A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study
ObjectivePreoperative prediction of visual recovery after pituitary adenoma resection surgery remains challenging. This study aimed to investigate the value of clinical and radiological features in preoperatively predicting visual outcomes after surgery.MethodsPatients undergoing endoscopic transsph...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2024.1479442/full |
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| author | Zijian Zheng Zijian Zheng He Wang Qianxi Chen Qianxi Chen Zhicheng Wang Zhicheng Wang Jun Fu Jun Fu Wenjian Fan Wenjian Fan Yuanxiang Lin Yuanxiang Lin Dezhi Kang Dezhi Kang Dezhi Kang Changzhen Jiang Changzhen Jiang Zhangya Lin Zhangya Lin Xiaorong Yan Xiaorong Yan Xiaorong Yan |
| author_facet | Zijian Zheng Zijian Zheng He Wang Qianxi Chen Qianxi Chen Zhicheng Wang Zhicheng Wang Jun Fu Jun Fu Wenjian Fan Wenjian Fan Yuanxiang Lin Yuanxiang Lin Dezhi Kang Dezhi Kang Dezhi Kang Changzhen Jiang Changzhen Jiang Zhangya Lin Zhangya Lin Xiaorong Yan Xiaorong Yan Xiaorong Yan |
| author_sort | Zijian Zheng |
| collection | DOAJ |
| description | ObjectivePreoperative prediction of visual recovery after pituitary adenoma resection surgery remains challenging. This study aimed to investigate the value of clinical and radiological features in preoperatively predicting visual outcomes after surgery.MethodsPatients undergoing endoscopic transsphenoidal surgery (ETS) for pituitary adenoma were included in this retrospective and prospective study. Preoperative MRI, visual acuity, visual field, and postoperative visual recovery data were collected. Logistic regression analysis was used to assess the importance of clinical and MRI features, and a prediction model was developed.ResultsThe cohort included 198 patients (150 retrospective, 48 prospective). In the retrospective data, visual recovery was observed in 111 patients (74.0%), while non-recovery was observed in 39 patients (26.0%). In the prospective data, visual recovery was observed in 27 patients (56.25%) and non-recovery in 21 patients (43.75%). Blindness, headache, adenoma area, and adenoma upward growth distance were negatively correlated with visual recovery (p < 0.05), while the pituitary gland area was positively correlated (p = 0.001). Logistic regression selected three clinical features: blindness, headache, and visual impairment course. Two additional imaging features, pituitary gland maximum area, and adenoma maximum area, were incorporated into the prediction model. The area under the curve of the prediction model was 0.944 in the retrospective cohort and 0.857 in the prospective cohort. Accuracy was 88% and 81.25%, respectively.ConclusionThis study successfully developed a clinical practical model combining clinical and radiological features to preoperatively predict visual recovery for patients with pituitary adenoma. The model has the potential to provide personalized counseling for individual patients. |
| format | Article |
| id | doaj-art-8ac74bcab1414a7ea556d75eed2e3c0b |
| institution | Kabale University |
| issn | 1664-2392 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Endocrinology |
| spelling | doaj-art-8ac74bcab1414a7ea556d75eed2e3c0b2025-01-28T16:59:15ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922024-12-011510.3389/fendo.2024.14794421479442A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective studyZijian Zheng0Zijian Zheng1He Wang2Qianxi Chen3Qianxi Chen4Zhicheng Wang5Zhicheng Wang6Jun Fu7Jun Fu8Wenjian Fan9Wenjian Fan10Yuanxiang Lin11Yuanxiang Lin12Dezhi Kang13Dezhi Kang14Dezhi Kang15Changzhen Jiang16Changzhen Jiang17Zhangya Lin18Zhangya Lin19Xiaorong Yan20Xiaorong Yan21Xiaorong Yan22Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, Xuanwu Hospital, Capital Medical University, China International Neuroscience Institute, Beijing, ChinaDepartment of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaFujian Provincial Institutes of Brain Disorders and Brain Sciences, First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaFujian Provincial Institutes of Brain Disorders and Brain Sciences, First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaObjectivePreoperative prediction of visual recovery after pituitary adenoma resection surgery remains challenging. This study aimed to investigate the value of clinical and radiological features in preoperatively predicting visual outcomes after surgery.MethodsPatients undergoing endoscopic transsphenoidal surgery (ETS) for pituitary adenoma were included in this retrospective and prospective study. Preoperative MRI, visual acuity, visual field, and postoperative visual recovery data were collected. Logistic regression analysis was used to assess the importance of clinical and MRI features, and a prediction model was developed.ResultsThe cohort included 198 patients (150 retrospective, 48 prospective). In the retrospective data, visual recovery was observed in 111 patients (74.0%), while non-recovery was observed in 39 patients (26.0%). In the prospective data, visual recovery was observed in 27 patients (56.25%) and non-recovery in 21 patients (43.75%). Blindness, headache, adenoma area, and adenoma upward growth distance were negatively correlated with visual recovery (p < 0.05), while the pituitary gland area was positively correlated (p = 0.001). Logistic regression selected three clinical features: blindness, headache, and visual impairment course. Two additional imaging features, pituitary gland maximum area, and adenoma maximum area, were incorporated into the prediction model. The area under the curve of the prediction model was 0.944 in the retrospective cohort and 0.857 in the prospective cohort. Accuracy was 88% and 81.25%, respectively.ConclusionThis study successfully developed a clinical practical model combining clinical and radiological features to preoperatively predict visual recovery for patients with pituitary adenoma. The model has the potential to provide personalized counseling for individual patients.https://www.frontiersin.org/articles/10.3389/fendo.2024.1479442/fullpituitary adenomaclinical practical modelvisual outcomegraphic segmentationmachine learning (ML) |
| spellingShingle | Zijian Zheng Zijian Zheng He Wang Qianxi Chen Qianxi Chen Zhicheng Wang Zhicheng Wang Jun Fu Jun Fu Wenjian Fan Wenjian Fan Yuanxiang Lin Yuanxiang Lin Dezhi Kang Dezhi Kang Dezhi Kang Changzhen Jiang Changzhen Jiang Zhangya Lin Zhangya Lin Xiaorong Yan Xiaorong Yan Xiaorong Yan A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study Frontiers in Endocrinology pituitary adenoma clinical practical model visual outcome graphic segmentation machine learning (ML) |
| title | A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study |
| title_full | A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study |
| title_fullStr | A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study |
| title_full_unstemmed | A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study |
| title_short | A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study |
| title_sort | clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study |
| topic | pituitary adenoma clinical practical model visual outcome graphic segmentation machine learning (ML) |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2024.1479442/full |
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