The State of Machine Learning in Outcomes Prediction of Transsphenoidal Surgery: A Systematic Review

Darrion Bo Yun Yang, Alexander Smith, Emily J. Smith, Anant Naik, Mika Janbahan, Charee Mooney Thompson, Lav R. Varshney, Wael Hassaneen

Research output: Contribution to journalReview articlepeer-review

Abstract

The purpose of this analysis is to assess the use of machine learning (ML) algorithms in the prediction of post-operative outcomes, including complications, recurrence, and death in transsphenoidal surgery. Following PRISMA guidelines, we systematically reviewed all papers that used at least one ML algorithm to predict outcomes after transsphenoidal surgery. We searched Scopus, PubMed, and Web of Science databases for studies published prior to May 12th, 2021. We identified 13 studies enrolling 5048 patients. We extracted the general characteristics of each study; the sensitivity, specificity, AUC of the ML models developed as well as the features identified as important by the ML models. We identified 12 studies with 5048 patients that included ML algorithms for adenomas, three with 1807 patients specifically for acromegaly, and five with 2105 patients specifically for Cushing's disease. Nearly all were single institution studies. The studies used a heterogeneous mix of ML algorithms and features to build predictive models. All papers reported an AUC greater than.7, which indicates clinical utility. ML algorithms have the potential to predict post-operative outcomes of transsphenoidal surgery and can improve patient care. Ensemble algorithms and neural networks were often top performers when compared to other ML algorithms. Biochemical and pre-operative features were most likely to be selected as important by ML models. Inexplicability remains a challenge, but algorithms such as local interpretable model-agnostic explanation or Shapley value can increase explainability of ML algorithms. Our analysis shows that ML algorithms have the potential to greatly assist surgeons in clinical decision making.

Original languageEnglish (US)
JournalJournal of Neurological Surgery, Part B: Skull Base
DOIs
StateAccepted/In press - 2021

Keywords

  • acromegaly
  • artificial intelligence
  • cushing's disease
  • machine learning
  • pituitary adenomas
  • transsphenoidal surgery

ASJC Scopus subject areas

  • Clinical Neurology

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