Rapid detection of liver metastasis risk in colorectal cancer patients through blood test indicators.

Introduction: Colorectal cancer (CRC) is one of the most common malignancies, with liver metastasis being its most common form of metastasis. The diagnosis of colorectal cancer liver metastasis (CRCLM) mainly relies on imaging techniques and puncture biopsy techniques, but there is no simple and quick early diagnosisof CRCLM. Methods: This study aims to develop a method for rapidly detecting the risk of liver metastasis in CRC patients through blood test indicators based on machine learning (ML) techniques, thereby improving treatment outcomes. To achieve this, blood test indicators from 246 CRC patients and 256 CRCLM patients were collected and analyzed, including routine blood tests, liver function tests, electrolyte tests, renal function tests, glucose determination, cardiac enzyme profiles, blood lipids, and tumor markers. Six commonly used ML models were used for CRC and CRCLM classification and optimized by using a feature selection strategy. Results: The results showed that AdaBoost algorithm can achieve the highest accuracy of 89.3% among the six models, which improved to 91.1% after feature selection strategy, resulting with 20 key markers. Conclusions: The results demonstrate that the combination of machine learning techniques with blood markers is feasible and effective for the rapid diagnosis of CRCLM, significantly im-proving diagnostic ac-curacy and patient prognosis.