Automatic detection of Small Hepatocellular Carcinoma (<= 2 cm) in cirrhotic liver based on Gd-EOB-DTPA-enhanced MRI using deep learning

Motivation: In light of the overlapping image features between small hepatocellular carcinoma (sHCC) and benign precancerous nodules, the detection of sHCC from cirrhosis liver is deemed difficult and challenging. Goal(s): To develop a fully automatic deep learning approach for the detection of sHCC in cirrhotic livers, utilizing Gd-EOB-DTPA-enhanced MRI. Approach: A 3D nnU-Net deep learning network was trained to perform automatic segmentation and detection of sHCC lesions. Results: 120 patients were included. The AUCs for discriminating between sHCC lesions and non-sHCC lesions were 0.967 and 0.864 in the training and test cohorts,, with both P<0.001. Impact: Deep learning holds promise for the noninvasive detection of sHCC, offering the potential to alleviate the workload of radiologists and mitigate the necessity for biopsies along with their associated complications.