Radiomics using multiparametric magnetic resonance imaging to predict postoperative visual outcomes of patients with pituitary adenoma
Summary: Background: Preoperative prediction of visual outcomes following pituitary adenoma surgery is challenging yet crucial for clinical decision-making. We aimed to develop models using radiomics from multiparametric MRI to predict postoperative visual outcomes. Methods: A cohort of 152 patient...
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Elsevier
2025-01-01
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| Series: | Asian Journal of Surgery |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1015958424015045 |
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| author | Yang Zhang Zhouyang Huang Yanjie Zhao Jianfeng Xu Chaoyue Chen Jianguo Xu |
| author_facet | Yang Zhang Zhouyang Huang Yanjie Zhao Jianfeng Xu Chaoyue Chen Jianguo Xu |
| author_sort | Yang Zhang |
| collection | DOAJ |
| description | Summary: Background: Preoperative prediction of visual outcomes following pituitary adenoma surgery is challenging yet crucial for clinical decision-making. We aimed to develop models using radiomics from multiparametric MRI to predict postoperative visual outcomes. Methods: A cohort of 152 patients with pituitary adenoma was retrospectively enrolled and divided into recovery and non-recovery groups based on visual examinations performed six months after surgery. Radiomic features of the optic chiasm were extracted from preoperative T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (T1CE). Predictive models were constructed using the least absolute shrinkage and selection operator wrapped with a support vector machine through five-fold cross-validation in the development cohort and evaluated in an independent test cohort. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity. Results: Four models were established based on radiomic features selected from individual or combined sequences. The AUC values of the models based on T1WI, T2WI and T1CE were 0.784, 0.724, 0.822 in the development cohort, and 0.767, 0.763, 0.794 in the independent test cohort. The multiparametric model demonstrated superior performance among the four models, with AUC of 0.851, accuracy of 0.832. sensitivity of 0.700, specificity of 0.910 in the development cohort, and AUC of 0.847, accuracy of 0.800, sensitivity of 0.882 and specificity of 0.750 in the independent test cohort. Conclusion: The multiparametric model utilizing radiomics of optic chiasm outperformed single-sequence models in predicting postoperative visual recovery in patients with pituitary adenoma, serving as a novel approach for enhancing personalized treatment strategies. |
| format | Article |
| id | doaj-art-43f9ca38090840f7aa7f51eb2c32b497 |
| institution | Kabale University |
| issn | 1015-9584 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Asian Journal of Surgery |
| spelling | doaj-art-43f9ca38090840f7aa7f51eb2c32b4972024-12-28T05:21:08ZengElsevierAsian Journal of Surgery1015-95842025-01-01481166172Radiomics using multiparametric magnetic resonance imaging to predict postoperative visual outcomes of patients with pituitary adenomaYang Zhang0Zhouyang Huang1Yanjie Zhao2Jianfeng Xu3Chaoyue Chen4Jianguo Xu5Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China; Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, ChinaDepartment of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China; Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, ChinaDepartment of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China; Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, ChinaDepartment of Neurosurgery, Third People's Hospital of Mianyang/Sichuan Mental Health Center, No. 109, Jianan Road, Mianyang, 621000, ChinaDepartment of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China; Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China; Corresponding author. West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, China.Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China; Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China; Corresponding author. West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, China.Summary: Background: Preoperative prediction of visual outcomes following pituitary adenoma surgery is challenging yet crucial for clinical decision-making. We aimed to develop models using radiomics from multiparametric MRI to predict postoperative visual outcomes. Methods: A cohort of 152 patients with pituitary adenoma was retrospectively enrolled and divided into recovery and non-recovery groups based on visual examinations performed six months after surgery. Radiomic features of the optic chiasm were extracted from preoperative T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (T1CE). Predictive models were constructed using the least absolute shrinkage and selection operator wrapped with a support vector machine through five-fold cross-validation in the development cohort and evaluated in an independent test cohort. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity. Results: Four models were established based on radiomic features selected from individual or combined sequences. The AUC values of the models based on T1WI, T2WI and T1CE were 0.784, 0.724, 0.822 in the development cohort, and 0.767, 0.763, 0.794 in the independent test cohort. The multiparametric model demonstrated superior performance among the four models, with AUC of 0.851, accuracy of 0.832. sensitivity of 0.700, specificity of 0.910 in the development cohort, and AUC of 0.847, accuracy of 0.800, sensitivity of 0.882 and specificity of 0.750 in the independent test cohort. Conclusion: The multiparametric model utilizing radiomics of optic chiasm outperformed single-sequence models in predicting postoperative visual recovery in patients with pituitary adenoma, serving as a novel approach for enhancing personalized treatment strategies.http://www.sciencedirect.com/science/article/pii/S1015958424015045Multiparametric MRIMachine learningPituitary adenomaRadiomicsVisual outcome |
| spellingShingle | Yang Zhang Zhouyang Huang Yanjie Zhao Jianfeng Xu Chaoyue Chen Jianguo Xu Radiomics using multiparametric magnetic resonance imaging to predict postoperative visual outcomes of patients with pituitary adenoma Asian Journal of Surgery Multiparametric MRI Machine learning Pituitary adenoma Radiomics Visual outcome |
| title | Radiomics using multiparametric magnetic resonance imaging to predict postoperative visual outcomes of patients with pituitary adenoma |
| title_full | Radiomics using multiparametric magnetic resonance imaging to predict postoperative visual outcomes of patients with pituitary adenoma |
| title_fullStr | Radiomics using multiparametric magnetic resonance imaging to predict postoperative visual outcomes of patients with pituitary adenoma |
| title_full_unstemmed | Radiomics using multiparametric magnetic resonance imaging to predict postoperative visual outcomes of patients with pituitary adenoma |
| title_short | Radiomics using multiparametric magnetic resonance imaging to predict postoperative visual outcomes of patients with pituitary adenoma |
| title_sort | radiomics using multiparametric magnetic resonance imaging to predict postoperative visual outcomes of patients with pituitary adenoma |
| topic | Multiparametric MRI Machine learning Pituitary adenoma Radiomics Visual outcome |
| url | http://www.sciencedirect.com/science/article/pii/S1015958424015045 |
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