Keywords: Diagnosis/Prediction, Liver, deep learning，small hepatocellular carcinomas，Dysplastic Nodule
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords