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World J Gastroenterol ; 30(15): 2128-2142, 2024 Apr 21.
Artículo en Inglés | MEDLINE | ID: mdl-38681988

RESUMEN

BACKGROUND: The prognosis for hepatocellular carcinoma (HCC) in the presence of cirrhosis is unfavourable, primarily attributable to the high incidence of recurrence. AIM: To develop a machine learning model for predicting early recurrence (ER) of post-hepatectomy HCC in patients with cirrhosis and to stratify patients' overall survival (OS) based on the predicted risk of recurrence. METHODS: In this retrospective study, 214 HCC patients with cirrhosis who underwent curative hepatectomy were examined. Radiomics feature selection was conducted using the least absolute shrinkage and selection operator and recursive feature elimination methods. Clinical-radiologic features were selected through univariate and multivariate logistic regression analyses. Five machine learning methods were used for model comparison, aiming to identify the optimal model. The model's performance was evaluated using the receiver operating characteristic curve [area under the curve (AUC)], calibration, and decision curve analysis. Additionally, the Kaplan-Meier (K-M) curve was used to evaluate the stratification effect of the model on patient OS. RESULTS: Within this study, the most effective predictive performance for ER of post-hepatectomy HCC in the background of cirrhosis was demonstrated by a model that integrated radiomics features and clinical-radiologic features. In the training cohort, this model attained an AUC of 0.844, while in the validation cohort, it achieved a value of 0.790. The K-M curves illustrated that the combined model not only facilitated risk stratification but also exhibited significant discriminatory ability concerning patients' OS. CONCLUSION: The combined model, integrating both radiomics and clinical-radiologic characteristics, exhibited excellent performance in HCC with cirrhosis. The K-M curves assessing OS revealed statistically significant differences.


Asunto(s)
Carcinoma Hepatocelular , Hepatectomía , Cirrosis Hepática , Neoplasias Hepáticas , Aprendizaje Automático , Recurrencia Local de Neoplasia , Tomografía Computarizada por Rayos X , Humanos , Carcinoma Hepatocelular/diagnóstico por imagen , Carcinoma Hepatocelular/cirugía , Carcinoma Hepatocelular/mortalidad , Carcinoma Hepatocelular/patología , Neoplasias Hepáticas/diagnóstico por imagen , Neoplasias Hepáticas/cirugía , Neoplasias Hepáticas/mortalidad , Neoplasias Hepáticas/patología , Masculino , Femenino , Cirrosis Hepática/complicaciones , Cirrosis Hepática/diagnóstico por imagen , Cirrosis Hepática/cirugía , Estudios Retrospectivos , Persona de Mediana Edad , Recurrencia Local de Neoplasia/epidemiología , Anciano , Tomografía Computarizada por Rayos X/métodos , Pronóstico , Valor Predictivo de las Pruebas , Curva ROC , Medición de Riesgo/métodos , Medición de Riesgo/estadística & datos numéricos , Estimación de Kaplan-Meier , Adulto , Hígado/diagnóstico por imagen , Hígado/patología , Hígado/cirugía , Factores de Riesgo , Radiómica
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