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Multicenter Study
. 2021 Oct 7:12:748725.
doi: 10.3389/fendo.2021.748725. eCollection 2021.

Machine-Learning Prediction of Postoperative Pituitary Hormonal Outcomes in Nonfunctioning Pituitary Adenomas: A Multicenter Study

Yi Fang  1   2 , He Wang  3 , Ming Feng  3 , Wentai Zhang  3 , Lei Cao  4 , Chenyu Ding  5 , Hongjie Chen  2 , Liangfeng Wei  1   2 , Shuwen Mu  1   2 , Zhijie Pei  1   2 , Jun Li  1   2 , Heng Zhang  1   2 , Renzhi Wang  3 , Shousen Wang  1   2
Affiliations
Multicenter Study

Machine-Learning Prediction of Postoperative Pituitary Hormonal Outcomes in Nonfunctioning Pituitary Adenomas: A Multicenter Study

Yi Fang et al. Front Endocrinol (Lausanne). .

Abstract

Objective: No accurate predictive models were identified for hormonal prognosis in non-functioning pituitary adenoma (NFPA). This study aimed to develop machine learning (ML) models to facilitate the prognostic assessment of pituitary hormonal outcomes after surgery.

Methods: A total of 215 male patients with NFPA, who underwent surgery in four medical centers from 2015 to 2021, were retrospectively reviewed. The data were pooled after heterogeneity assessment, and they were randomly divided into training and testing sets (172:43). Six ML models and logistic regression models were developed using six anterior pituitary hormones.

Results: Only thyroid-stimulating hormone (p < 0.001), follicle-stimulating hormone (p < 0.001), and prolactin (PRL; p < 0.001) decreased significantly following surgery, whereas growth hormone (GH) (p < 0.001) increased significantly. The postoperative GH (p = 0.07) levels were slightly higher in patients with gross total resection, but the PRL (p = 0.03) level was significantly lower than that in patients with subtotal resection. The optimal model achieved area-under-the-receiver-operating-characteristic-curve values of 0.82, 0.74, and 0.85 in predicting hormonal hypofunction, new deficiency, and hormonal recovery following surgery, respectively. According to feature importance analyses, the preoperative levels of the same type and other hormones were all important in predicting postoperative individual hormonal hypofunction.

Conclusion: Fluctuation in anterior pituitary hormones varies with increases and decreases because of transsphenoidal surgery. The ML models could accurately predict postoperative pituitary outcomes based on preoperative anterior pituitary hormones in NFPA.

Keywords: hypopituitarism; machine learning; neuroendocrine tumor; pituitary tumors; prognosis; surgery.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Receiver operating characteristic curves and precision-recall curves of machine learning for the postoperative outcomes of the adenohypophysis. (A) Postoperative hormonal hypofunction; (B) New hormonal hypofunction; (C) Hormonal recovery. Decision tree (DT), Random Forest (RF), K-nearest neighbor (KNN), Adaptive Boosting algorithm (AdaBoost), Support Vector Machine (SVM), Neural Network (NN), logistic regression (Logit).
Figure 2
Figure 2
The machine learning models prediction of individual hormonal deficiency and feature importance analysis. (A) TSH hypofunction; (B) FSH hypofunction; (C) LH hypofunction; (D) PRL hypofunction; (E) ACTH hypofunction.
See this image and copyright information in PMC

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