首页|Zhejiang Normal University Reports Findings in Liver Metastasis (Rapid detection of liver metastasis risk in colorectal cancer patients through blood test indic ators)
Zhejiang Normal University Reports Findings in Liver Metastasis (Rapid detection of liver metastasis risk in colorectal cancer patients through blood test indic ators)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Liver Metas tasis is the subject of a report. According to news originating from Jinhua, Peo ple's Republic of China, by NewsRx correspondents, research stated, "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 me tastasis (CRCLM) mainly relies on imaging techniques and puncture biopsy techniq ues, but there is no simple and quick early diagnosisof CRCLM." Our news journalists obtained a quote from the research from Zhejiang Normal Uni versity, "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 collect ed and analyzed, including routine blood tests, liver function tests, electrolyt e tests, renal function tests, glucose determination, cardiac enzyme profiles, b lood lipids, and tumor markers. Six commonly used ML models were used for CRC an d CRCLM classification and optimized by using a feature selection strategy. 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 select ion strategy, resulting with 20 key markers."
JinhuaPeople's Republic of ChinaAsiaBiomarkersCancerColon CancerColorectal ResearchCyborgsDiagnostics an d ScreeningEmerging TechnologiesGastroenterologyHealth and MedicineHepat ologyLiver MetastasisMachine LearningOncologyRisk and Prevention
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