Published online Sep 15, 2024. doi: 10.4251/wjgo.v16.i9.3839
Revised: July 31, 2024
Accepted: August 7, 2024
Published online: September 15, 2024
Processing time: 163 Days and 23.7 Hours
Liver cancer is one of the most prevalent malignant tumors worldwide, and its early detection and treatment are crucial for enhancing patient survival rates and quality of life. However, the early symptoms of liver cancer are often not obvious, resulting in a late-stage diagnosis in many patients, which significantly reduces the effectiveness of treatment. Developing a highly targeted, widely applicable, and practical risk prediction model for liver cancer is crucial for enhancing the early diagnosis and long-term survival rates among affected individuals.
To develop a liver cancer risk prediction model by employing machine learning techniques, and subsequently assess its performance.
In this study, a total of 550 patients were enrolled, with 190 hepatocellular carcinoma (HCC) and 195 cirrhosis patients serving as the training cohort, and 83 HCC and 82 cirrhosis patients forming the validation cohort. Logistic regression (LR), support vector machine (SVM), random forest (RF), and least absolute shrinkage and selection operator (LASSO) regression models were developed in the training cohort. Model performance was assessed in the validation cohort. Additionally, this study conducted a comparative evaluation of the diagnostic efficacy between the ASAP model and the model developed in this study using receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) to determine the optimal predictive model for assessing liver cancer risk.
Six variables including age, white blood cell, red blood cell, platelet counts, alpha-fetoprotein and protein induced by vitamin K absence or antagonist II levels were used to develop LR, SVM, RF, and LASSO regression models. The RF model exhibited superior discrimination, and the area under curve of the training and validation sets was 0.969 and 0.858, respectively. These values significantly surpassed those of the LR (0.850 and 0.827), SVM (0.860 and 0.803), LASSO regression (0.845 and 0.831), and ASAP (0.866 and 0.813) models. Furthermore, calibration and DCA indicated that the RF model exhibited robust calibration and clinical validity.
The RF model demonstrated excellent prediction capabilities for HCC and can facilitate early diagnosis of HCC in clinical practice.
Core Tip: We constructed a prediction model for hepatocellular carcinoma with reliable and effective clinical diagnostic capacity. In the training cohort (n = 385), machine learning models were developed based on six variables including age; white blood cell, red blood cell, and platelet counts; and alpha-fetoprotein and protein induced by vitamin K absence or antagonist II levels. The performance of these models was assessed in an independent validation cohort of 165 subjects. We compared our model with the ASAP model using receiver operating characteristic curve, calibration, and decision curve analysis. Our findings demonstrated that a random forest model exhibited discriminatory power, calibration performance, and clinical utility.