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

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.