Cargando…

Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging

BACKGROUND: Radiomics analyses has been proposed to interrogate the biology of tumour as well as to predict/assess response to therapy in vivo. The objective of this work was to assess the sensitivity of radiomics features to noise, resolution, and tumour volume in the context of a co-clinical trial...

Descripción completa

Detalles Bibliográficos
Autores principales:	Roy, Sudipta, Whitehead, Timothy D., Quirk, James D., Salter, Amber, Ademuyiwa, Foluso O., Li, Shunqiang, An, Hongyu, Shoghi, Kooresh I.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Elsevier 2020
Materias:	Research Paper
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479492/ https://www.ncbi.nlm.nih.gov/pubmed/32891051 http://dx.doi.org/10.1016/j.ebiom.2020.102963

Ejemplares similares

Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer
por: Roy, Sudipta, et al.
Publicado: (2021)

Correction to: Co‑clinical FDG‑PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple‑negative breast cancer
por: Roy, Sudipta, et al.
Publicado: (2021)

Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary
por: Dutta, Kaushik, et al.
Publicado: (2021)

Radiomics Analysis of Susceptibility Weighted Imaging for Hepatocellular Carcinoma: Exploring the Correlation between Histopathology and Radiomics Features
por: Geng, Zhijun, et al.
Publicado: (2020)

Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer
por: Jajodia, Ankush, et al.
Publicado: (2021)

Test–Retest Performance of a 1-Hour Multiparametric MR Image Acquisition Pipeline With Orthotopic Triple-Negative Breast Cancer Patient-Derived Tumor Xenografts
por: Ge, Xia, et al.
Publicado: (2019)

T2-Weighted Image-Based Radiomics Signature for Discriminating Between Seminomas and Nonseminoma
por: Zhang, Peipei, et al.
Publicado: (2019)

Noise sensitivity of (89)Zr-Immuno-PET radiomics based on count-reduced clinical images
por: Somasundaram, Ananthi, et al.
Publicado: (2022)

Development and evaluation clinical-radiomics analysis based on T1-weighted imaging for diagnosing neonatal acute bilirubin encephalopathy
por: Yu, Jinhong, et al.
Publicado: (2023)

Substantia Nigra Radiomics Feature Extraction of Parkinson’s Disease Based on Magnitude Images of Susceptibility-Weighted Imaging
por: Ren, Qingguo, et al.
Publicado: (2021)

Diffusion-weighted imaging-based radiomics in epithelial ovarian tumors: Assessment of histologic subtype
por: Xu, Yi, et al.
Publicado: (2022)

Diffusion-weighted imaging-based radiomics for predicting 1-year ischemic stroke recurrence
por: Wang, Hao, et al.
Publicado: (2022)

Differentiation of Pelvic Osteosarcoma and Ewing Sarcoma Using Radiomic Analysis Based on T2-Weighted Images and Contrast-Enhanced T1-Weighted Images
por: Dai, Yi, et al.
Publicado: (2020)

Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials
por: Peehl, Donna M., et al.
Publicado: (2023)

A decade of radiomics research: are images really data or just patterns in the noise?
por: Pinto dos Santos, Daniel, et al.
Publicado: (2020)

Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing
por: Dulhanty, Chris, et al.
Publicado: (2020)

Radiomics signature extracted from diffusion-weighted magnetic resonance imaging predicts outcomes in osteosarcoma
por: Zhao, Shuliang, et al.
Publicado: (2019)

Amygdala's T1-weighted image radiomics outperforms volume for differentiation of anxiety disorder and its subtype
por: Li, Qingfeng, et al.
Publicado: (2023)

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T(2) *‐weighted images of cervical spondylotic myelopathy
por: Zhang, Meng‐Ze, et al.
Publicado: (2021)

Three-Dimensional Radiomics Features of Magnetic Resonance T2-Weighted Imaging Combined With Clinical Characteristics to Predict the Recurrence of Acute Pancreatitis
por: Hu, Yuntao, et al.
Publicado: (2022)

Editorial: Radiomics in Cardiovascular Imaging
por: Kolossváry, Márton, et al.
Publicado: (2022)

Evaluation of Apparent Diffusion Coefficient Repeatability and Reproducibility for Preclinical MRIs Using Standardized Procedures and a Diffusion-Weighted Imaging Phantom
por: Malyarenko, Dariya, et al.
Publicado: (2023)

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging
por: Abdel Razek, Ahmed Abdel Khalek, et al.
Publicado: (2021)

Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging
por: Liu, Panshi, et al.
Publicado: (2020)

Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases
por: Sartoretti, Elisabeth, et al.
Publicado: (2021)

Variability of radiomic features extracted from multi-b-value diffusion-weighted images in hepatocellular carcinoma
por: Zhang, Jing, et al.
Publicado: (2019)

Radiomics Nomogram Based on High-b-Value Diffusion-Weighted Imaging for Distinguishing the Grade of Bladder Cancer
por: Feng, Cui, et al.
Publicado: (2022)

Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer
por: Li, Chunli, et al.
Publicado: (2021)

Diffusion-Weighted Imaging as a Quantitative Imaging Biomarker for Predicting Proliferation Rate in Hepatocellular Carcinoma: Developing a Radiomics Nomogram
por: Yan, Chuan, et al.
Publicado: (2023)

Predicting Tumor Perineural Invasion Status in High-Grade Prostate Cancer Based on a Clinical–Radiomics Model Incorporating T2-Weighted and Diffusion-Weighted Magnetic Resonance Images
por: Zhang, Wei, et al.
Publicado: (2022)

Radiomics: the facts and the challenges of image analysis
por: Rizzo, Stefania, et al.
Publicado: (2018)

Frontiers from Radiomics in Molecular Imaging
por: Castiglioni, Isabella, et al.
Publicado: (2019)

Editorial: Radiomics in prostate cancer imaging
por: Brunese, Luca, et al.
Publicado: (2022)

Radiomics analysis in medical imaging research
por: Elmahdy, Mahmoud, et al.
Publicado: (2023)

Radiomics in Breast Imaging: Future Development
por: Panico, Alessandra, et al.
Publicado: (2023)

Radiomics in Breast Imaging from Techniques to Clinical Applications: A Review
por: Lee, Seung-Hak, et al.
Publicado: (2020)

Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma
por: Liang, Zhong-Guo, et al.
Publicado: (2019)

Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis
por: Nakamoto, Takahiro, et al.
Publicado: (2019)

Use of a radiomics-clinical model based on magnetic diffusion-weighted imaging for preoperative prediction of lymph node metastasis in rectal cancer patients
por: Li, Yehan, et al.
Publicado: (2023)

Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes
por: Leithner, Doris, et al.
Publicado: (2019)

Cannot write session to /tmp/vufind_sessions/sess_4fikclfjvgonkroml26rq92d2g