Machine Learning and Statistical Analysis of Biochemical Remission Predictors in Somatotroph Adenoma Resections

 

Introduction: There is persistent debate in the literature surrounding the true predictors of biochemical remission after surgical resection of somatotroph adenomas. A multimodal analysis of a large number of patients is needed to better understand which patients may be at higher or lower risk for remission failure after surgery.

Methods: A retrospective review was performed on patients undergoing transsphenoidal surgery for somatotroph adenomas at a single high-volume academic pituitary center from 2012 to 2019. Biochemical remission was defined as normalization of age and sex-adjusted serum IGF-1 levels at least six months after surgery. Covariates examined included patient sex, age at surgery, tumor size and cavernous sinus invasion, number of prior pituitary surgeries, preoperative serum growth hormone (GH) and IGF-1, extent of resection, postoperative tumor size, and immediate postoperative GH and IGF-1. Tumor size was defined both as the largest of 3-axis measurements and as a 3D volumetric recording from pre and postoperative imaging studies. Preoperative variables’ effects on remission status were examined by Pearson’s chi-squared tests, Wilcoxon rank sum tests, and Fisher’s exact tests, where appropriate. Pre- and postoperative variables were included in a Random Forest machine learning model to assess for their importance in determining remission status. Preoperative variables found to be significant remission predictors on statistical testing and important in the Random Forest model were subsequently assessed via receiver operating characteristic (ROC) analysis to determine numeric thresholds that optimally predicted preoperative likelihood of remission success or failure.

Results: A total of 97 patients were identified with somatotroph adenomas who underwent transsphenoidal resection, with 78 patients (80%) achieving biochemical remission. Statistical testing found that patients who failed remission were more likely to have larger tumors (1.9 vs. 1.6 cm by largest axis, p = 0.031, and 3.61 vs. 2.57 cm³ by 3D volume, p = 0.009) that invaded the cavernous sinus more frequently (68% vs. 27%, p < 0.001), and have higher preoperative GH (24.3 vs. 18.7 ng/mL, p = 0.041) and IGF-1 (878 vs. 670 ng/mL, p = 0.045). A random forest machine learning model with 10,000 iterations found 3D tumor volume (mean decrease in model accuracy 2.3) and preoperative IGF-1 (1.85 decrease) to be important preoperative predictors in determining remission status. ROC analysis revealed that among patients with biochemical remission, 83.3% had preoperative GH levels below 8.21 ng/mL (AUC 0.695, p = 0.012), 94.7% had preoperative 3D tumor volume less than 1.51 cm³ (AUC 0.696, p = 0.001; [Fig. 1]), and 100% had preoperative IGF-1 less than 718.5 ng/mL (AUC=0.742, p = 0.001).

Conclusions: Important preoperative predictors of postoperative remission for somatotroph adenoma resections include serum HGH, serum IGF-1, cavernous sinus invasion, and tumor size. 95% of patients who achieve post-op remission have pre-op 3D tumor volumes less than 1.51 cm³.

Publication History

Article published online:
05 February 2024

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