Abstract
Preoperative prediction of visual recovery after pituitary adenoma surgery remains a challenge. We aimed to investigate the value of MRI-based radiomics of the optic chiasm in predicting postoperative visual field outcome using machine learning technology. A total of 131 pituitary adenoma patients were retrospectively enrolled and divided into the recovery group (N = 79) and the non-recovery group (N = 52) according to visual field outcome following surgical chiasmal decompression. Radiomic features were extracted from the optic chiasm on preoperative coronal T2-weighted imaging. Least absolute shrinkage and selection operator regression were first used to select optimal features. Then, three machine learning algorithms were employed to develop radiomic models to predict visual recovery, including support vector machine (SVM), random forest and linear discriminant analysis. The prognostic performances of models were evaluated via five-fold cross-validation. The results showed that radiomic models using different machine learning algorithms all achieved area under the curve (AUC) over 0.750. The SVM-based model represented the best predictive performance for visual field recovery, with the highest AUC of 0.824. In conclusion, machine learning-based radiomics of the optic chiasm on routine MR imaging could potentially serve as a novel approach to preoperatively predict visual recovery and allow personalized counseling for individual pituitary adenoma patients.
Highlights
Pituitary adenoma is one of the most common tumors in the sellar region and commonly presents with visual disturbance due to chiasmal compression and subsequent axonal injury
visual field (VF) recovery after decompression surgery is an important concern to clinicians, as the recovery extent varies across different patients with pituitary adenomas
Despite several factors having been investigated in previous studies, preoperative prediction of visual recovery remains a challenge [1]
Summary
Pituitary adenoma is one of the most common tumors in the sellar region and commonly presents with visual disturbance due to chiasmal compression and subsequent axonal injury. Compression of the optic chiasm usually first leads to visual field (VF) defects, classically manifesting as bitemporal hemianopia [1]. Surgical resection is the predominant approach to decompress the optic chiasm and alleviate visual dysfunction, with varied degrees of visual recovery after surgery [2]. Various factors have been reported to be associated with postoperative vision recovery, including patient age, tumor size, duration of symptoms, preoperative visual function, and retinal nerve fiber layer (RNFL) thickness [3,4,5]. The identification of novel markers in predicting the postoperative visual outcome is clinically valuable
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