Shinya Yuki, Ghaith Abdul Karim, Hong Sukwoo, Herndon Justine S, Palit Sandhya R, Erickson Dana, Bancos Irina, Saez-Alegre Miguel, Morshed Ramin A, Pinheiro Neto Carlos, Meyer Fredric B, Atkinson John L D, Van Gompel Jamie J
1Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota.
2Department of Neurosurgery, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan.
Neurosurg Focus. 2025 Jul 1;59(1):E10. doi: 10.3171/2025.4.FOCUS2597.
Patients with growth hormone (GH)-secreting pituitary adenomas (PAs) experience various symptoms and comorbidities, which can ultimately lead to increased mortality. This study aimed to develop and validate a machine learning (ML) model for predicting long-term outcomes in patients with GH-secreting PAs following endonasal transsphenoidal surgery (ETS).
The authors conducted a retrospective three-institution cohort study that included patients with GH-secreting PAs treated with ETS between 2013 and 2023. Clinical, radiological, and biochemical data were collected. The main outcome of interest was the intervention-free rate (IFR) after primary ETS. Supervised ML algorithms, including decision trees and random forests, were developed to predict the IFR. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC) and Shapley Additive Explanations (SHAP) values.
The median follow-up for 100 patients with GH-secreting PAs (53% female) was 64 months (range 1-130 months). Additional intervention for persistent or recurrent acromegaly was required in 32% of patients. Following primary ETS alone, the 3-year IFR was 70% and the 5-year IFR was 67%. Multiple ML models were developed and evaluated using AUROCs. The decision tree analysis achieved an accuracy of 81% and emphasized the importance of both gross-total resection (GTR) and patient age in determining the long-term IFR. To better understand the factors that contributed to model performance, SHAP analysis was applied to the best-performing model. The SHAP dependence plots showed that key factors associated with a longer IFR included tumor size < 9 mm, GTR, patient age > 65 years, and Knosp grade 0.
This ML model offers a more nuanced and potentially more accurate approach to identify patients more likely to develop recurrent or persistent acromegaly following primary ETS and require additional treatment. Following external validation, this ML model could improve personalized treatment planning and follow-up strategies and enhance patient care and resource allocation in clinical practice.
生长激素（GH）分泌型垂体腺瘤（PA）患者会出现各种症状和合并症，最终可能导致死亡率增加。本研究旨在开发并验证一种机器学习（ML）模型，用于预测经鼻蝶窦手术（ETS）后GH分泌型PA患者的长期预后。
作者进行了一项回顾性三机构队列研究，纳入了2013年至2023年间接受ETS治疗的GH分泌型PA患者。收集了临床、放射学和生化数据。主要关注的结局是初次ETS后的无干预率（IFR）。开发了包括决策树和随机森林在内的监督ML算法来预测IFR。使用受试者操作特征曲线下面积（AUROC）和Shapley值（SHAP）评估模型性能。
100例GH分泌型PA患者（53%为女性）的中位随访时间为64个月（范围1 - 130个月）。32%的患者需要对持续性或复发性肢端肥大症进行额外干预。仅初次ETS后，3年IFR为70%，5年IFR为67%。使用AUROC开发并评估了多个ML模型。决策树分析的准确率为81%，并强调了全切（GTR）和患者年龄在确定长期IFR中的重要性。为了更好地理解影响模型性能的因素，将SHAP分析应用于表现最佳的模型。SHAP依赖图显示，与较长IFR相关的关键因素包括肿瘤大小<9 mm、GTR、患者年龄>65岁和Knosp分级0。
这种ML模型为识别初次ETS后更可能发生复发性或持续性肢端肥大症并需要额外治疗的患者提供了一种更细致入微且可能更准确的方法。经过外部验证后，这种ML模型可以改善个性化治疗规划和随访策略，并在临床实践中加强患者护理和资源分配。
