Advancements in deep learning for image-guided tumor ablation therapies: a comprehensive review.

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Abstract

Image-guided tumor ablation (IGTA) has revolutionized modern oncological treatments by providing minimally invasive options that ensure precise tumor eradication with minimal patient discomfort. Traditional techniques such as ultrasound (US), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) have been instrumental in the planning, execution, and evaluation of ablation therapies. However, these methods often face limitations, including poor contrast, susceptibility to artifacts, and variability in operator expertise, which can undermine the accuracy of tumor targeting and therapeutic outcomes. Incorporating deep learning (DL) into IGTA represents a significant advancement that addresses these challenges. This review explores the role and potential of DL in different phases of tumor ablation therapy: preoperative, intraoperative, and postoperative. In the preoperative stage, DL excels in advanced image segmentation, enhancement, and synthesis, facilitating precise surgical planning and optimized treatment strategies. During the intraoperative phase, DL supports image registration and fusion, and real-time surgical planning, enhancing navigation accuracy and ensuring precise ablation while safeguarding surrounding healthy tissues. In the postoperative phase, DL is pivotal in automating the monitoring of treatment responses and in the early detection of recurrences through detailed analyses of follow-up imaging. This review highlights the essential role of deep learning in modernizing IGTA, showcasing its significant implications for procedural safety, efficacy, and patient outcomes in oncology. As deep learning technologies continue to evolve, they are poised to redefine the standards of care in tumor ablation therapies, making treatments more accurate, personalized, and patient-friendly.

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