| Autor: |
Chen, Qingqing, Zhu, Yajing, Chen, Yinan, Wang, Fang, Hu, Xi, Ye, Yuxiang, Dou, Xin, Huang, Yechong, Deng, Liping, Zhou, Wei, Liang, Xiao, Hu, Hongjie |
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| Zdroj: |
Medical Physics; May2023, Vol. 50 Issue 5, p2872-2883, 12p |
| Abstrakt: |
Purpose: To investigate the applicability of multidimensional convolutional neural networks (CNNs) together with multiphase contrast‐enhanced CT images on automated detection of diverse focal liver lesions (FLLs). Methods: We trained detection models based on 2.5D and 3D CNN frameworks using 567 patients with 3892 FLLs and validated on a relatively large independent cohort of 1436 patients with 4723 lesions. The detection performance across different phases (arterial, portal venous [PV], and combined phases) was assessed for the 2.5D model. The lesions were divided into two groups with a cutoff size of 20 mm, and further subdivided into four subgroups of <10, 10–20, 20–50, and ≥50 mm, to verify the detection rates for lesions of different sizes for the 2.5D and 3D models. McNemar's test was used to compare the detection sensitivities among different methods. In addition, sensitivity with 95% confidence intervals and free‐response receiver operating characteristics (FROC) curves were plotted for visualization of the detectability. Results: In the 2.5D model, the detection rate of PV phase outperformed arterial phase, and a combination of the two phases further improved the performance over a single phase. The detection sensitivities in the arterial, PV, and combined phases were 0.737 versus 0.802 versus 0.832 for all lesions. The 3D model was superior to the 2.5D model for detecting benign lesions (0.896 vs. 0.807, p < 0.001), malignant lesions (0.940 vs. 0.918, p = 0.013), and all lesions (0.902 vs. 0.832, p < 0.001) regardless of size division. Particularly, the 3D model showed higher sensitivity than the 2.5D model in detecting lesions smaller than 20 mm (0.868 vs. 0.759, p < 0.001). For lesions larger than 20 mm, both the 3D and the 2.5D models achieved excellent detection performance. Conclusions: The proposed CNN detection model was demonstrated to adaptively learn the feature representations of diverse FLLs and generalize well to a large‐scale validation dataset. The use of multiphase significantly improved the detectability of FLLs compared to single phase. 3D CNN framework showed an enhanced capability over the 2.5D in the detection of FLLs, particularly small lesions. The promising performance shows that the proposed CNN detection system could be a powerful clinical tool for the early detection of hepatic tumors. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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