Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing’s Disease - Zhang, Wentai; Sun, Mengke; Fan, Yanghua; Wang, He; Feng, Ming; Zhou, Shaohua; Wang, Renzhi (Frontiers in Endocrinology, 2021)
Background: 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 Cushing’s disease (CD). Purpose: Our current study aims to devise and assess an ML-based model to preoperatively predict immediate remission after TSS in patients with CD. Methods: A total of 1045 participants with CD who received TSS at Peking Union Medical College Hospital in a 20-year period (February 2000 and September 2019) were enrolled in the present study, which examined their clinical characteristics and early postoperative outcomes. In total 9 ML classifiers were applied to construct models for the preoperative prediction of immediate remission. 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 the AUC. Results: The 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), morning adrenocorticotropic hormone (ACTH)(p=0.008) and disease course (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 using stacking ensemble method with 4 characteristics: first operation, cavernous sinus invasion on preoperative MRI, tumour size and preoperative ACTH. Conclusion: We developed a readily available ML-based model that is useful for the preoperative prediction of immediate remission in patients with CD.