Deep learning analysis of magnetic resonance imaging accurately detects early-stage perihilar cholangiocarcinoma in patients with primary sclerosing cholangitis

Background and aims: Among those with primary sclerosing cholangitis (PSC), perihilar CCA (pCCA) is often diagnosed at a late-stage and is a leading source of mortality. Detection of pCCA in PSC when curative action can be taken is challenging. Our aim was to create a deep learning model that analyzed magnetic resonance imaging (MRI) to detect early-stage pCCA and compare its diagnostic performance with expert radiologists. Approach and results: We conducted a multicenter, international, retrospective cohort study involving adults with large duct PSC who underwent contrast-enhanced MRI. Senior abdominal radiologists reviewed the images. All patients with pCCA had early-stage cancer and were registered for liver transplantation. We trained a 3D DenseNet-121 model, a form of deep learning, using MRI images and assessed its performance in a separate test cohort. The study included 398 patients (training cohort n=150; test cohort n=248). pCCA was present in 230 individuals (training cohort n=64; test cohort n=166). In the test cohort, the respective performances of the model compared to the radiologists were: sensitivity 87.9% versus 50.0%, p<0.001; specificity 84.1% versus 100.0%, p<0.001; area under receiving operating curve 86.0% versus 75.0%, p<0.001. Even when a mass was absent, the model had a higher sensitivity for pCCA than radiologists (91.6% vs. 50.6%, p<0.001) and maintained good specificity (84.1%). Conclusion: The 3D DenseNet-121 MRI model effectively detects early-stage pCCA in PSC patients. Compared to expert radiologists, the model missed fewer cases of cancer.