Deep learning analysis of MRI accurately detects early-stage perihilar cholangiocarcinoma in patients with primary sclerosing cholangitis
Yashbir Singh, John E. Eaton, Sudhakar K. Venkatesh, Christopher L. Welle, Byron H. Smith, Shahriar Faghani, Mette Vesterhus, Tom H. Karlsen, Kristin Kaasen Jørgensen, Trine Folseraas, Kosta Petrovic, Anne Negård, Ida Bjoerk, Andreas Abildgaard, Aliya Gulamhusein, Kartik Jhaveri, Gregory J. Gores, Sumera I. Ilyas, Timuçin Taner, Julie K. Heimbach, Tayyab S. Diwan, Nicholas F. LaRusso, Konstantinos N. Lazaridis, Bradley J. Erickson
Abstract
BACKGROUND AND AIMS: Among those with primary sclerosing cholangitis (PSC), perihilar cholangiocarcinoma (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 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%). CONCLUSIONS: 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.