treatment planning, and longitudinal assessment of therapeutic response. Accurate tumor segmentation and treatment response evaluation are critical for effective clinical decision-making; however, conventional manual methods are often labor-intensive, time-consuming, and prone to considerable interobserver variability. The rapid advancement of artificial intelligence (AI), particularly machine learning and deep learning, has revolutionized medical image analysis by enabling automated, reproducible, and highly accurate interpretation of complex imaging datasets. Recent progress in convolutional neural networks, transformer-based architectures, and foundation models has substantially improved the accuracy and robustness of tumor segmentation across diverse imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and hybrid imaging techniques. Concurrently, AI-powered methodologies have enhanced treatment response assessment through radiomics, longitudinal image analysis, and predictive modeling, facilitating earlier detection of therapeutic effectiveness, disease progression, and treatment resistance. Furthermore, emerging multimodal AI frameworks that integrate imaging, digital pathology, genomic information, and clinical data are advancing precision oncology by providing comprehensive, patient-specific insights for personalized cancer management. Despite these significant achievements, several challenges continue to limit the widespread clinical adoption of AI technologies. Data heterogeneity, limited availability of high-quality annotated datasets, model interpretability, regulatory and ethical considerations, and integration into existing clinical workflows remain important obstacles. Nevertheless, emerging paradigms—including foundation models, self-supervised learning, federated learning, and explainable artificial intelligence—offer promising solutions to improve model robustness, generalizability, transparency, and scalability across diverse healthcare environments. This review provides a comprehensive overview of recent advances in AI-driven automated tumor segmentation and treatment response assessment. It examines current methodological developments, summarizes major clinical applications, critically discusses existing limitations, and highlights future research directions for successfully integrating artificial intelligence technologies into routine oncology practice, ultimately supporting more precise, efficient, and personalized cancer care.