Deep learning-based survival prediction of brain tumor patients using attention-guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging

Mazher, Moona; Qayyum, Abdul; Puig, Domenec; Abdel-Nasser, Mohamed

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Deep learning-based survival prediction of brain tumor patients using attention-guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging - imarina:9366619

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https://hdl.handle.net/20.500.11797/imarina9366619

Autor/es de la URV:	Abdelnasser Mohamed Mahmoud, Mohamed / Mazher, Moona / Puig Valls, Domènec Savi
Autor según el artículo:	Mazher, Moona; Qayyum, Abdul; Puig, Domenec; Abdel-Nasser, Mohamed
Direcció de correo del autor:	mohamed.abdelnasser@urv.cat moona.mazher@estudiants.urv.cat domenec.puig@urv.cat
Identificador del autor:	0000-0002-1074-2441 0000-0003-4444-5776 0000-0002-0562-4205
Año de publicación de la revista:	2024
Tipo de publicación:	Journal Publications
Referencia de l'ítem segons les normes APA:	Mazher, Moona; Qayyum, Abdul; Puig, Domenec; Abdel-Nasser, Mohamed (2024). Deep learning-based survival prediction of brain tumor patients using attention-guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging. International Journal Of Imaging Systems And Technology, 34(1), -. DOI: 10.1002/ima.23010
Referencia al articulo segun fuente origial:	International Journal Of Imaging Systems And Technology. 34 (1):
Resumen:	Automatic survival prediction of gliomas from brain magnetic resonance imaging (MRI) volumes is an essential step for a patient's prognosis analysis. Radiomics research delivers beneficial feature information from MRI imaging which is substantially required by clinicians and oncologists for predicting disease prognosis for precise surgical treatment and planning. In recent years, the success of deep learning has been vast in the field of medical imaging, and it shows state-of-the-art performance in applications like segmentation, classification, regression, and detection. Therefore, in this paper, we proposed a collective method using deep learning and radiomics techniques for the survival prediction of brain tumor patients. We first propose a hierarchical channel attention (HAM) module and a multi-scale-aware feature enhancement (MSAFE) to efficiently fuse adjacent hierarchical features in the proposed segmentation model. After segmentation, deep/latent features (LCNN) are extracted from the bottom layer of the proposed segmentation model. Later, we extracted selected radiomics features (histogram, location, and shape) using input images and segmented masks from the proposed segmentation model. Further, the 3D deep learning regressor has been trained for 3D regressor-based deep feature extraction. We proposed the method of overall survival prediction for the brain tumor patients by combining all the meaningful features including clinical features (age) that also favorably contribute to the survival days prediction for the glioma's patients. To predict the survival days for each patient, the selected features are trained to analyze the performance of various regression techniques like random forest (RF), decision tree (DT), and XGBoost. Our proposed combined feature-based method achieved the highest performance for survival days prediction over the state-of-the-art methods. We also perform extensive experiments to show the effectiveness of each feature extraction method. The experimental results infer that deep learning-based features along with radiomic features and clinical features are truly vital paradigms to estimate survival days.
DOI del artículo:	10.1002/ima.23010
Enlace a la fuente original:	https://onlinelibrary.wiley.com/doi/full/10.1002/ima.23010
Versión del articulo depositado:	info:eu-repo/semantics/publishedVersion
Acceso a la licencia de uso:	https://creativecommons.org/licenses/by/3.0/es/
Departamento:	Enginyeria Informàtica i Matemàtiques
URL Documento de licencia:	https://repositori.urv.cat/ca/proteccio-de-dades/
Áreas temáticas:	Software Radiology, nuclear medicine and imaging Radiology, nuclear medicin Optics Imaging science & photographic technology Health informatics Engineering, electrical & electronic Engenharias iv Electronic, optical and magnetic materials Electrical and electronic engineering Computer vision and pattern recognition Computer science applications Ciência da computação Biomedical engineering Astronomia / física
Palabras clave:	Survival prediction Segmentation Radiomics Multimodal brain tumor Mri Model Medical image processing Deep learning Brain tumor prognosis Brain tumor
Entidad:	Universitat Rovira i Virgili
Fecha de alta del registro:	2024-10-12

Descripción:	Automatic survival prediction of gliomas from brain magnetic resonance imaging (MRI) volumes is an essential step for a patient's prognosis analysis. Radiomics research delivers beneficial feature information from MRI imaging which is substantially required by clinicians and oncologists for predicting disease prognosis for precise surgical treatment and planning. In recent years, the success of deep learning has been vast in the field of medical imaging, and it shows state-of-the-art performance in applications like segmentation, classification, regression, and detection. Therefore, in this paper, we proposed a collective method using deep learning and radiomics techniques for the survival prediction of brain tumor patients. We first propose a hierarchical channel attention (HAM) module and a multi-scale-aware feature enhancement (MSAFE) to efficiently fuse adjacent hierarchical features in the proposed segmentation model. After segmentation, deep/latent features (LCNN) are extracted from the bottom layer of the proposed segmentation model. Later, we extracted selected radiomics features (histogram, location, and shape) using input images and segmented masks from the proposed segmentation model. Further, the 3D deep learning regressor has been trained for 3D regressor-based deep feature extraction. We proposed the method of overall survival prediction for the brain tumor patients by combining all the meaningful features including clinical features (age) that also favorably contribute to the survival days prediction for the glioma's patients. To predict the survival days for each patient, the selected features are trained to analyze the performance of various regression techniques like random forest (RF), decision tree (DT), and XGBoost. Our proposed combined feature-bas
Tipo:	Journal Publications
Coautor:	Universitat Rovira i Virgili
Títol:	Deep learning-based survival prediction of brain tumor patients using attention-guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging
Materia:	Biomedical Engineering,Computer Science Applications,Computer Vision and Pattern Recognition,Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials,Engineering, Electrical & Electronic,Health Informatics,Imaging Science & Photographic Technology,Optics,Radiology, Nuclear Medicin,Software Survival prediction Segmentation Radiomics Multimodal brain tumor Mri Model Medical image processing Deep learning Brain tumor prognosis Brain tumor Software Radiology, nuclear medicine and imaging Radiology, nuclear medicin Optics Imaging science & photographic technology Health informatics Engineering, electrical & electronic Engenharias iv Electronic, optical and magnetic materials Electrical and electronic engineering Computer vision and pattern recognition Computer science applications Ciência da computação Biomedical engineering Astronomia / física
Fecha:	2024
Autor:	Mazher, Moona Qayyum, Abdul Puig, Domenec Abdel-Nasser, Mohamed
Derechos:	info:eu-repo/semantics/openAccess

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