Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing's Disease.

Wentai Zhang,He Wang,Renzhi Wang,Mengke Sun,Ming Feng,Shaohua Zhou,Yanghua Fan

doi:10.3389/fendo.2021.635795

Abstract

BackgroundThere are no established accurate models that use machine learning (ML) methods to preoperatively predict immediate remission after transsphenoidal surgery (TSS) in patients diagnosed with histology-positive Cushing’s disease (CD).PurposeOur current study aims to devise and assess an ML-based model to preoperatively predict immediate remission after TSS in patients with CD.MethodsA total of 1,045 participants with CD who received TSS at Peking Union Medical College Hospital in a 20-year period (between February 2000 and September 2019) were enrolled in the present study. In total nine ML classifiers were applied to construct models for the preoperative prediction of immediate remission with preoperative factors. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the models. The performance of each ML-based model was evaluated in terms of AUC.ResultsThe overall immediate remission rate was 73.3% (766/1045). First operation (p<0.001), cavernous sinus invasion on preoperative MRI(p<0.001), tumour size (p<0.001), preoperative ACTH (p=0.008), and disease duration (p=0.010) were significantly related to immediate remission on logistic univariate analysis. The AUCs of the models ranged between 0.664 and 0.743. The highest AUC, i.e., the best performance, was 0.743, which was achieved by stacking ensemble method with four factors: first operation, cavernous sinus invasion on preoperative MRI, tumour size and preoperative ACTH.ConclusionWe developed a readily available ML-based model for the preoperative prediction of immediate remission in patients with CD.

Highlights

Pituitary adrenocorticotropic hormone (ACTH) hypersecretion, known as Cushing’s disease (CD), is one of the aetiologies of Cushing syndrome(CS), causing a variety of manifestations such as fatigue, weight gain, osteoporosis, diabetes mellitus, thin skin, and ecchymoses [1]
The highest area under the curve (AUC), i.e., the best performance, was 0.743, which was achieved by stacking ensemble method with four factors: first operation, cavernous sinus invasion on preoperative magnetic resonance imaging (MRI), tumour size and preoperative ACTH
In total 1,045 participants treated for CD from February 2000 to September 2019 were enrolled in this study

Summary

Introduction

Pituitary adrenocorticotropic hormone (ACTH) hypersecretion, known as Cushing’s disease (CD), is one of the aetiologies of Cushing syndrome(CS), causing a variety of manifestations such as fatigue, weight gain, osteoporosis, diabetes mellitus, thin skin, and ecchymoses [1]. According to a consensus statement, the first-line treatment option for CD is transsphenoidal surgery (TSS) [2]. While several studies have revealed that postoperative hypocortisolaemia [6,7,8],low urinary free cortisol level [9] and low ACTH levels [10] are consistent with an increased chance of longterm remission for patients with CD, there are still no accurate models for the preoperative prediction of immediate remission. There are no established accurate models that use machine learning (ML) methods to preoperatively predict immediate remission after transsphenoidal surgery (TSS) in patients diagnosed with histology-positive Cushing’s disease (CD).

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