  Objectives: to propose a fully-automatic computer-aided diagnosis (CAD)
solution for liver lesion characterization, with uncertainty estimation.
  Methods: we enrolled 400 patients who had either liver resection or a biopsy
and was diagnosed with either hepatocellular carcinoma (HCC), intrahepatic
cholangiocarcinoma, or secondary metastasis, from 2006 to 2019. Each patient
was scanned with T1WI, T2WI, T1WI venous phase (T2WI-V), T1WI arterial phase
(T1WI-A), and DWI MRI sequences. We propose a fully-automatic deep CAD pipeline
that localizes lesions from 3D MRI studies using key-slice parsing and provides
a confidence measure for its diagnoses. We evaluate using five-fold cross
validation and compare performance against three radiologists, including a
senior hepatology radiologist, a junior hepatology radiologist and an abdominal
radiologist.
  Results: the proposed CAD solution achieves a mean F1 score of 0.62,
outperforming the abdominal radiologist (0.47), matching the junior hepatology
radiologist (0.61), and underperforming the senior hepatology radiologist
(0.68). The CAD system can informatively assess its diagnostic confidence,
i.e., when only evaluating on the 70% most confident cases the mean f1 score
and sensitivity at 80% specificity for HCC vs. others are boosted from 0.62 to
0.71 and 0.84 to 0.92, respectively.
  Conclusion: the proposed fully-automatic CAD solution can provide good
diagnostic performance with informative confidence assessments in finding and
discriminating liver lesions from MRI studies.
