Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging

Table 1 Patient demographics, imaging properties of each lesion class, and details of metastatic lesion origin

Patent properties	FNH	HCC	MET	Total
Number of patients	42	13	14	69
Age in years at imaging, mean ± SD	45 ± 12	66 ± 5	57 ± 10	54 ± 14
Sex
Male	11	8	8	27
Female	31	5	6	42
Lesion properties
Number	71	69	76	216
Primary type
CRC			21
Leiomyosarcoma			18
GI adenocc. or cholangiocc.			15
Breast cc.			11
Pancreas cc.			7
Neuroendocrine ileum cc.			3
Papillary thyroid cc.			1

cc.: Carcinoma; CRC: Colorectal cancer; FNH: Focal nodular hyperplasia; GI: Gastrointestinal; HCC: Hepatocellular carcinoma; MET: Metastasis; SD: Standard deviation; T: Tesla.

Table 2 Evaluation metrics of the two-dimensional and three-dimensional densely connected convolutional neural networks

Input data	PPV	Sensitivity	F1 score	Specificity	NPV
FNH 2D	0.9091	1.0000	0.9524	0.9500	1.0000
3D	0.9000	0.9000	0.9000	0.9500	0.9500
HCC 2D	1.0000	0.8000	0.8889	1.0000	0.9091
3D	0.8750	0.7000	0.7778	0.9500	0.8636
MET 2D	0.9091	1.0000	0.9524	0.9500	1.0000
3D	0.7500	0.9000	0.8182	0.8500	0.9444
Mean 2D	0.9394	0.9333	0.9312	0.9667	0.9697
3D	0.8417	0.8333	0.8320	0.9167	0.9194

2D: Two-dimensional; 3D: Three-dimensional; FNH: Focal nodular hyperplasia; HCC: Hepatocellular carcinoma; MET: Metastasis; NPV: Negative predictive value; PPV: Positive predictive value.