Machine learning-based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: Is the future coming?

Matteo Zoli, Victor E. Staartjes, Federica Guaraldi, Filippo Friso, Arianna Rustici, Sofia Asioli, Giacomo Solli, Ernesto Pasquini, Luca Regli, Carlo Serra, Diego Mazzatenta

Research output: Contribution to journalArticlepeer-review

Abstract

OBJECTIVE Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing disease (CD). METHODS All consecutive patients in our center who underwent surgery for CD through the endoscopic endonasal approach were retrospectively reviewed. Study endpoints were gross-tumor removal (GTR), postsurgical remission, and long-term control of disease. Several demographic, radiological, and histological factors were assessed as potential predictors. For ML-based modeling, data were randomly divided into 2 sets with an 80% to 20% ratio for bootstrapped training and testing, respectively. Several algorithms were tested and tuned for the area under the curve (AUC). RESULTS The study included 151 patients. GTR was achieved in 137 patients (91%), and postsurgical hypersecretion remission was achieved in 133 patients (88%). At last follow-up, 116 patients (77%) were still in remission after surgery and in 21 patients (14%), CD was controlled with complementary treatment (overall, of 131 cases, 87% were under control at follow-up). At internal validation, the endpoints were predicted with AUCs of 0.81-1.00, accuracy of 81%-100%, and Brier scores of 0.035-0.151. Tumor size and invasiveness and histological confirmation of adrenocorticotropic hormone (ACTH)-secreting cells were the main predictors for the 3 endpoints of interest. CONCLUSIONS ML algorithms were used to train and internally validate robust models for all the endpoints, giving accurate outcome predictions in CD cases. This analytical method seems promising for potentially improving future patient care and counseling; however, careful clinical interpretation of the results remains necessary before any clinical adoption of ML. Moreover, further studies and increased sample sizes are definitely required before the widespread adoption of ML to the study of CD.

Original languageEnglish
Article numberE5
JournalNeurosurgical Focus
Volume48
Issue number6
DOIs
Publication statusPublished - Jun 1 2020

Keywords

  • ACTH-secreting tumor
  • Cushing disease
  • Endoscopic endonasal surgery
  • Machine learning
  • Outcome
  • Predictors

ASJC Scopus subject areas

  • Surgery
  • Clinical Neurology

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