4.1 Review

Harmonization Strategies in Multicenter MRI-Based Radiomics

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Biology

A U-Net Ensemble for breast lesion segmentation in DCE MRI

Roa'a Khaled et al.

Summary: DCE-MRI is recognized as an effective tool for Breast Cancer diagnosis, with automated lesion segmentation being essential for accurate analysis. This paper proposes an automated breast lesion segmentation method based on a U-Net framework, utilizing an ensemble approach to improve accuracy and achieving better results on the TCGA-BRCA dataset.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Article Computer Science, Artificial Intelligence

Brain tumor segmentation based on the dual-path network of multi-modal MRI images

Lingling Fang et al.

Summary: This paper proposes a dual-path network based on multi-modal feature fusion to address the issue of tumor segmentation. The network effectively combines different kernel methods, reduces overlap frequency and vanishing gradient, and establishes a dual-path model to enhance segmentation accuracy.

PATTERN RECOGNITION (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

A Guide to ComBat Harmonization of Imaging Biomarkers in Multicenter Studies

Fanny Orlhac et al.

Summary: The impact of PET image acquisition and reconstruction parameters on SUV measurements or radiomic feature values is discussed in this article. The use of the ComBat method to reduce the scanner effect is explained with practical examples. Situations where the ComBat assumptions are not met and should not be used are also presented.

JOURNAL OF NUCLEAR MEDICINE (2022)

Article Multidisciplinary Sciences

Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects

Hannah Horng et al.

Summary: Radiomic features have a wide range of clinical applications, but their performance can be affected by variability due to image acquisition factors. This study proposes two methods for addressing these limitations, including a sequential method and a Gaussian Mixture Model-based method. The methods were evaluated on lung computed tomography datasets and demonstrated improved harmonization performance and similar survival analysis results.

SCIENTIFIC REPORTS (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics: a primer on high-throughput image phenotyping

Kyle J. Lafata et al.

Summary: Radiomics is a method that utilizes computer algorithms to extract and analyze quantitative features from radiological images to describe digital fingerprints of disease. It is driven by systems biology, supported by data analytics, and powered by artificial intelligence, with a process divided into five key phases. In abdominal radiology, radiomics can reduce errors and enhance the accuracy of imaging characteristics.

ABDOMINAL RADIOLOGY (2022)

Article Neurosciences

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset

Dezheng Tian et al.

Summary: The accumulation of large-sample MRI datasets from multiple sites has provided critical resources for understanding brain function and disorders. However, significant site effects hinder consistent findings across studies. In this study, a deep learning-based framework was proposed to harmonize imaging data by eliminating site effects while preserving biological characteristics. The framework performed well in removing site effects and improving data similarity, offering a powerful solution for reliable and reproducible multisite studies.

NEUROIMAGE (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Post-Processing Bias Field Inhomogeneity Correction for Assessing Background Parenchymal Enhancement on Breast MRI as a Quantitative Marker of Treatment Response

Alex Anh-Tu Nguyen et al.

Summary: Background parenchymal enhancement (BPE) in dynamic contrast-enhanced breast magnetic resonance imaging (MRI) is associated with response to neoadjuvant chemotherapy (NAC) in breast cancer patients. Bias correction improves the segmentation quality of fibroglandular tissue (FGT) and the resulting BPE measurement.

TOMOGRAPHY (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Reproducibility of radiomics features derived from intravoxel incoherent motion diffusion-weighted MRI of cervical cancer

Hao Chen et al.

Summary: This study investigated the reproducibility of IVIM-based radiomics features in cervical cancer, revealing that inter- and intra-observer variability can impact the reproducibility of these features. It suggests that multicenter studies should consider and address these sources of variability in order to improve the reliability of radiomics analysis.

ACTA RADIOLOGICA (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

How can we combat multicenter variability in MR radiomics? Validation of a correction procedure

Fanny Orlhac et al.

Summary: The study demonstrated that ComBat harmonization effectively removes inter-center technical inconsistencies in radiomic feature values and increases the sensitivity of studies using data from multiple scanners.

EUROPEAN RADIOLOGY (2021)

Article Neurosciences

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal

Nicola K. Dinsdale et al.

Summary: The study proposed a deep learning based training scheme to create scanner-invariant features, reducing the influence of scanner on network predictions. The framework can harmonize multi-site datasets and adapt to various data scenarios, including biased datasets and limited training labels.

NEUROIMAGE (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics in Oncology: A Practical Guide

Joshua D. Shur et al.

Summary: Radiomics is the extraction of mineable data from medical imaging to improve oncology diagnosis and prognostication, with a multidisciplinary workflow involving planning, data extraction, and validation. Applications in oncology typically involve classification tasks and prediction of clinical events, requiring collaboration between radiologists and data scientists.

RADIOGRAPHICS (2021)

Review Radiology, Nuclear Medicine & Medical Imaging

Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review

Cindy Xue et al.

Summary: Radiomics research has been rapidly growing in recent years, with concerns on reliability raised. This systematic review focused on the use of the intraclass correlation coefficient (ICC) as a reliability metric in radiomics studies. The review found that while feature reliability to image segmentation is extensively studied, image acquisition introduces more variability than image segmentation, particularly in MRI studies.

QUANTITATIVE IMAGING IN MEDICINE AND SURGERY (2021)

Article Multidisciplinary Sciences

The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset

Abdalla Ibrahim et al.

Summary: The reproducibility of radiomic features varies depending on the heterogeneity of the data, with a wide range of reproducible RFs across different scenarios. ComBat method may not be applicable to all RFs, but only to a percentage of those-called ComBatable RFs-depending on the data being harmonized.

PLOS ONE (2021)

Article Biology

Visual interpretability in 3D brain tumor segmentation network

Hira Saleem et al.

Summary: Medical image segmentation is crucial in diagnostic procedures, especially in brain tumor detection. This study investigates the performance of 3D CNN in brain tumor segmentation and proposes techniques for model interpretability to ensure accurate and transparent predictions.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Article Multidisciplinary Sciences

A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets

Ronrick Da-ano et al.

Summary: The study incorporated transfer learning technique into four versions of ComBat, successfully harmonizing features of new patients from known centers and achieving predictive models with similar performance as models developed using all available data for harmonization.

PLOS ONE (2021)

Article Multidisciplinary Sciences

Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups

Gergo Veres et al.

Summary: The study compared three commonly used discretization methods in MRI radiomics and analyzed the effects of parameter variations on the results. Different discretization methods were found to have an impact on the results, leading to differences in outcomes for various lesions and MRI sequences.

PLOS ONE (2021)

Review Gastroenterology & Hepatology

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

Arnaldo Stanzione et al.

Summary: Rectal cancer is a common tumor with significant morbidity and mortality rates. Medical imaging, particularly using artificial intelligence, plays a crucial role in diagnosis and treatment, offering promising results but facing challenges in clinical translation.

WORLD JOURNAL OF GASTROENTEROLOGY (2021)

Article Multidisciplinary Sciences

Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer

James C. Korte et al.

Summary: This study investigates the reproducibility of radiomics features calculated with two widely used software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Only a subset of highly correlated features are considered reproducible, and including non-reproducible radiomics features in a HNC radiotherapy response model impacts the model's performance. Using radiomic features from different software packages can classify equivalent patient groups, but only when restricting the model to reliable features.

SCIENTIFIC REPORTS (2021)

Article Multidisciplinary Sciences

Impact of rescanning and repositioning on radiomic features employing a multi-object phantom in magnetic resonance imaging

Simon Bernatz et al.

Summary: The study analyzed the robustness and reproducibility of MRI radiomic features, finding that shape features were the most robust class and T2 map was the most robust imaging technique with high robustness and discriminative power.

SCIENTIFIC REPORTS (2021)

Article Oncology

Multicenter DSC-MRI-Based Radiomics Predict IDH Mutation in Gliomas

Georgios C. Manikis et al.

Summary: Significant efforts have been made in developing MRI-based radiogenomics for predicting IDH status in gliomas, but external validation sets are often lacking. This study addresses these challenges with a multicenter DSC-MRI radiomics approach, including an independent exploratory set and external validation on two cohorts. The results showed improved predictive performance and explainability by using dynamic-based image standardization techniques.

CANCERS (2021)

Article Oncology

Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features

Yingping Li et al.

Summary: This study investigates how image preprocessing methods and harmonization methods can help remove scanner effects and improve the reproducibility of radiomic features in brain MRI studies. The ComBat method is found to be essential in removing scanner effects, while intensity normalization methods improve the robustness of the harmonized features.

CANCERS (2021)

Review Health Care Sciences & Services

Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods

Shruti Atul Mali et al.

Summary: Radiomics converts medical images into mineable data for clinical decision support. Various investigations have assessed the reproducibility and validation of radiomic features, leading to different harmonization solutions in image and feature domains to address variability across scanners and protocol settings. Deep learning solutions, such as GANs and NST techniques, are highlighted for multi-centric radiomic studies.

JOURNAL OF PERSONALIZED MEDICINE (2021)

Article Oncology

Intensity standardization methods in magnetic resonance imaging of head and neck cancer

Kareem A. Wahid et al.

Summary: The study emphasizes the importance of intensity standardization in quantitative analysis of head and neck cancer MRI. A workflow based on healthy tissue ROI analysis was developed to evaluate intensity standardization methods. Results showed significant impact of intensity standardization methods on consistency of MRI images in heterogeneous acquisition parameters, while the impact was minimal in homogeneous acquisition parameters.

PHYSICS & IMAGING IN RADIATION ONCOLOGY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study

Marie-Judith Saint Martin et al.

Summary: This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies. The pipeline effectively reduces intra and inter-acquisition variabilities, harmonising radiomic features between coils and improving lesion classification performance. More work is needed to assess this pipeline on patient data for robust multi-scanner radiomic studies.

MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Experimental Multicenter and Multivendor Evaluation of the Performance of PET Radiomic Features Using 3-Dimensionally Printed Phantom Inserts

Elisabeth Pfaehler et al.

JOURNAL OF NUCLEAR MEDICINE (2020)

Review Radiology, Nuclear Medicine & Medical Imaging

Radiomics: from qualitative to quantitative imaging

William Rogers et al.

BRITISH JOURNAL OF RADIOLOGY (2020)

Article Engineering, Biomedical

MedGAN: Medical image translation using GANs

Karim Armanious et al.

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma

Hajar Moradmand et al.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Introduction to Radiomics

Marius E. Mayerhoefer et al.

JOURNAL OF NUCLEAR MEDICINE (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

T2w-MRI signal normalization affects radiomics features reproducibility

Elisa Scalco et al.

MEDICAL PHYSICS (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Effects of MRI image normalization techniques in prostate cancer radiomics

Lars J. Isaksson et al.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing

He Ren et al.

QUANTITATIVE IMAGING IN MEDICINE AND SURGERY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform

Isabella Fornacon-Wood et al.

EUROPEAN RADIOLOGY (2020)

Review Oncology

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

Martin Kocher et al.

STRAHLENTHERAPIE UND ONKOLOGIE (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Scanner invariant representations for diffusion MRI harmonization

Daniel Moyer et al.

MAGNETIC RESONANCE IN MEDICINE (2020)

Editorial Material Radiology, Nuclear Medicine & Medical Imaging

Current status of Radiomics for cancer management: Challenges versus opportunities for clinical practice

Hua Li et al.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2020)

Review Radiology, Nuclear Medicine & Medical Imaging

Radiomics in medical imaging-how-to guide and critical reflection

Janita E. van Timmeren et al.

INSIGHTS INTO IMAGING (2020)

Article Computer Science, Interdisciplinary Applications

Superpixel-based deep convolutional neural networks and active contour model for automatic prostate segmentation on 3D MRI scans

Giovanni L. F. da Silva et al.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Robustness of CT radiomic features against image discretization and interpolation in characterizing pancreatic neuroendocrine neoplasms

Sara Loi et al.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2020)

Review Engineering, Biomedical

Harmonization strategies for multicenter radiomics investigations

R. Da-Ano et al.

PHYSICS IN MEDICINE AND BIOLOGY (2020)

Article Multidisciplinary Sciences

Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics

Alexandre Carre et al.

SCIENTIFIC REPORTS (2020)

Article Computer Science, Information Systems

Medical image processing with contextual style transfer

Yin Xu et al.

HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES (2020)

Proceedings Paper Engineering, Biomedical

MRI Image Harmonization using Cycle-Consistent Generative Adversarial Network

Gourav Modanwal et al.

MEDICAL IMAGING 2020: COMPUTER-AIDED DIAGNOSIS (2020)

Article Computer Science, Artificial Intelligence

Beyond Sharing Weights for Deep Domain Adaptation

Artem Rozantsev et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2019)

Article Multidisciplinary Sciences

Gray-level discretization impacts reproducible MRI radiomics texture features

Loic Duron et al.

PLOS ONE (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type

Helge C. Kniep et al.

RADIOLOGY (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

DeepHarmony: A deep learning approach to contrast harmonization across scanner changes

Blake E. Dewey et al.

MAGNETIC RESONANCE IMAGING (2019)

Article Oncology

Repeatability and reproducibility of MRI-based radiomic features in cervical cancer

Sandra Fiset et al.

RADIOTHERAPY AND ONCOLOGY (2019)

Article Multidisciplinary Sciences

Repeatability of Multiparametric Prostate MRI Radiomics Features

Michael Schwier et al.

SCIENTIFIC REPORTS (2019)

Article Multidisciplinary Sciences

Assessing radiomic feature robustness to interpolation in 18F-FDG PET imaging

Philip Whybra et al.

SCIENTIFIC REPORTS (2019)

Review Radiology, Nuclear Medicine & Medical Imaging

MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology

Ulrike Schick et al.

BRITISH JOURNAL OF RADIOLOGY (2019)

Article Medicine, General & Internal

Tumor grading of soft tissue sarcomas using MRI-based radiomics

Jan C. Peeken et al.

EBIOMEDICINE (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy

Francois Lucia et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Robustness and Reproducibility of Radiomics in Magnetic Resonance Imaging A Phantom Study

Bettina Baessler et al.

INVESTIGATIVE RADIOLOGY (2019)

Review Radiology, Nuclear Medicine & Medical Imaging

Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives

Ji Eun Park et al.

KOREAN JOURNAL OF RADIOLOGY (2019)

Proceedings Paper Engineering, Biomedical

Liver Segmentation in Abdominal CT Images Using Probabilistic Atlas and Adaptive 3D Region Growing

Shima Rafiei et al.

2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) (2019)

Review Radiology, Nuclear Medicine & Medical Imaging

Machine learning applications in prostate cancer magnetic resonance imaging

Renato Cuocolo et al.

EUROPEAN RADIOLOGY EXPERIMENTAL (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Creating Robust Predictive Radiomic Models for Data From Independent Institutions Using Normalization

Avishek Chatterjee et al.

IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Feasibility of state of the art PET/CT systems performance harmonisation

Andres Kaalep et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study

Rafael Ortiz-Ramon et al.

EUROPEAN RADIOLOGY (2018)

Review Oncology

Repeatability and Reproducibility of Radiomic Features: A Systematic Review

Alberto Traverso et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors

Ashirbani Saha et al.

MEDICAL PHYSICS (2018)

Article Neurosciences

Harmonization of cortical thickness measurements across scanners and sites

Jean-Philippe Fortin et al.

NEUROIMAGE (2018)

Editorial Material Biology

Rethinking the role of clinical imaging

James P. B. O'Connor

Article Clinical Neurology

Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis

R. T. Shinohara et al.

AMERICAN JOURNAL OF NEURORADIOLOGY (2017)

Article Computer Science, Artificial Intelligence

Modified possibilistic fuzzy C-means algorithms for segmentation of magnetic resonance image

Jeetashree Aparajeeta et al.

APPLIED SOFT COMPUTING (2016)

Article Neurosciences

Removing inter-subject technical variability in magnetic resonance imaging studies

Jean-Philippe Fortin et al.

NEUROIMAGE (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics: Images Are More than Pictures, They Are Data

Robert J. Gillies et al.

RADIOLOGY (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0

Ronald Boellaard et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2015)

Article Neuroimaging

Statistical normalization techniques for magnetic resonance imaging

Russell T. Shinohara et al.

NEUROIMAGE-CLINICAL (2014)

Article Radiology, Nuclear Medicine & Medical Imaging

3D Slicer as an image computing platform for the Quantitative Imaging Network

Andriy Fedorov et al.

MAGNETIC RESONANCE IMAGING (2012)

Article Computer Science, Artificial Intelligence

Evaluating intensity normalization on MRIs of human brain with multiple sclerosis

Mohak Shah et al.

MEDICAL IMAGE ANALYSIS (2011)

Article Computer Science, Interdisciplinary Applications

N4ITK: Improved N3 Bias Correction

Nicholas J. Tustison et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2010)

Article Mathematical & Computational Biology

Adjusting batch effects in microarray expression data using empirical Bayes methods

W. Evan Johnson et al.

BIOSTATISTICS (2007)

Review Computer Science, Interdisciplinary Applications

A review of methods for correction of intensity inhomogeneity in MRI

Uros Vovk et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2007)

Article Radiology, Nuclear Medicine & Medical Imaging

Partial volume segmentation of brain magnetic resonance images based on maximum a posteriori probability

X Li et al.

MEDICAL PHYSICS (2005)

Article Radiology, Nuclear Medicine & Medical Imaging

Influence of MRI acquisition protocols and image intensity normalization methods on texture classification

G Collewet et al.

MAGNETIC RESONANCE IMAGING (2004)

Article Computer Science, Interdisciplinary Applications

New variants of a method of MRI scale standardization

LG Nyúl et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2000)