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Personalized Mortality Risk Stratification in ALD- and MASLD-Related Hepatocellular Carcinoma Using a Machine

Miguel Suárez1,2,3, Sergio Gil-Rojas1,2,3, Pablo Martínez-Blanco1,2,3

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This summary is machine-generated.

Machine learning, particularly Random Forest, accurately predicts mortality in hepatocellular carcinoma (HCC) patients with alcohol-associated liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD). Liver function and inflammation markers are key predictors, outperforming tumor markers.

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Artificial IntelligenceRandom Forestalcohol-associated liver diseasehepatocellular carcinomamachine learningmetabolic-dysfunction associated steatotic liver diseaseprognosis

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Area of Science:

  • Hepatology
  • Machine Learning in Medicine
  • Oncology

Background:

  • Hepatocellular carcinoma (HCC) epidemiology is shifting, with alcohol-associated liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD) emerging as primary drivers in developed nations.
  • Early identification of prognostic factors for mortality in these specific HCC patient groups is crucial for timely intervention.

Purpose of the Study:

  • To identify key prognostic factors for mortality at diagnosis in HCC patients with ALD and MASLD.
  • To evaluate the performance of various machine learning (ML) algorithms in predicting mortality within this cohort.
  • To establish a reference predictive model using the Random Forest (RF) algorithm.

Main Methods:

  • A retrospective, multicenter cohort study involving 91 patients diagnosed with ALD- or MASLD-related HCC between 2008 and 2023.
  • Collection of demographic, clinical, and biochemical data.
  • Implementation and comparison of multiple ML algorithms (RF, SVM, Decision Tree, Naïve Bayes, KNN) with Bayesian optimization for hyperparameter tuning.

Main Results:

  • The Random Forest (RF) algorithm demonstrated superior predictive performance (AUC: 0.91, precision: 90.67%, F1 score: 91.05%), significantly outperforming other ML models.
  • Key predictors of mortality included serum albumin, CRP/albumin ratio, Barcelona Clinic Liver Cancer (BCLC) stage, and the Albumin-Bilirubin (ALBI) score.
  • MELD 3.0 showed greater predictive accuracy than other MELD score variations, while alpha-fetoprotein (AFP) had limited prognostic value.

Conclusions:

  • Liver function markers and systemic inflammation indicators are more effective than tumor markers for predicting early mortality in ALD- and MASLD-related HCC.
  • The RF algorithm shows significant potential for personalized prognosis in this patient population.
  • External validation of the RF model in independent datasets is necessary before widespread clinical adoption.