Machine learning-based clinical outcome prediction in surgery for acromegaly

Olivier Zanier,Matteo Zoli,Luca Regli,Marco Faustini-Fustini,Zoran Erlic,Valentino Marino Picciola,Sofia Asioli,Ernesto Pasquini,Carlo Serra,Federica Guaraldi,Diego Mazzatenta,Arianna Rustici,Victor E Staartjes

doi:10.1007/s12020-021-02890-z

Abstract

PurposeBiochemical remission (BR), gross total resection (GTR), and intraoperative cerebrospinal fluid (CSF) leaks are important metrics in transsphenoidal surgery for acromegaly, and prediction of their likelihood using machine learning would be clinically advantageous. We aim to develop and externally validate clinical prediction models for outcomes after transsphenoidal surgery for acromegaly.MethodsUsing data from two registries, we develop and externally validate machine learning models for GTR, BR, and CSF leaks after endoscopic transsphenoidal surgery in acromegalic patients. For the model development a registry from Bologna, Italy was used. External validation was then performed using data from Zurich, Switzerland. Gender, age, prior surgery, as well as Hardy and Knosp classification were used as input features. Discrimination and calibration metrics were assessed.ResultsThe derivation cohort consisted of 307 patients (43.3% male; mean [SD] age, 47.2 [12.7] years). GTR was achieved in 226 (73.6%) and BR in 245 (79.8%) patients. In the external validation cohort with 46 patients, 31 (75.6%) achieved GTR and 31 (77.5%) achieved BR. Area under the curve (AUC) at external validation was 0.75 (95% confidence interval: 0.59–0.88) for GTR, 0.63 (0.40–0.82) for BR, as well as 0.77 (0.62–0.91) for intraoperative CSF leaks. While prior surgery was the most important variable for prediction of GTR, age, and Hardy grading contributed most to the predictions of BR and CSF leaks, respectively.ConclusionsGross total resection, biochemical remission, and CSF leaks remain hard to predict, but machine learning offers potential in helping to tailor surgical therapy. We demonstrate the feasibility of developing and externally validating clinical prediction models for these outcomes after surgery for acromegaly and lay the groundwork for development of a multicenter model with more robust generalization.

Highlights

These authors contributed : Olivier Zanier, Matteo ZoliThese authors jointly supervised this work: D
The more factors that come into play, the harder it gets for clinicians to take them and their interactions into account
Biochemical remission (BR) was defined as postoperative HGH level random or after oral glucose tolerance test

Summary

Introduction

These authors contributed : Olivier Zanier, Matteo Zoli. These authors jointly supervised this work: D. A GH-secreting pituitary tumor is the cause of acromegaly in more than 95% of patients and surgical treatment remains the first-line therapy in most cases [2]. The more factors that come into play, the harder it gets for clinicians to take them and their interactions into account. Based on these patient features, machine learning (ML) can be implemented to tailor treatment to a patient’s individual characteristics in the era of “personalized medicine” [6]. It has become evident that ML has strong potential for outcome prediction and sometimes even outperforms statistical modeling techniques [7, 8]

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