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A brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy

OBJECTIVES: Gliomas and brain metastases (Mets) are the most common brain malignancies. The treatment strategy and clinical prognosis of patients are different, requiring accurate diagnosis of tumor types. However, the traditional radiomics diagnostic pipeline requires manual annotation and lacks in...

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Autores principales: Yu, Liheng, Yu, Zekuan, Sun, Linlin, Zhu, Li, Geng, Daoying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10576559/
https://www.ncbi.nlm.nih.gov/pubmed/37841015
http://dx.doi.org/10.3389/fmed.2023.1232496
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author Yu, Liheng
Yu, Zekuan
Sun, Linlin
Zhu, Li
Geng, Daoying
author_facet Yu, Liheng
Yu, Zekuan
Sun, Linlin
Zhu, Li
Geng, Daoying
author_sort Yu, Liheng
collection PubMed
description OBJECTIVES: Gliomas and brain metastases (Mets) are the most common brain malignancies. The treatment strategy and clinical prognosis of patients are different, requiring accurate diagnosis of tumor types. However, the traditional radiomics diagnostic pipeline requires manual annotation and lacks integrated methods for segmentation and classification. To improve the diagnosis process, a gliomas and Mets computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy on multi-center datasets was proposed. METHODS: Overall, 1,022 high-grade gliomas and 775 Mets patients’ preoperative MR images were adopted in the study, including contrast-enhanced T1-weighted (T1-CE) and T2-fluid attenuated inversion recovery (T2-flair) sequences from three hospitals. Two segmentation models trained on the gliomas and Mets datasets, respectively, were used to automatically segment tumors. Multiple radiomics features were extracted after automatic segmentation. Several machine learning classifiers were used to measure the impact of feature selection methods. A weight soft voting (RSV) model and ensemble decision strategy based on prior knowledge (EDPK) were introduced in the radiomics pipeline. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the classification performance. RESULTS: The proposed pipeline improved the diagnosis of gliomas and Mets with ACC reaching 0.8950 and AUC reaching 0.9585 after automatic lesion segmentation, which was higher than those of the traditional radiomics pipeline (ACC:0.8850, AUC:0.9450). CONCLUSION: The proposed model accurately classified gliomas and Mets patients using MRI radiomics. The novel pipeline showed great potential in diagnosing gliomas and Mets with high generalizability and interpretability.
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spelling pubmed-105765592023-10-15 A brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy Yu, Liheng Yu, Zekuan Sun, Linlin Zhu, Li Geng, Daoying Front Med (Lausanne) Medicine OBJECTIVES: Gliomas and brain metastases (Mets) are the most common brain malignancies. The treatment strategy and clinical prognosis of patients are different, requiring accurate diagnosis of tumor types. However, the traditional radiomics diagnostic pipeline requires manual annotation and lacks integrated methods for segmentation and classification. To improve the diagnosis process, a gliomas and Mets computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy on multi-center datasets was proposed. METHODS: Overall, 1,022 high-grade gliomas and 775 Mets patients’ preoperative MR images were adopted in the study, including contrast-enhanced T1-weighted (T1-CE) and T2-fluid attenuated inversion recovery (T2-flair) sequences from three hospitals. Two segmentation models trained on the gliomas and Mets datasets, respectively, were used to automatically segment tumors. Multiple radiomics features were extracted after automatic segmentation. Several machine learning classifiers were used to measure the impact of feature selection methods. A weight soft voting (RSV) model and ensemble decision strategy based on prior knowledge (EDPK) were introduced in the radiomics pipeline. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the classification performance. RESULTS: The proposed pipeline improved the diagnosis of gliomas and Mets with ACC reaching 0.8950 and AUC reaching 0.9585 after automatic lesion segmentation, which was higher than those of the traditional radiomics pipeline (ACC:0.8850, AUC:0.9450). CONCLUSION: The proposed model accurately classified gliomas and Mets patients using MRI radiomics. The novel pipeline showed great potential in diagnosing gliomas and Mets with high generalizability and interpretability. Frontiers Media S.A. 2023-09-29 /pmc/articles/PMC10576559/ /pubmed/37841015 http://dx.doi.org/10.3389/fmed.2023.1232496 Text en Copyright © 2023 Yu, Yu, Sun, Zhu and Geng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Yu, Liheng
Yu, Zekuan
Sun, Linlin
Zhu, Li
Geng, Daoying
A brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy
title A brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy
title_full A brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy
title_fullStr A brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy
title_full_unstemmed A brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy
title_short A brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy
title_sort brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10576559/
https://www.ncbi.nlm.nih.gov/pubmed/37841015
http://dx.doi.org/10.3389/fmed.2023.1232496
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