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Objectives <p>To develop an automated deep learning (DL) methodology for detecting small hepatocellular carcinoma (sHCC) in cirrhotic livers, leveraging Gd-EOB-DTPA-enhanced MRI.</p> Methods <p>The present retrospective study included a total of 120 patients with cirrhosis, comprising 78 patients with sHCC and 42 patients with non-HCC cirrhosis, who were selected through stratified sampling. The dataset was divided into training and testing sets (8:2 ratio). The nnU-Net exhibits enhanced capabilities in segmenting small objects. The segmentation performance was assessed using the Dice coefficient. The ability to distinguish between sHCC and non-HCC lesions was evaluated through ROC curves, AUC scores and <i>P</i> values. The case-level detection performance for sHCC was evaluated through several metrics: accuracy, sensitivity, and specificity.</p> Results <p>The AUCs for distinguishing sHCC patients from non-HCC patients at the lesion level were 0.967 and 0.864 for the training and test cohorts, respectively, both of which were statistically significant at <i>P</i> &lt; 0.001. At the case level, distinguishing between patients with sHCC and patients with cirrhosis resulted in accuracies of 92.5% (95% CI, 85.1–96.9%) and 81.5% (95% CI, 61.9–93.7%), sensitivities of 95.1% (95% CI, 86.3–99.0%) and 88.2% (95% CI, 63.6–98.5%), and specificities of 87.5% (95% CI, 71.0–96.5%) and 70% (95% CI, 34.8–93.3%) for the training and test sets, respectively.</p> Conclusion <p>The DL methodology demonstrated its efficacy in detecting sHCC within a cohort of patients with cirrhosis.</p> Graphical Abstract <p></p>

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Automated detection of small hepatocellular carcinoma in cirrhotic livers: applying deep learning to Gd-EOB-DTPA-enhanced MRI

  • 期刊论文

  • 发表日期：

    2025年3月10日

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  • JunQiang Lei,
  • YongSheng Xu,
  • YuanHui Zhu,
  • ShanShan Jiang,
  • Song Tian,
  • Yi Zhu

摘要

Objectives

To develop an automated deep learning (DL) methodology for detecting small hepatocellular carcinoma (sHCC) in cirrhotic livers, leveraging Gd-EOB-DTPA-enhanced MRI.

Methods

The present retrospective study included a total of 120 patients with cirrhosis, comprising 78 patients with sHCC and 42 patients with non-HCC cirrhosis, who were selected through stratified sampling. The dataset was divided into training and testing sets (8:2 ratio). The nnU-Net exhibits enhanced capabilities in segmenting small objects. The segmentation performance was assessed using the Dice coefficient. The ability to distinguish between sHCC and non-HCC lesions was evaluated through ROC curves, AUC scores and P values. The case-level detection performance for sHCC was evaluated through several metrics: accuracy, sensitivity, and specificity.

Results

The AUCs for distinguishing sHCC patients from non-HCC patients at the lesion level were 0.967 and 0.864 for the training and test cohorts, respectively, both of which were statistically significant at P < 0.001. At the case level, distinguishing between patients with sHCC and patients with cirrhosis resulted in accuracies of 92.5% (95% CI, 85.1–96.9%) and 81.5% (95% CI, 61.9–93.7%), sensitivities of 95.1% (95% CI, 86.3–99.0%) and 88.2% (95% CI, 63.6–98.5%), and specificities of 87.5% (95% CI, 71.0–96.5%) and 70% (95% CI, 34.8–93.3%) for the training and test sets, respectively.

Conclusion

The DL methodology demonstrated its efficacy in detecting sHCC within a cohort of patients with cirrhosis.

Graphical Abstract