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Macrotrabecular-massive subtype in hepatocellular carcinoma based on contrast-enhanced CT: deep learning outperforms

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A novel deep learning model accurately predicts the macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) using CT scans. This advanced tool surpasses traditional machine learning, aiding in clinical decisions and personalized therapy for HCC patients.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Hepatocellular carcinoma (HCC) is a primary liver cancer with distinct subtypes impacting prognosis and treatment.
  • Accurate non-invasive subtyping of HCC, such as the macrotrabecular-massive (MTM) subtype, is crucial for effective clinical management.
  • Current diagnostic methods may require invasive procedures, highlighting the need for advanced imaging-based predictive tools.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model for predicting the MTM subtype of HCC using contrast-enhanced computed tomography (CT) images.
  • To compare the diagnostic performance of the developed DL model against conventional machine learning (ML) models and baseline DL approaches.
  • To explore the potential of integrating clinical parameters with DL models for enhanced MTM subtype prediction.

Main Methods:

  • Retrospective collection of contrast-enhanced CT data from 368 HCC patients across two medical centers.
  • Development of a novel DL network, ResNet-ViT Contrastive Learning (RVCL), based on ResNet-50 architecture.
  • Comparative analysis of RVCL against baseline DL and ML models using Area Under the Receiver Operating Characteristic Curve (AUC) for performance evaluation.

Main Results:

  • The RVCL model achieved a superior AUC of 0.93 for MTM subtype prediction on the external test set, significantly outperforming baseline DL (AUCs 0.46-0.72) and ML models (AUCs 0.49-0.60).
  • Integration of Alpha-Fetoprotein (AFP) with the RVCL model did not yield a statistically significant improvement in diagnostic performance.
  • The RVCL model demonstrates robust accuracy in predicting the MTM subtype non-invasively.

Conclusions:

  • Contrast-enhanced CT combined with the RVCL deep learning model provides an accurate method for predicting the MTM subtype of HCC.
  • The RVCL model represents a significant advancement over traditional ML techniques for HCC subtyping.
  • This AI-driven approach offers a valuable tool for clinical decision-making and can guide personalized therapeutic strategies in HCC management.