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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 biopsyand was diagnosed with either hepatocellular carcinoma (HCC), intrahepaticcholangiocarcinoma, or secondary metastasis, from 2006 to 2019. Each patientwas 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 pipelinethat localizes lesions from 3D MRI studies using key-slice parsing and providesa confidence measure for its diagnoses. We evaluate using five-fold crossvalidation and compare performance against three radiologists, including asenior hepatology radiologist, a junior hepatology radiologist and an abdominalradiologist.  Results: the proposed CAD solution achieves a mean F1 score of 0.62,outperforming the abdominal radiologist (0.47), matching the junior hepatologyradiologist (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 scoreand sensitivity at 80% specificity for HCC vs. others are boosted from 0.62 to0.71 and 0.84 to 0.92, respectively.  Conclusion: the proposed fully-automatic CAD solution can provide gooddiagnostic performance with informative confidence assessments in finding anddiscriminating liver lesions from MRI studies.

A deep learning pipeline for localization, differentiation, and uncertainty estimation of liver lesions using multi-phasic and multi-sequence MRI