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Machine learning-based preoperative predictive analytics in transsphenoidal surgery for Cushing´s disease: can we predict the future?
EANS Academy. Staartjes V. Sep 27, 2019; 276011; EP04068
Mr. Victor E. Staartjes
Mr. Victor E. Staartjes

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Abstract
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Background: Although postoperative morbidity and mortality have become relatively low in transnasal transsphenoidal surgery (TSS) for pituitary adenomas, long-term prognosis remains difficult. Especially in Cushing's disease, where residual tumor and hormonal endpoints - both hard to predict - have been demonstrably linked to residual morbidity and mortality, the ability to pre- and perioperatively predict such outcomes with high reliability would be valuable in patient counseling. We aimed to apply machine learning algorithms to predict four relevant endpoints after surgery for Cushing's disease.
Methods: Endpoints were biochemical remission, GTR, recurrence, and achievement of normosecretion. A range of demographic and disease-specific inputs were collected, including tumor size, Hardy, Wilson, and Knosp grading, tumor size, preoperative hormonal function, prior treatments, histology, and immediate postoperative hormonal status. Factors univariately associated with functional impairment were identified. For machine learning-based modelling, data was randomly split into two sets in a 80%/20% ratio for bootstrapped training and testing, respectively. We trialed seven algorithms and tuned for AUC.
Results: We included 151 patients, of which 146 (97%) achieved biochemical remission, 107 (71%) achieved normosecretion, 137 (91%) underwent GTR, and 26 (17%) experienced recurrence. The four final models were based on support vector machines, random forests, and neural networks. At internal validation, bochemical remission, normosecretion, and GTR were predicted with AUCs of 0.68-1.0, accuracy of 74.2%-100%, and Brier scores of 0.035-0.177. However, recurrence was only predictable with AUC of 0.576, accuracy of 74.2%, and Brier score of 0.250. No reliable predictors of endocrinological outcome and recurrence with relevant effect size were identified using traditional interferential statistics.
Conclusions: We trained and internally validated robust machine learning models that identify clinically relevant endpoints in pituitary surgery. Machine learning algorithms may predict outcomes and adverse events that were previously near unpredictable, thus enabling safer and improved patient care and better patient counseling.
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