A Systematic Review of Automated Lymph Node Detection Methods in Head and Neck Cancer: Clinical Significance, Performance, and Challenges
Keywords:
clinicians, Lymph, implementation, algorithms, magnetic resonance imaging (MRI), benchmarkAbstract
This systematic review investigates the role of automated lymph node detection methods, particularly within the context of head and neck cancer diagnosis and treatment. Lymph node metastasis is a crucial factor in the staging and management of head and neck cancer patients, and the accuracy of lymph node assessment significantly influences treatment decisions and patient outcomes. Automated methods, especially those based on deep learning, have emerged as potential game-changers in enhancing the precision and efficiency of lymph node evaluation in medical imaging. Following PRISMA guidelines, this review presents a meticulous selection and analysis of relevant studies from peer-reviewed journals. It evaluates the clinical significance, performance metrics, limitations, and challenges of automated lymph node detection methods. Clinical relevance is a central theme, emphasizing these methods' critical role in aiding clinicians in lymph node assessment, influencing treatment planning, and improving patient care. Performance evaluation indicates that deep learning techniques, especially convolutional neural networks (CNNs), exhibit impressive accuracy across various imaging modalities like computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, the review also highlights persistent challenges, including high computational demands, false positives/negatives, and the need for seamless integration into clinical workflows. Standardization efforts through benchmark datasets and evaluation metrics are essential for future advancements. Addressing computational resource constraints, refining algorithms, and ensuring practical clinical implementation is essential for fully realizing the potential of automated lymph node detection in the management of head and neck cancer. This review serves as a comprehensive resource for researchers and clinicians seeking to navigate this evolving landscape.
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