Machine learning-based model to predict long-term tumor control and additional interventions following pituitary surgery for Cushing's disease.

IF 3.5 2区 医学 Q1 CLINICAL NEUROLOGY
Yuki Shinya, Abdul Karim Ghaith, Sukwoo Hong, Dana Erickson, Irina Bancos, Justine S Herndon, Caroline J Davidge-Pitts, Ryan T Nguyen, Antonio Bon Nieves, Miguel Sáez Alegre, Ramin A Morshed, Carlos D Pinheiro Neto, Maria Peris Celda, Bruce E Pollock, Fredric B Meyer, John L D Atkinson, Jamie J Van Gompel
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引用次数: 0

Abstract

Objective: In this study, the authors aimed to establish a supervised machine learning (ML) model based on multiple tree-based algorithms to predict long-term biochemical outcomes and intervention-free survival (IFS) after endonasal transsphenoidal surgery (ETS) in patients with Cushing's disease (CD).

Methods: The medical records of patients who underwent ETS for CD between 2013 and 2023 were reviewed. Data were collected on the patient's baseline characteristics, intervention details, histopathology, surgical outcomes, and postoperative endocrine functions. The study's primary outcome was IFS, and the therapeutic outcomes were labeled as "under control" or "treatment failure," depending on whether additional therapeutic interventions after primary ETS were required. The decision tree and random forest classifiers were trained and tested to predict long-term IFS based on unseen data, using an 80/20 cohort split.

Results: Data from 150 patients, with a median follow-up period of 56 months, were extracted. In the cohort, 42 (28%) patients required additional intervention for persistent or recurrent CD. Consequently, the IFS rates following ETS alone were 83% at 3 years and 78% at 5 years. Multivariable Cox proportional hazards analysis demonstrated that a smaller tumor diameter that could be detected by MRI (hazard ratio 0.95, 95% CI 0.90-0.99; p = 0.047) was significantly associated with greater IFS. However, the lack of tumor detection on MRI was a poor predictor. The ML-based model using a decision tree model displayed 91% accuracy (95% CI 0.70-0.94, sensitivity 87.0%, specificity 89.0%) in predicting IFS in the unseen test dataset. Random forest analysis revealed that tumor size (mean minimal depth 1.67), Knosp grade (1.75), patient age (1.80), and BMI (1.99) were the four most significant predictors of long-term IFS.

Conclusions: The ML algorithm could predict long-term postoperative endocrinological remission in CD with high accuracy, indicating that prognosis may vary not only with previously reported factors such as tumor size, Knosp grade, gross-total resection, and patient age but also with BMI. The decision tree flowchart could potentially stratify patients with CD before ETS, allowing for the selection of personalized treatment options and thereby assisting in determining treatment plans for these patients. This ML model may lead to a deeper understanding of the complex mechanisms of CD by uncovering patterns embedded within the data.

基于机器学习的模型预测库欣病垂体手术后的长期肿瘤控制和额外干预。
目的:在这项研究中,作者旨在建立一个基于多树算法的监督机器学习(ML)模型,以预测库欣病(CD)患者经鼻蝶窦手术(ETS)后的长期生化结果和无干预生存(IFS)。方法:回顾2013年至2023年间接受ETS治疗的CD患者的医疗记录。收集患者的基线特征、干预细节、组织病理学、手术结果和术后内分泌功能的数据。该研究的主要结果是IFS,治疗结果被标记为“控制”或“治疗失败”,这取决于是否需要在初始ETS后进行额外的治疗干预。对决策树和随机森林分类器进行了训练和测试,以使用80/20队列划分来预测基于未见数据的长期IFS。结果:从150例患者中提取数据,中位随访期为56个月。在队列中,42例(28%)患者因持续性或复发性CD需要额外的干预。因此,单独使用ETS后3年的IFS率为83%,5年的IFS率为78%。多变量Cox比例风险分析显示,MRI可以检测到较小的肿瘤直径(风险比0.95,95% CI 0.90-0.99;p = 0.047)与更高的IFS显著相关。然而,MRI缺乏肿瘤检测是一个很差的预测指标。使用决策树模型的基于ml的模型在预测未见测试数据集中的IFS时显示出91%的准确性(95% CI 0.70-0.94,灵敏度87.0%,特异性89.0%)。随机森林分析显示,肿瘤大小(平均最小深度1.67)、Knosp分级(1.75)、患者年龄(1.80)和BMI(1.99)是长期IFS的四个最显著的预测因素。结论:ML算法可以高精度预测CD术后长期内分泌缓解,提示预后不仅与先前报道的肿瘤大小、Knosp分级、总切除和患者年龄等因素有关,还与BMI有关。决策树流程图可能会在ETS之前对CD患者进行分层,允许选择个性化的治疗方案,从而帮助确定这些患者的治疗计划。通过揭示数据中嵌入的模式,这个ML模型可以让我们更深入地理解CD的复杂机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of neurosurgery
Journal of neurosurgery 医学-临床神经学
CiteScore
7.20
自引率
7.30%
发文量
1003
审稿时长
1 months
期刊介绍: The Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of Neurosurgery: Pediatrics, and Neurosurgical Focus are devoted to the publication of original works relating primarily to neurosurgery, including studies in clinical neurophysiology, organic neurology, ophthalmology, radiology, pathology, and molecular biology. The Editors and Editorial Boards encourage submission of clinical and laboratory studies. Other manuscripts accepted for review include technical notes on instruments or equipment that are innovative or useful to clinicians and researchers in the field of neuroscience; papers describing unusual cases; manuscripts on historical persons or events related to neurosurgery; and in Neurosurgical Focus, occasional reviews. Letters to the Editor commenting on articles recently published in the Journal of Neurosurgery, Journal of Neurosurgery: Spine, and Journal of Neurosurgery: Pediatrics are welcome.
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