A machine learning approach to predict early outcomes after pituitary adenoma surgery

Abstract

<jats:sec><jats:title>OBJECTIVE</jats:title><jats:p>Pituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. The authors sought to build a predictive model using supervised machine learning to accurately predict early outcomes of pituitary adenoma surgery.</jats:p></jats:sec><jats:sec><jats:title>METHODS</jats:title><jats:p>A retrospective cohort of 400 consecutive pituitary adenoma patients was used. Patient variables/predictive features were limited to common patient characteristics to improve model implementation. Univariate and multivariate odds ratio analysis was performed to identify individual risk factors for common postoperative complications and to compare risk factors with model predictors. The study population was split into 300 training/validation patients and 100 testing patients to train and evaluate four machine learning models using binary classification accuracy for predicting early outcomes.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>The study included a total of 400 patients. The mean ± SD patient age was 53.9 ± 16.3 years, 59.8% of patients had nonfunctioning adenomas and 84.7% had macroadenomas, and the mean body mass index (BMI) was 32.6 ± 7.8 (58.0% obesity rate). Multivariate odds ratio analysis demonstrated that age &lt; 40 years was associated with a 2.86 greater odds of postoperative diabetes insipidus and that nonobese patients (BMI &lt; 30) were 2.2 times more likely to develop postoperative hyponatremia. Using broad criteria for a poor early postoperative outcome—major medical and early surgical complications, extended length of stay, emergency department admission, inpatient readmission, and death—31.0% of patients met criteria for a poor early outcome. After model training, a logistic regression model with elastic net (LR-EN) regularization best predicted early postoperative outcomes of pituitary adenoma surgery on the 100-patient testing set—sensitivity 68.0%, specificity 93.3%, overall accuracy 87.0%. The receiver operating characteristic and precision-recall curves for the LR-EN model had areas under the curve of 82.7 and 69.5, respectively. The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing’s disease.</jats:p></jats:sec><jats:sec><jats:title>CONCLUSIONS</jats:title><jats:p>Early postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.</jats:p></jats:sec>

A systematic review on machine learning in sellar region diseases: quality and reporting items

“…Three studies used the same image database such that only the latest published study was included. At last, this systematic review included 16 studies (6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21) (Table 2), with the diagnosis of general pituitary neoplasms, acromegaly, Cushing’s disease, craniopharyngioma and growth hormone deficiency. More than half of the studies were published in the recent 2 years.…”

“…Sample size in these studies varied from tens to thousands. The majority of the studies (6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20) (76.5%) used the diagnosis of a specific disease as the outcome, only four studies (17, 18, 19, 21) tested on the treatment outcome. In the diagnostic studies, three studies (7, 12, 14) used image features to categorize magnetic resonance images (MRIs), two (6, 13) used face photos to predict acromegaly, two (15, 20) predicted growth home deficiency using serum proteins, two (10, 11) used histological spectrum to predict histology diagnosis, one (9) used serum proteins to predict pituitary adenoma and one (8) predicted surgical phase using videos.…”

“…In the diagnostic studies, three studies (7, 12, 14) used image features to categorize magnetic resonance images (MRIs), two (6, 13) used face photos to predict acromegaly, two (15, 20) predicted growth home deficiency using serum proteins, two (10, 11) used histological spectrum to predict histology diagnosis, one (9) used serum proteins to predict pituitary adenoma and one (8) predicted surgical phase using videos. In studies on treatment outcomes, one study (17) predicted poor early postoperative outcome, one (18) predicted gross-total resection, one (19) predicted response to somatostatin analogs and one (21) predicted growth after growth hormone treatment. All the outcomes were either dichotomized or categorical outcomes except one in the continuous form (21).…”

“…One study (13) chose ensemble models by combining the decisions from multiple models to improve the overall performance. One study (17) compared several models and chose the one with the best performance. With regard to validation methods, five studies (8, 9, 12, 15, 19) used k-fold cross-validation, two studies (14, 20) used bootstrap and three studies (6, 7, 10) used leave-one-out cross-validation.…”

Artificial Intelligence in Neuroanesthesiology and Neurocritical Care

“…44 ML has been used to predict outcome in intracranial aneurysms treated with flow diverters. 45 Hollon et al 46 sought to build a predictive model using supervised ML to accurately predict early outcomes of pituitary adenoma surgery. These results provide insight into how predictive modeling using ML can be used to improve the perioperative management of pituitary adenoma patients.…”