Colorectal cancer is the third most common malignancy worldwide, and prognosis largely depends on how effectively metastatic disease is managed. The liver is the most frequent and prognostically important site of metastasis, and patients responding well to chemotherapy may become candidates for curative hepatic resection. However, the presence of extrahepatic metastasis (EHM) critically influences treatment eligibility and survival. Although clinical scores such as the Fong and Beppu systems include EHM as a determinant, its detection by imaging remains limited, especially for small or occult lesions. Accurate identification of EHM is also essential when considering liver transplantation for unresectable colorectal liver metastases (CRLM), where EHM remains an exclusion criterion. The EXMETIS Study aims to develop a non-invasive diagnostic model using serum exosomal microRNAs (miRNAs) to detect both hepatic and extrahepatic metastases in patients with CRLM. By integrating circulating miRNA profiling with machine learning-based analysis, this study seeks to supplement imaging diagnostics, improve treatment stratification, and enhance clinical decision-making for metastatic colorectal cancer.