| Autor: |
Yuzhou Huang, Shurui Zhou, Yanji Luo, Jinmao Zou, Yaqing Li, Shaojie Chen, Ming Gao, Kaihong Huang, Guoda Lian |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Radiation Oncology, Vol 18, Iss 1, Pp 1-11 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1748-717X |
| DOI: |
10.1186/s13014-023-02273-w |
| Popis: |
Abstract Background Nearly one fourth of patients with pancreatic ductal adenocarcinoma (PDAC) occur to liver metastasis after surgery, and liver metastasis is a risk factor for prognosis for those patients with surgery therapy. However, there is no effective way to predict liver metastasis post-operation. Method Clinical data and preoperative magnetic resonance imaging (MRI) of PDAC patients diagnosed between July 2010 and July 2020 were retrospectively collected from three hospital centers in China. The significant MRI radiomics features or clinicopathological characteristics were used to establish a model to predict liver metastasis in the development and validation cohort. Results A total of 204 PDAC patients from three hospital centers were divided randomly (7:3) into development and validation cohort. Due to poor predictive value of clinical features, MRI radiomics model had similar receiver operating characteristics curve (ROC) value to clinical-radiomics combing model in development cohort (0.878 vs. 0.880, p = 0.897) but better ROC in validation dataset (0.815 vs. 0.732, p = 0.022). Radiomics model got a sensitivity of 0.872/0.750 and a specificity of 0.760/0.822 to predict liver metastasis in development and validation cohort, respectively. Among 54 patients randomly selected with post-operation specimens, fibrosis markers (α-smooth muscle actin) staining was shown to promote radiomics model with ROC value from 0.772 to 0.923 (p = 0.049) to predict liver metastasis. Conclusion This study developed and validated an MRI-based radiomics model and showed a good performance in predicting liver metastasis in resectable PDAC patients. |
| Databáze: |
Directory of Open Access Journals |
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