Abstract
Background: Some patients with acromegaly do not reach the remission standard in the short term after surgery but achieve remission without additional postoperative treatment during long-term follow-up; this phenomenon is defined as postoperative delayed remission (DR). DR may complicate the interpretation of surgical outcomes in patients with acromegaly and interfere with decision-making regarding postoperative adjuvant therapy.Objective: We aimed to develop and validate machine learning (ML) models for predicting DR in acromegaly patients who have not achieved remission within 6 months of surgery.Methods: We enrolled 306 acromegaly patients and randomly divided them into training and test datasets. We used the recursive feature elimination (RFE) algorithm to select features and applied six ML algorithms to construct DR prediction models. The performance of these ML models was validated using receiver operating characteristics analysis. We used permutation importance, SHapley Additive exPlanations (SHAP), and local interpretable model–agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models.Results: Fifty-five (17.97%) acromegaly patients met the criteria for DR, and five features (post-1w rGH, post-1w nGH, post-6m rGH, post-6m IGF-1, and post-6m nGH) were significantly associated with DR in both the training and the test datasets. After the RFE feature selection, the XGboost model, which comprised the 15 important features, had the greatest discriminatory ability (area under the curve = 0.8349, sensitivity = 0.8889, Youden's index = 0.6842). The XGboost model showed good discrimination ability and provided significantly better estimates of DR of patients with acromegaly compared with using only the Knosp grade. The results obtained from permutation importance, SHAP, and LIME algorithms showed that post-6m IGF-1 is the most important feature in XGboost algorithm prediction and showed the reliability and the clinical practicability of the XGboost model in DR prediction.Conclusions: ML-based models can serve as an effective non-invasive approach to predicting DR and could aid in determining individual treatment and follow-up strategies for acromegaly patients who have not achieved remission within 6 months of surgery.
Highlights
Is a chronic endocrine disease that is mostly caused by growth hormone (GH)-secreting pituitary adenomas (PAs), resulting in excessive circulating levels of insulin-like growth factor 1 (IGF1) and in high morbidity and mortality [1, 2]
Previous studies have focused on the retrospective analysis of clinical risk factors and their associations with delayed remission, and the results have revealed that postoperative 3-month IGF1 levels might have a significant influence on delayed remission [6]
As shown in the Endocrine Society Clinical Practice Guideline on acromegaly [3], the preoperative diagnostic criteria for acromegaly are as follows: [1] adult patients with clinical symptoms of acromegaly [3], [2] PA confirmed by pituitary magnetic resonance imaging (MRI), and [3] preoperative IGF1 values exceeding the upper limit of the age- and the gender-related reference range [19] and lack of suppression of GH to
Summary
Is a chronic endocrine disease that is mostly caused by growth hormone (GH)-secreting pituitary adenomas (PAs), resulting in excessive circulating levels of insulin-like growth factor 1 (IGF1) and in high morbidity and mortality [1, 2]. The remission of acromegaly needs to meet the following two conditions at least 12 weeks after surgery: normalized levels of IGF1 and a random GH level of
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