Evidence map›Paper›PMID 39735648›Full record

ArticleFrontiers in endocrinology2024

A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study.

Zijian Zheng, He Wang, Qianxi Chen, Zhicheng Wang, Jun Fu, Wenjian Fan, Yuanxiang Lin, Dezhi Kang, Changzhen Jiang, Zhangya Lin and 1 more

Erratum issuedAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Zijian Zheng *Department of Neurosurgery, Binhai Branch of Nation al Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
He Wang *Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, China International Neuroscience Institute, Beijing, China.
Qianxi Chen *Department of Neurosurgery, Binhai Branch of Nation al Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
Zhicheng WangDepartment of Neurosurgery, Binhai Branch of Nation al Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
Jun FuDepartment of Neurosurgery, Binhai Branch of Nation al Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
Wenjian FanDepartment of Neurosurgery, Binhai Branch of Nation al Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
Yuanxiang LinDepartment of Neurosurgery, Binhai Branch of Nation al Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
Dezhi KangDepartment of Neurosurgery, Binhai Branch of Nation al Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
Changzhen JiangDepartment of Neurosurgery, Binhai Branch of Nation al Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
Zhangya LinDepartment of Neurosurgery, Binhai Branch of Nation al Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
Xiaorong YanDepartment of Neurosurgery, Binhai Branch of Nation al Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Preoperative 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. Methods: Patients 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. Results: The 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. Conclusion: This 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.

Indexed as

AdenomaMagnetic Resonance ImagingPituitary NeoplasmsAdultAgedFemaleHumansMaleMiddle AgedPrognosisProspective StudiesRecovery of FunctionRetrospective StudiesTreatment OutcomeVision DisordersVisual Acuityclinical practical modelgraphic segmentationmachine learning (ML)pituitary adenomavisual outcome

Identifiers

PMID39735648
PMCPMC11671264

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.