Lost Jan, Ashraf Nader, Jekel Leon, von Reppert Marc, Tillmanns Niklas, Willms Klara, Merkaj Sara, Petersen Gabriel Cassinelli, Avesta Arman, Ramakrishnan Divya, Omuro Antonio, Nabavizadeh Ali, Bakas Spyridon, Bousabarah Khaled, Lin MingDe, Aneja Sanjay, Sabel Michael, Aboian Mariam
Department of Neurosurgery, Heinrich-Heine University, Dusseldorf, Germany.
College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Neurooncol Adv. 2024 Oct 3;6(1):vdae157. doi: 10.1093/noajnl/vdae157. eCollection 2024 Jan-Dec.
Glioma, the most prevalent primary brain tumor, poses challenges in prognosis, particularly in the high-grade subclass, despite advanced treatments. The recent shift in tumor classification underscores the crucial role of isocitrate dehydrogenase (IDH) mutation status in the clinical care of glioma patients. However, conventional methods for determining IDH status, including biopsy, have limitations. Exploring the use of machine learning (ML) on magnetic resonance imaging to predict IDH mutation status shows promise but encounters challenges in generalizability and translation into clinical practice because most studies either use single institution or homogeneous datasets for model training and validation. Our study aims to bridge this gap by using multi-institution data for model validation.
This retrospective study utilizes data from large, annotated datasets for internal (377 cases from Yale New Haven Hospitals) and external validation (207 cases from facilities outside Yale New Haven Health). The 6-step research process includes image acquisition, semi-automated tumor segmentation, feature extraction, model building with feature selection, internal validation, and external validation. An extreme gradient boosting ML model predicted the IDH mutation status, confirmed by immunohistochemistry.
The ML model demonstrated high performance, with an Area under the Curve (AUC), Accuracy, Sensitivity, and Specificity in internal validation of 0.862, 0.865, 0.885, and 0.713, and external validation of 0.835, 0.851, 0.850, and 0.847.
The ML model, built on a heterogeneous dataset, provided robust results in external validation for the prediction task, emphasizing its potential clinical utility. Future research should explore expanding its applicability and validation in diverse global healthcare settings.
胶质瘤是最常见的原发性脑肿瘤，尽管有先进的治疗方法，但在预后方面仍面临挑战，尤其是高级别亚类。最近肿瘤分类的转变凸显了异柠檬酸脱氢酶（IDH）突变状态在胶质瘤患者临床护理中的关键作用。然而，包括活检在内的传统IDH状态检测方法存在局限性。探索利用磁共振成像上的机器学习（ML）来预测IDH突变状态显示出前景，但在可推广性和转化为临床实践方面面临挑战，因为大多数研究要么使用单一机构的数据，要么使用同质数据集进行模型训练和验证。我们的研究旨在通过使用多机构数据进行模型验证来弥合这一差距。
这项回顾性研究利用来自大型注释数据集的数据进行内部验证（耶鲁纽黑文医院的377例病例）和外部验证（耶鲁纽黑文健康系统以外机构的207例病例）。六步研究过程包括图像采集、半自动肿瘤分割、特征提取、基于特征选择的模型构建、内部验证和外部验证。一个极端梯度提升ML模型预测IDH突变状态，并通过免疫组织化学进行确认。
ML模型表现出高性能，内部验证中的曲线下面积（AUC）、准确率、灵敏度和特异性分别为0.862、0.865、0.885和0.713，外部验证中的相应指标分别为0.835、0.851、0.850和0.847。
基于异质数据集构建的ML模型在预测任务的外部验证中提供了可靠的结果，强调了其潜在的临床应用价值。未来的研究应探索在不同的全球医疗环境中扩大其适用性和验证范围。
