[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125671-en":3,"doc-seo-125671-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125671,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",7,"Healthcare","Differentiating Radiation Necrosis and Metastatic Progression in Brain Tumors Using Radiomics and Machine Learning","Distinguishing radiation necrosis from metastatic progression in brain tumors is difficult with conventional MRI because their appearances overlap and both can follow stereotactic radiosurgery. This study develops an automated radiomics-based machine-learning approach to separate radiation necrosis from brain metastasis progression. Radiomics features are extracted from post-contrast T1-weighted MRI using multiple filtering and transform methods, then classifiers are trained and validated with a train/test split. Performance is assessed using ROC AUC, accuracy, sensitivity, and specificity, aiming to improve therapeutic decision-making without biopsy.","Downloaded from [http://journals.lww.com/amjclinicaloncology by BhDMf5ePHKav1zEoum1tQfN4a](http://journals.lww.com/amjclinicaloncology by BhDMf5ePHKav1zEoum1tQfN4a)+kJLhEZgbsIHo4XMi 0hCywCX 1AWnYQp/ I l Qr HD3i 3D0OdRyi 7TvS Fl4Cf3VC1y0abggQZXdtwnfKZBYtws= on 08/16/2023  \nORIGINAL ARTICLE  \nDifferentiating Radiation Necrosis and Metastatic Progression in Brain Tumors Using Radiomics and Machine  \nLearning  \nElahheh Salari, PhD,* Haitham Elsamaloty, MD,† Aniruddha Ray, PhD,‡§ Mersiha Hadziahmetovic, MD,* and E. Ishmael Parsai, PhD*  \nObjectives: Distinguishing between radiation necrosis (RN) and metastatic progression is extremely challenging due to their similarity in conventional imaging. This is crucial from a therapeutic point of view as this determines the outcome of the treatment. This study aims to establish an automated technique to differentiate RN from brain metastasis progression using radiomics with machine learning.  \nMethods: Eighty-six patients with brain metastasis after they underwent stereotactic radiosurgery as primary treatment were selected. Discrete wavelets transform, Laplacian-of-Gaussian, Gradient, and Square were applied to magnetic resonance post-contrast T1-weighted images to extract radiomics features. After feature selection, dataset was randomly split into train/test (80%/20%) datasets. Random forest classification, logistic regression, and support vector classification were trained and subsequently validated using test set. The classification performance was measured by area under the curve (AUC) value of receiver operating characteristic curve, accuracy, sensitivity, and specificity.  \nResults: The best performance was achieved using random forest classification with a Gradient filter (AUC = 0.910 ± 0.047, accuracy 0.8 ± 0.071, sensitivity = 0.796 ± 0.055, specificity = 0.922 ± 0.059) . For, support vector classification the best result obtains using wavelet_HHH with a high AUC of 0.890 ± 0.89, accuracy of 0.777 ± 0.062, sensitivity = 0.701 ± 0.084, and specificity = 0.85 ± 0.112. Logistic regression using wavelet_HHH provides a poor result with AUC = 0.882 ± 0.051, accuracy of 0.753 ± 0.08, sensitivity = 0.717 ± 0.208, and specificity = 0.816 ± 0.123.  \nConclusion: This type of machine-learning approach can help accurately distinguish RN from recurrence in magnetic resonance imaging, without the need for biopsy. This has the potential to improve the therapeutic outcome.  \nFrom the Departments of *Radiation Oncology; †Radiology, University of Toledo Medical Center, Sylvania; ‡Department of Physics and Astronomy, Adjunct Faculty; and §Department of Radiation Oncology, University of Toledo, Toledo, OH.  \nE.S. and H.E.: conceived and designed the analysis, collected, and contributed to the data, performed the analysis, and wrote the paper. A.R. and M.H.: conceived the analysis and wrote the paper. E.I.P.: conceived and designed the analysis, wrote the paper.  \nApproval from the Internal Review Board (IRB) of the University of Toledo (300579-UT) was acquired for this investigation on June 16, 2021 .  \nThe authors declare no conflicts of interest.  \nCorrespondence: E. Ishmael Parsai, PhD, Department of Radiation Oncology, University of Toledo Medical Center, 8874 Linden Lake Road, Sylvania, OH 43560. E-mail: [Ishmael.parsai@gmail.com](Ishmael.parsai@gmail.com).  \nCopyright © 2023 The Author(s) . Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in anyway or used commercially without permission from the journal.  \nISSN: 0277-3732/23/000-000  \nDOI: 10.1097/COC.0000000000001036  \nKey Words: brain metastasis progression, machine learning, radiation necrosis, radiomics  \n(Am J Clin Oncol 2023;00:000–000)  \nBrain metastasis is generally difficult to manage, and they  ","cbCaimcSfQB0Z4cG","https://ap.wps.com/l/cbCaimcSfQB0Z4cG","pdf",684527,1,10,"English","en",105,"# Objectives\n# Methods\n## Radiomics feature extraction\n## Model training and validation\n# Results\n# Conclusion","[{\"question\":\"Why is differentiating radiation necrosis from metastatic progression challenging on conventional MRI?\",\"answer\":\"Radiation necrosis and metastatic recurrence can show highly similar findings on standard MRI sequences due to disruption of the blood-brain barrier, making visual discrimination difficult.\"},{\"question\":\"How were radiomics features extracted for machine learning?\",\"answer\":\"Radiomics features were extracted from post-contrast T1-weighted MRI using discrete wavelet transform, Laplacian-of-Gaussian, Gradient, and Square methods applied to the images.\"},{\"question\":\"Which machine-learning model performed best in distinguishing the two conditions?\",\"answer\":\"Random forest classification achieved the best overall performance when using a Gradient filter, with the highest reported AUC and strong accuracy and specificity.\"}]","Differentiating Radiation Necrosis and Metastatic Progression in Brain Tumors Using Radiomics and Machine Learning | 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