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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Wu, Linyong Lai, Qingfeng Li, Songhua Wu, Shaofeng Li, Yizhong Huang, Ju Zeng, Qiuli Wei, Dayou |
| Abstract | Background The aim of this study was to conduct a systematic review and meta-analysis to comprehensively evaluate the performance and methodological quality of artificial intelligence (AI) in predicting recurrence after single first-line treatment for liver cancer. Methods A rigorous and systematic evaluation was conducted on the AI studies related to recurrence after single first-line treatment for liver cancer, retrieved from the PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases. The area under the curve (AUC), sensitivity (SENC), and specificity (SPEC) of each study were extracted for meta-analysis. Results Six percutaneous ablation (PA) studies, 16 surgical resection (SR) studies, and 5 transarterial chemoembolization (TACE) studies were included in the meta-analysis for predicting recurrence after hepatocellular carcinoma (HCC) treatment, respectively. Four SR studies and 2 PA studies were included in the meta-analysis for recurrence after intrahepatic cholangiocarcinoma (ICC) and colorectal cancer liver metastasis (CRLM) treatment. The pooled SENC, SEPC, and AUC of AI in predicting recurrence after primary HCC treatment via PA, SR, and TACE were 0.78, 0.90, and 0.92; 0.81, 0.77, and 0.86; and 0.73, 0.79, and 0.79, respectively. The values for ICC treated with SR and CRLM treated with PA were 0.85, 0.71, 0.86 and 0.69, 0.63,0.74, respectively. Conclusion This systematic review and meta-analysis demonstrates the comprehensive application value of AI in predicting recurrence after a single first-line treatment of liver cancer, with satisfactory results, indicating the clinical translation potential of AI in predicting recurrence after liver cancer treatment. |
| Related Links | https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-024-01440-z.pdf |
| Ending Page | 19 |
| Page Count | 19 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712342 |
| DOI | 10.1186/s12880-024-01440-z |
| Journal | BMC Medical Imaging |
| Issue Number | 1 |
| Volume Number | 24 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-10-07 |
| Access Restriction | Open |
| Subject Keyword | Imaging Radiology Liver cancer Artificial intelligence Recurrence Percutaneous ablation Surgical resection Transarterial chemoembolization |
| Content Type | Text |
| Resource Type | Article |
| Subject | Radiology, Nuclear Medicine and Imaging |
| Journal Impact Factor | 2.9/2023 |
| 5-Year Journal Impact Factor | 2.8/2023 |
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