Materials and methods: First, a dual-time approach was assessed, for which
the CNN was provided sequences of the MRI that initially depicted new MM
(diagnosis MRI) as well as of a prediagnosis MRI: inclusion of only
contrast-enhanced T1-weighted images (CNNdual_ce) was compared with inclusion
of also the native T1-weighted images, T2-weighted images, and FLAIR sequences
of both time points (CNNdual_all).Second, results were compared with the
corresponding single time approaches, in which the CNN was provided exclusively
the respective sequences of the diagnosis MRI.Casewise diagnostic performance
parameters were calculated from 5-fold cross-validation.
Results: In total, 94 cases with 494 MMs were included. Overall, the highest
diagnostic performance was achieved by inclusion of only the contrast-enhanced
T1-weighted images of the diagnosis and of a prediagnosis MRI (CNNdual_ce,
sensitivity = 73%, PPV = 25%, F1-score = 36%). Using exclusively
contrast-enhanced T1-weighted images as input resulted in significantly less
false-positives (FPs) compared with inclusion of further sequences beyond
contrast-enhanced T1-weighted images (FPs = 5/7 for CNNdual_ce/CNNdual_all, P <
1e-5). Comparison of contrast-enhanced dual and mono time approaches revealed
that exclusion of prediagnosis MRI significantly increased FPs (FPs = 5/10 for
CNNdual_ce/CNNce, P < 1e-9).Approaches with only native sequences were clearly
inferior to CNNs that were provided contrast-enhanced sequences.
Conclusions: Automated MM detection on contrast-enhanced T1-weighted images
performed with high sensitivity. Frequent FPs due to artifacts and vessels were
significantly reduced by additional inclusion of prediagnosis MRI, but not by
inclusion of further sequences beyond contrast-enhanced T1-weighted images.
Future studies might investigate different change detection architectures for
computer-aided detection.

用对比增强 T1 加权图像进行自动检测，结合预诊断 MRI 能显著减少假阳性结果，但是在对比增强 T1 加权图像之外加入其他序列对假阳性结果减少无明显影响。