| Autor: |
Tingting Mu, Xinde Zheng, Danjun Song, Jiejun Chen, Xuewang Yue, Wentao Wang, Shengxiang Rao |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
European Journal of Radiology Open, Vol 13, Iss , Pp 100610- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2352-0477 |
| DOI: |
10.1016/j.ejro.2024.100610 |
| Popis: |
Purpose: To evaluate the effectiveness of a constructed deep learning model in predicting early recurrence after surgery in hepatocellular carcinoma (HCC) patients with solitary tumors ≤5 cm. Materials and methods: Our study included a total of 331 HCC patients who underwent curative resection, with all patients having preoperative dynamic contrast-enhanced MRI (DCE-MRI). Patients who recurred within two years after surgery were defined as early recurrence. The enrolled patients were randomly divided into the training group and the testing group. A ResNet-based deep learning model with eight conventional neural network branches was built to predict the early recurrence status of these patients. Patient characteristics and laboratory tests were further filtered by regression models and then integrated with deep learning models to improve the prediction performance. Results: Among 331 HCC patients, 70 (21.1 %) experienced early recurrence. In multivariate Cox regression analysis, only tumor size (Hazard ratio (HR=1.394, 95 %CI:1.011–1.920, p value=0.043) and deep learning extracted image features (HR: 38440, 95 %CI:2321–636600, p value |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
