Cargando…

Erratum: Imaging biomarkers of glioblastoma treatment response: a systematic review and meta-analysis of recent machine learning studies

Detalles Bibliográficos
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2023
Materias:	Oncology
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246473/ https://www.ncbi.nlm.nih.gov/pubmed/37293592 http://dx.doi.org/10.3389/fonc.2023.1217461

Ejemplares similares

Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies
por: Booth, Thomas C., et al.
Publicado: (2022)

Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis
por: Guha, Amrita, et al.
Publicado: (2022)

Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors
por: Chen, Chaoyue, et al.
Publicado: (2019)

Next Generation Sequencing and Machine Learning Technologies Are Painting the Epigenetic Portrait of Glioblastoma
por: Jovčevska, Ivana
Publicado: (2020)

Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma
por: Qian, Zenghui, et al.
Publicado: (2021)

Efficacy and Safety of Hypofractionated Radiotherapy for the Treatment of Newly Diagnosed Glioblastoma Multiforme: A Systematic Review and Meta-Analysis
por: Liao, Guixiang, et al.
Publicado: (2019)

Corrigendum: Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma
por: Qian, Zenghui, et al.
Publicado: (2021)

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis
por: Li, Yilin, et al.
Publicado: (2022)

Editorial: Molecular biology of biomarkers in diagnosis and treatment of glioblastoma multiforme
por: Zhao, Jianyi, et al.
Publicado: (2023)

The application of machine learning to imaging in hematological oncology: A scoping review
por: Kotsyfakis, Stylianos, et al.
Publicado: (2022)

A Systematic Review and Meta-Analysis on the Number of Adjuvant Temozolomide Cycles in Newly Diagnosed Glioblastoma
por: Attarian, Fahimeh, et al.
Publicado: (2021)

Radiomics-Based Machine Learning Technology Enables Better Differentiation Between Glioblastoma and Anaplastic Oligodendroglioma
por: Fan, Yimeng, et al.
Publicado: (2019)

Erratum: Tumor response assessment on imaging following immunotherapy
Publicado: (2023)

Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis
por: Lu, Dongmei, et al.
Publicado: (2023)

The predictive value of radiomics-based machine learning for peritoneal metastasis in gastric cancer patients: a systematic review and meta-analysis
por: Zhang, Fan, et al.
Publicado: (2023)

Imaging-Based Machine Learning Analysis of Patient-Derived Tumor Organoid Drug Response
por: Spiller, Erin R., et al.
Publicado: (2021)

Moving towards a unified classification of glioblastomas utilizing artificial intelligence and deep machine learning integration
por: Hill, Ciaran Scott, et al.
Publicado: (2023)

Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review
por: Subramanian, Harry, et al.
Publicado: (2021)

Whole-Body MRI Is an Effective Imaging Modality for Hematological Malignancy Treatment Response Assessment: A Systematic Review and Meta-Analysis
por: Lin, Guisen, et al.
Publicado: (2022)

The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis
por: Li, Yue, et al.
Publicado: (2023)

PD-L1 Expression in Glioblastoma, the Clinical and Prognostic Significance: A Systematic Literature Review and Meta-Analysis
por: Hao, Chengcheng, et al.
Publicado: (2020)

Comparison of Radiomics-Based Machine-Learning Classifiers in Diagnosis of Glioblastoma From Primary Central Nervous System Lymphoma
por: Chen, Chaoyue, et al.
Publicado: (2020)

Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
por: Parmar, Chintan, et al.
Publicado: (2015)

A Comparison Between Chemo-Radiotherapy Combined With Immunotherapy and Chemo-Radiotherapy Alone for the Treatment of Newly Diagnosed Glioblastoma: A Systematic Review and Meta-Analysis
por: Lara-Velazquez, Montserrat, et al.
Publicado: (2021)

Xerna™ TME Panel is a machine learning-based transcriptomic biomarker designed to predict therapeutic response in multiple cancers
por: Uhlik, Mark, et al.
Publicado: (2023)

Multiparametric MRI and [(18)F]Fluorodeoxyglucose Positron Emission Tomography Imaging Is a Potential Prognostic Imaging Biomarker in Recurrent Glioblastoma
por: Hassanzadeh, Comron, et al.
Publicado: (2017)

Predicting IDH subtype of grade 4 astrocytoma and glioblastoma from tumor radiomic patterns extracted from multiparametric magnetic resonance images using a machine learning approach
por: Kandalgaonkar, Pashmina, et al.
Publicado: (2022)

Blood Biomarkers of Glioma in Response Assessment Including Pseudoprogression and Other Treatment Effects: A Systematic Review
por: Raza, Istafa J., et al.
Publicado: (2020)

Targeting Glioblastoma Stem Cells: A Review on Biomarkers, Signal Pathways and Targeted Therapy
por: Tang, Xuejia, et al.
Publicado: (2021)

A systematic review on intra-arterial cerebral infusions of chemotherapeutics in the treatment of glioblastoma multiforme: The state-of-the-art
por: Pinkiewicz, Mateusz, et al.
Publicado: (2022)

Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review
por: Xie, Xiaoliang, et al.
Publicado: (2021)

Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review
por: Wijethilake, Navodini, et al.
Publicado: (2023)

Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures
por: Liu, Dongming, et al.
Publicado: (2021)

Erratum: Efficacy of Location-Based Features for Survival Prediction of Patients With Glioblastoma Depending on Resection Status
Publicado: (2021)

Circulating tumour DNA as biomarker for rectal cancer: A systematic review and meta-analyses
por: van Rees, Jan M., et al.
Publicado: (2023)

Development of a machine learning framework for radiation biomarker discovery and absorbed dose prediction
por: Andersson, Björn, et al.
Publicado: (2023)

Erratum: Improving the Prediction of Benign or Malignant Breast Masses Using a Combination of Image Biomarkers and Clinical Parameters
Publicado: (2021)

Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early
por: Morelli, Mariangela, et al.
Publicado: (2022)

Comparing Biomarkers for Predicting Pathological Responses to Neoadjuvant Therapy in HER2-Positive Breast Cancer: A Systematic Review and Meta-Analysis
por: Zhao, Fuxing, et al.
Publicado: (2021)

Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis
por: de Causans, Alix, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_inmbivv1os4masd9o98kngcovi