Enhancing clinical decision-making: An externally validated machine learning model for predicting isocitrate dehydrogenase mutation in gliomas using radiomics from presurgical magnetic resonance imaging.

Autor:	Lost J; Department of Neurosurgery, Heinrich-Heine University, Dusseldorf, Germany., Ashraf N; College of Medicine, Alfaisal University, Riyadh, Saudi Arabia., Jekel L; DKFZ Division of Translational Neurooncology at the WTZ, German Cancer Consortium, DKTK Partner Site, University Hospital Essen, Essen, Germany., von Reppert M; University of Leipzig, Leipzig, Germany., Tillmanns N; Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Dusseldorf, Germany., Willms K; University of Leipzig, Leipzig, Germany., Merkaj S; University of Ulm, Ulm, Germany., Petersen GC; University of Göttingen, Göttingen, Germany., Avesta A; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA., Ramakrishnan D; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA., Omuro A; Department of Neurology and Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut, USA., Nabavizadeh A; Department of Radiology, Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, Pennsylvania, USA., Bakas S; Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA., Bousabarah K; Visage Imaging, Inc., Berlin, Germany., Lin M; Visage Imaging, Inc., San Diego, California, USA., Aneja S; Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut, USA., Sabel M, Aboian M; Department of Radiology, Children's Hospital of Philadelphia (CHOP), Philadelphia, Pennsylvania, USA.
Jazyk:	angličtina
Zdroj:	Neuro-oncology advances [Neurooncol Adv] 2024 Oct 03; Vol. 6 (1), pp. vdae157. Date of Electronic Publication: 2024 Oct 03 (Print Publication: 2024).
DOI:	10.1093/noajnl/vdae157
Abstrakt:	Background: 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. Methods: 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. Results: 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. Conclusions: 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. Competing Interests: MingDe Lin is an employee and stockholder of Visage Imaging, Inc., and unrelated to this work, receives funding from NIH/NCI R01 CA206180 and is a board member of Tau Beta Pi Engineering Honor Society. Khaled Bousabarah is an employee of Visage Imaging, GmbH. Michael Sabel is a consultant for Novocure and Codman. (© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu