Abstract
Metastatic tumors of unknown primary (MUOs) present a diagnostic challenge due to the absence of an identifiable primary tumor site. The diagnostic approach for MUOs involves a comprehensive evaluation that includes clinical assessment, imaging studies, laboratory tests, and tissue sampling. Various imaging modalities, such as CT, MRI, PET scans, and ultrasound, are used to assess the extent of metastasis and identify potential primary tumor sites. Treatment options for MUOs include systemic therapies like chemotherapy, targeted therapy, immunotherapy, and hormone therapy, along with supportive care measures. Prognosis varies widely and is influenced by factors such as the extent of metastasis, tumor characteristics, treatment response, and patient factors [1]. Artificial intelligence (AI) has the potential to aid in diagnosis and management through image analysis, predictive modeling, pathology analysis, and risk assessment. The integration of AI requires careful validation and collaboration between healthcare professionals and AI experts. A multidisciplinary approach is crucial for optimal management of MUOs, and ongoing research aims to enhance diagnostic methods, treatment strategies, and prognostic models.
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