Construction and evaluation of a liver cancer risk prediction model based on machine learning

BACKGROUND 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.AIM To develop a liver cancer risk prediction model by employing machine learning techniques,and subsequently assess its performance.METHODS 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.RESULTS 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.CONCLUSION The RF model demonstrated excellent prediction capabilities for HCC and can facilitate early diagnosis of HCC in clinical practice.

Correlation analysis of gene polymorphisms and β-lactam allergy

A total of 64 patients with β-lactam allergy and 30 control subjects were enrolled in a case-control study. This study is aimed to analyze the relationship between β-lactam allergy and 10 single nucleotide polymorphisms（SNPs） in interleukin-10（IL-10）, IL-13, IL-4Rα, high-affinity immunoglobulin E-receptor β chain（FcεRIβ）, interferon γ receptor 2（IFNGR2）, and CYP3A4, and within the Han Chinese population of Northwest China. Genotyping for the SNPs was conducted using the Sequenom Mass ARRAY platform. SPSS 17.0 was employed to analyze the statistical data and SHEsis was used to perform the haplotype reconstruction and analyze linkage disequilibrium of SNPs of IL-10 and IL-13. The results showed that the genotype distribution of CYP3A4 rs2242480/CT differed significantly between case and control groups of males（P=0.022; odds ratio（OR）=0.167, 95% confidence interval（CI）： 0.032–0.867）. Further analysis showed that CCA, CCG, and TAA haplotypes of IL-10 had no significant correlation in patients with β-lactam allergy. The correlation between CCT and CAC haplotypes of IL-13 and β-lactam allergy needs to be further studied. The analysis did not reveal any differences in the distribution of others gene polymorphisms between cases and controls.