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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Sun, Bao-Ye Gu, Pei-Yi Guan, Ruo-Yu Zhou, Cheng Lu, Jian-Wei Yang, Zhang-Fu Pan, Chao Zhou, Pei-Yun Zhu, Ya-Ping Li, Jia-Rui Wang, Zhu-Tao Gao, Shan-Shan Gan, Wei Yi, Yong Luo, Ye Qiu, Shuang-Jian |
| Abstract | Background Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC. Methods We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MRI of these patients were collected, annotated, and further analyzed by DL in this study. A predictive model for MVI integrating DL-predicted MVI status (DL-MVI) and clinical parameters was constructed with multivariate logistic regression. Results Of 321 HCC patients, 136 patients were pathologically MVI absent and 185 patients were MVI present. Recurrence-free survival (RFS) and overall survival (OS) were significantly different between the DL-predicted MVI-absent and MVI-present. Among all clinical variables, only DL-predicted MVI status and a-fetoprotein (AFP) were independently associated with MVI: DL-MVI (odds ratio [OR] = 35.738; 95% confidence interval [CI] 14.027–91.056; p < 0.001), AFP (OR = 4.634, 95% CI 2.576–8.336; p < 0.001). To predict the presence of MVI, DL-MVI combined with AFP achieved an area under the curve (AUC) of 0.824. Conclusions Our predictive model combining DL-MVI and AFP achieved good performance for predicting MVI and clinical outcomes in patients with HCC. |
| Related Links | https://wjso.biomedcentral.com/counter/pdf/10.1186/s12957-022-02645-8.pdf |
| Ending Page | 13 |
| Page Count | 13 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14777819 |
| DOI | 10.1186/s12957-022-02645-8 |
| Journal | World Journal of Surgical Oncology |
| Issue Number | 1 |
| Volume Number | 20 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2022-06-08 |
| Access Restriction | Open |
| Subject Keyword | Surgical Oncology Deep learning Microvascular invasion Clinical outcome HCC |
| Content Type | Text |
| Resource Type | Article |
| Subject | Oncology Surgery |
| Journal Impact Factor | 2.5/2023 |
| 5-Year Journal Impact Factor | 2.8/2023 |
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