Prediction of Recurrence after Transsphenoidal Surgery for Cushing’s Disease: The Use of Machine Learning Algorithms

Abstract

Background: There are no reliable predictive models for recurrence after transsphenoidal surgery (TSS) for Cushing’s disease (CD). Objectives: This study aimed to develop machine learning (ML)-based predictive models for CD recurrence after initial TSS and to evaluate their performance. Method: A total of 354 CD patients were included in this retrospective, supervised learning, data mining study. Predictive models for recurrence were developed according to 17 variables using 7 algorithms. Models were evaluated based on the area under the receiver operating characteristic curve (AUC). Results: All patients were followed up for over 12 months (mean ± SD 43.80 ± 35.61). The recurrence rate was 13.0%. Age (p < 0.001), postoperative morning serum cortisol nadir (p = 0.002), and postoperative (p < 0.001) and preoperative (p = 0.04) morning adrenocorticotropin (ACTH) level were significantly related to recurrence. AUCs of the 7 models ranged from 0.608 to 0.781. The best performance (AUC = 0.781, 95% CI 0.706, 0.856) appeared when 8 variables were introduced to the random forest (RF) algorithm, which was much better than that of logistic regression (AUC = 0.684, p = 0.008) and that of using only postoperative morning serum cortisol (AUC = 0.635, p < 0.001). According to the feature selection algorithms, the top 3 predictors were age, postoperative serum cortisol, and postoperative ACTH. Conclusions: Using ML-based models for prediction of the recurrence after initial TSS for CD is feasible, and RF performs best. The performance of most of ML-based models was significantly better than that of some conventional models.

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

“…The cavernous sinus invasion ( 22 ) and the tumor texture ( 2 ) were determined by the surgeon during the operation. The cavernous sinus invasion of tumors was considered to be positive if the tumor extended the cavernous sinus and a cavernous sinus defect was observed ( 23 ). Tumor that could be suctioned out using an aspirator was considered as soft, while a tumor that could not be suctioned out was considered as firm ( 2 ).…”

“…ML models usually have distinctive black box and uninterpretable characteristics, which means that the function between the features and the response is invisible to the researcher ( 23 , 31 – 33 ).…”

“…We used the following six representative supervised ML algorithms for clinical feature screening and model construction in the training dataset: logistic regression (LR), gradient boosting decision tree (GBDT), adaptive boosting (AdaBoost), extreme gradient boost (XGBoost), categorical boosting (CatBoost), and random forest (RF) (23,29). The detailed parameters of the six algorithms are presented in Supplementary Table 1.…”

“…For each iteration, a random 5-fold cross-validation was performed for training dataset based on the corresponding number of clinical features. The experiment was repeated five times, and we used a grid search approach to identify the optimal parameters for each model in the training dataset (23).…”

Recurrence after pituitary surgery in adult Cushing’s disease: a systematic review on diagnosis and treatment

“…According to a study by Liu et al using different machine learning algorithms, the most important predictors for recurrence were young age, postoperative serum cortisol, and postoperative ACTH, both measured in the first 7 days after surgery (cut-offs see Table 2) [25]. However, sensitivity and specificity using such an algorithm were quite low with 87% and 58%, respectively.…”