Quality management in health care · 2025
Predicting Liver Cancer Risk Using Comprehensive Medical History.
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Аннотация
BACKGROUND AND OBJECTIVES: Liver cancer mortality is rising faster than any other cancer, significantly impacting life expectancy due to its relatively young median age at diagnosis and high mortality rate. There are currently no consistently recommended screening tests for liver cancer in individuals with a high-risk profile or abnormalities in body systems other than liver disease with cirrhosis. This study aims to screen various body system diseases that might be associated with liver cancer risk.
METHODS: The study utilized the All of Us database, including 410 361 US-based adults aged 18 and above, of whom 2171 had liver cancer. Least Absolute Shrinkage and Selection Operator regression and logistic regression were used to identify significant predictors and calculate odds ratios (ORs). All statistical analyses were conducted using R software.
RESULTS: Out of the total participants, 0.5% had liver cancer diagnoses. Male gender and white race were associated with an increased risk of liver cancer (OR = 1.2). Certain diseases were strongly linked to a higher risk of liver cancer, such as liver cirrhosis, chronic steatorrhea, and neoplasms of unknown behavior in the genitourinary organs, each with an OR greater than 8. Digestive disorders, including pancreatic disorders and chronic hepatitis B and C, were also associated with an increased risk of liver cancer (OR > 4).
CONCLUSIONS: The predictive model has the potential to enhance liver cancer outcomes by effectively targeting at-risk populations and by advocating for early screening among those with high-risk bodily diseases or specific diseases, which could impact survival rates.
METHODS: The study utilized the All of Us database, including 410 361 US-based adults aged 18 and above, of whom 2171 had liver cancer. Least Absolute Shrinkage and Selection Operator regression and logistic regression were used to identify significant predictors and calculate odds ratios (ORs). All statistical analyses were conducted using R software.
RESULTS: Out of the total participants, 0.5% had liver cancer diagnoses. Male gender and white race were associated with an increased risk of liver cancer (OR = 1.2). Certain diseases were strongly linked to a higher risk of liver cancer, such as liver cirrhosis, chronic steatorrhea, and neoplasms of unknown behavior in the genitourinary organs, each with an OR greater than 8. Digestive disorders, including pancreatic disorders and chronic hepatitis B and C, were also associated with an increased risk of liver cancer (OR > 4).
CONCLUSIONS: The predictive model has the potential to enhance liver cancer outcomes by effectively targeting at-risk populations and by advocating for early screening among those with high-risk bodily diseases or specific diseases, which could impact survival rates.