Construction of a Nomogram to Predict Overall Survival in Patients with Early-Onset Hepatocellular Carcinoma: A Retrospective Cohort Study.
Tianrui KuangWangbin MaJiacheng ZhangJia YuWenhong DengKeshuai DongWeixing WangPublished in: Cancers (2023)
Hepatocellular carcinoma (HCC) is a widespread and impactful cancer which has pertinent implications worldwide. Although most cases of HCC are typically diagnosed in individuals aged ≥60 years, there has been a notable rise in the occurrence of HCC among younger patients. However, there is a scarcity of precise prognostic models available for predicting outcomes in these younger patients. A retrospective analysis was conducted to investigate early-onset hepatocellular carcinoma (EO-LIHC) using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2018. The analysis included 1392 patients from the SEER database and our hospital. Among them, 1287 patients from the SEER database were assigned to the training cohort ( n = 899) and validation cohort 1 ( n = 388), while 105 patients from our hospital were assigned to validation cohort 2. A Cox regression analysis showed that age, sex, AFP, grade, stage, tumor size, surgery, and chemotherapy were independent risk factors. The nomogram developed in this study demonstrated its discriminatory ability to predict the 1-, 3-, and 5-year overall survival (OS) rates in EO-LIHC patients based on individual characteristics. Additionally, a web-based OS prediction model specifically tailored for EO-LIHC patients was created and validated. Overall, these advancements contribute to improved decision-making and personalized care for individuals with EO-LIHC.
Keyphrases
- end stage renal disease
- early onset
- ejection fraction
- chronic kidney disease
- newly diagnosed
- prognostic factors
- peritoneal dialysis
- type diabetes
- machine learning
- minimally invasive
- emergency department
- palliative care
- decision making
- radiation therapy
- risk assessment
- adipose tissue
- insulin resistance
- late onset
- chronic pain
- adverse drug
- patient reported
- quality improvement
- locally advanced
- papillary thyroid
- big data