Multi-sequence magnetic resonance imaging radiomics combined with imaging features predicts the difficulty of HIFU treatment of uterine fibroids
Abstract To establish a multivariate linear regression model for predicting the difficulty of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids based on multi-sequence magnetic resonance imaging radiomics features. A retrospective analysis was conducted on 218 patients with uteri...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86958-0 |
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| author | Li Shen Xiao Huang Yuyao Liu Shanwei Bai Fang Wang Quan Yang |
| author_facet | Li Shen Xiao Huang Yuyao Liu Shanwei Bai Fang Wang Quan Yang |
| author_sort | Li Shen |
| collection | DOAJ |
| description | Abstract To establish a multivariate linear regression model for predicting the difficulty of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids based on multi-sequence magnetic resonance imaging radiomics features. A retrospective analysis was conducted on 218 patients with uterine fibroids who underwent HIFU treatment, including 178 cases from Yongchuan Hospital of Chongqing Medical University and 40 cases from the Second Affiliated Hospital of Chongqing Medical University (external validation set). Radiomics features were extracted and selected from magnetic resonance images, and potentially related imaging features were collected. The energy efficiency factor (EEF) was used as the dependent variable. Imaging models, radiomics models, and joint models were established using a stepwise approach. The model with the highest R2 value was selected for external validation. The R2 value of the combined model was 0.642, higher than that of other models. Spearman correlation analysis showed a correlation coefficient of R = 0.824 (P < 0.001) between predicted EEF and actual EEF. External validation yielded a correlation coefficient of R = 0.645 (P < 0.001). A model for predicting EEF has been developed, which is clinically important for predicting the difficulty of HIFU treatment of uterine fibroids. |
| format | Article |
| id | doaj-art-4030f61c521f403c870a023d2d79879c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-4030f61c521f403c870a023d2d79879c2025-01-26T12:29:34ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-025-86958-0Multi-sequence magnetic resonance imaging radiomics combined with imaging features predicts the difficulty of HIFU treatment of uterine fibroidsLi Shen0Xiao Huang1Yuyao Liu2Shanwei Bai3Fang Wang4Quan Yang5Yongchuan Hospital of Chongqing Medical UniversityYongchuan Hospital of Chongqing Medical UniversityYongchuan Hospital of Chongqing Medical UniversityThe Second Affiliated Hospital of Chongqing Medical UniversityDepartment of Research and Development, Shanghai United Imaging Intelligence Co., LtdYongchuan Hospital of Chongqing Medical UniversityAbstract To establish a multivariate linear regression model for predicting the difficulty of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids based on multi-sequence magnetic resonance imaging radiomics features. A retrospective analysis was conducted on 218 patients with uterine fibroids who underwent HIFU treatment, including 178 cases from Yongchuan Hospital of Chongqing Medical University and 40 cases from the Second Affiliated Hospital of Chongqing Medical University (external validation set). Radiomics features were extracted and selected from magnetic resonance images, and potentially related imaging features were collected. The energy efficiency factor (EEF) was used as the dependent variable. Imaging models, radiomics models, and joint models were established using a stepwise approach. The model with the highest R2 value was selected for external validation. The R2 value of the combined model was 0.642, higher than that of other models. Spearman correlation analysis showed a correlation coefficient of R = 0.824 (P < 0.001) between predicted EEF and actual EEF. External validation yielded a correlation coefficient of R = 0.645 (P < 0.001). A model for predicting EEF has been developed, which is clinically important for predicting the difficulty of HIFU treatment of uterine fibroids.https://doi.org/10.1038/s41598-025-86958-0Uterine fibroidsHIFUEEFRadiomics |
| spellingShingle | Li Shen Xiao Huang Yuyao Liu Shanwei Bai Fang Wang Quan Yang Multi-sequence magnetic resonance imaging radiomics combined with imaging features predicts the difficulty of HIFU treatment of uterine fibroids Scientific Reports Uterine fibroids HIFU EEF Radiomics |
| title | Multi-sequence magnetic resonance imaging radiomics combined with imaging features predicts the difficulty of HIFU treatment of uterine fibroids |
| title_full | Multi-sequence magnetic resonance imaging radiomics combined with imaging features predicts the difficulty of HIFU treatment of uterine fibroids |
| title_fullStr | Multi-sequence magnetic resonance imaging radiomics combined with imaging features predicts the difficulty of HIFU treatment of uterine fibroids |
| title_full_unstemmed | Multi-sequence magnetic resonance imaging radiomics combined with imaging features predicts the difficulty of HIFU treatment of uterine fibroids |
| title_short | Multi-sequence magnetic resonance imaging radiomics combined with imaging features predicts the difficulty of HIFU treatment of uterine fibroids |
| title_sort | multi sequence magnetic resonance imaging radiomics combined with imaging features predicts the difficulty of hifu treatment of uterine fibroids |
| topic | Uterine fibroids HIFU EEF Radiomics |
| url | https://doi.org/10.1038/s41598-025-86958-0 |
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