EFFECTIVENESS OF AI ALGORITHMS IN SEGMENTING ONCOLOGICAL LESIONS

Abstract

Accurate segmentation of oncological lesions is critical for diagnosis, treatment planning, and monitoring therapeutic response in cancer patients. Artificial intelligence (AI) algorithms, particularly deep learning models, have shown promising results in automating the segmentation of tumors across various imaging modalities, including CT, MRI, and PET scans. This paper reviews current AI-based techniques for oncological lesion segmentation, focusing on convolutional neural networks (CNNs), U-Net architectures, and hybrid models. The study evaluates their performance in terms of accuracy, sensitivity, and clinical applicability. Challenges such as limited annotated datasets, variability in imaging protocols, and integration into clinical practice are also discussed. By highlighting recent advancements, this paper emphasizes the role of AI in enhancing precision oncology and supporting radiologists in effective decision-making.