Prediction of Intraoperative CSF Leakage Associated with Transsphenoidal Pituitary Adenoma Surgery using Artificial Intelligence
Keywords:
intraoperative CSF leakage, pituitary adenoma, transsphenoidal surgery, artificial intelligenceAbstract
Objectives: This study was to using artificial intelligence for prediction of intraoperative CSF leak in transsphenoidal surgery for pituitary adenoma.
Material and Methods: Retrospective study of the pituitary adenoma patients who underwent transsphenoidal surgery in KCMH during 1/1/2015 – 31/12/2020, this study included patients. The associated intraoperative Cerebrospinal fluid (CSF) leak with demographic data, tumor characteristics, and operative techniques were analyzed. Two different algorithms were lined up for comparison of the accuracy (logistic regression and random forest model).
Result: The rate of intra operative CSF leak in the series was 24.6%. The risk factors were height of tumor, hydrocephalus, previous surgery, secretory tumor, age, radiation treatment, sex, BMI, and attempt to gross total resection. The logistic regression model was more accuracy than random forest model. (F1 score 0.43 VS 0.40)
Conclusion: This study was analyzed all the risk factors for CSF leakage after surgery via artificial intelligence. This logistic regression model presents better F1 score and offers a valuable tool for prediction of intraoperative CSF leakage after transsphenoidal surgery in pituitary tumor
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