Development and validation of a machine learning model based on interpretable clinical characteristics for preoperative prediction of Ki-67 expression in pituitary adenomas
Key Points
Prediction of ki-67 expression is possible through a machine learning model, which enhances preoperative assessments.
The model achieved an accuracy rate of 85% in validation tests, indicating its potential usefulness in clinical settings.
Validation of the predictive model was based on interpretable clinical characteristics relevant to pituitary adenomas.
These findings may improve surgical decision-making and patient outcomes in those with pituitary adenomas.
Development and validation of a machine learning model based on interpretable clinical characteristics for preoperative prediction of Ki-67 expression in pituitary adenomas | Synapse
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