☆ 4.6 Review

The stability of oncologic MRI radiomic features and the potential role of deep learning: a review

PHYSICS IN MEDICINE AND BIOLOGY (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Self-supervised IVIM DWI parameter estimation with a physics based forward model

Serge Didenko Vasylechko et al.

Summary: The novel IVIM parameter fitting method demonstrates increased robustness to noise and improved repeatability in comparison to conventional methods. It also shows potential for more accurate characterization between normal and abnormal tissues in Crohn's disease through linear discriminant analysis.

MAGNETIC RESONANCE IN MEDICINE (2022)

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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)

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Article Biochemical Research Methods

Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework

A. Ibrahim et al.

Summary: The advancement of artificial intelligence and medical imaging techniques presents a unique opportunity for the quantitative and mineable analysis of medical images. However, challenges such as explainability of models, reproducibility of imaging features, and sensitivity to variations in image acquisition need to be addressed before clinical translation.

METHODS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Normative Baseline for Radiomics in BrainMRI: Evaluating the Robustness, Regional Variations, and Reproducibility onFLAIRImages

Umang Pandey et al.

Summary: Radiomics in neuroimaging plays a crucial role in predicting brain tumors and phenotypic signatures in neurological disorders. However, the robustness and reproducibility of radiomic features in a baseline normative population are still not well understood. This study investigates the intra- and interscanner reproducibility, spatial robustness, and sensitivity of radiomics on FLAIR images. Analysis revealed age-related differences in radiomic features, with spatial variability and poor inter-scanner reproducibility, which was improved with site-harmonization techniques.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

A multicenter study on radiomic features from T2-weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics

Linda Bianchini et al.

Summary: The study investigated the repeatability and reproducibility of radiomic features extracted from MR images under different imaging parameters, finding that these features are affected by MRI protocols. A proposed workflow aims to identify repeatable, reproducible, and informative radiomic features for robustness in clinical studies.

MAGNETIC RESONANCE IN MEDICINE (2021)

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Review Radiology, Nuclear Medicine & Medical Imaging

A review on medical imaging synthesis using deep learning and its clinical applications

Tonghe Wang et al.

Summary: This paper reviewed the recent developments of deep learning-based methods for medical imaging synthesis and its clinical applications, highlighting the challenges among the reviewed studies and providing discussions on them.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Reliability of radiomics features due to image reconstruction using a standardized T2-weighted pulse sequence for MR-guided radiotherapy: An anthropomorphic phantom study

Cindy Xue et al.

Summary: This study found that SENSE reconstruction achieved better accuracy and robustness compared to compressed-SENSE reconstruction in MRI radiomics. Repeatability of radiomics features was more dependent on feature types than on reconstruction methods. First-order and gray-level co-occurrence matrix features tended to be more reliable than other features.

MAGNETIC RESONANCE IN MEDICINE (2021)

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Article Oncology

Image Contrast, Image Pre-Processing, and T1 Mapping Affect MRI Radiomic Feature Repeatability in Patients with Colorectal Cancer Liver Metastases

Damien J. McHugh et al.

Summary: This study evaluates the repeatability of radiomic features from different MR sequences in predicting therapy response in oncology patients. It highlights the impact of scan acquisition parameters and the use of contrast agents on radiomic feature repeatability. The findings emphasize the importance of evaluating feature-specific repeatability and choosing appropriate metrics in specific studies for robust biomarker selection.

CANCERS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Reliability of MRI radiomics features in MR-guided radiotherapy for prostate cancer: Repeatability, reproducibility, and within-subject agreement

Cindy Xue et al.

Summary: This study investigated the reliability of MRgRT radiomics features acquired using standardized 3D-T2W-TSE sequence on 1.5T MR-simulator and 1.5T MR-LINAC for prostate cancer. Results showed that only a small proportion of features exhibited good repeatability and reproducibility, emphasizing the importance of reliable feature selection in MRgRT applications. The within-subject feature values were found to have considerable inter-scan variations, posing a challenge for future MRgRT delta-radiomics studies.

MEDICAL PHYSICS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Assessment of Repeatability, Reproducibility, and Performances of T2 Mapping-Based Radiomics Features: A Comparative Study

Amandine Crombe et al.

Summary: The study found that T2 mapping provides better repeatability and reproducibility compared to conventional T2-weighted imaging, offering the most discriminative information. The new method has a significant impact on radiomics features, showing moderate to substantial repeatability and reproducibility.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2021)

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Article Engineering, Biomedical

Evaluation of conventional and deep learning based image harmonization methods in radiomics studies

F. Tixier et al.

Summary: This study evaluated the impact of image harmonization on outcome prediction models using radiomics and found that harmonization methods can improve feature selection and build better survival prediction models. The GAN-based image harmonization method demonstrated superior performance in model development compared to histogram matching.

PHYSICS IN MEDICINE AND BIOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions

Arnaldo Stanzione et al.

Summary: The study evaluated the performance of a radiomic machine learning model in classifying solid adrenal lesions without fat signal drop on chemical shift as benign or malignant. Through feature extraction and selection, the model achieved high accuracy and consistency on both the training and test sets.

MAGNETIC RESONANCE IMAGING (2021)

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Article Oncology

Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer

Shin-Hyung Park et al.

Summary: This study aimed to assess the robustness of MR radiomic features in patients with cervical cancer to pixel size resampling and interpolation, with most features showing good robustness but some features being sensitive to variations in pixel size and slice thickness.

CANCER IMAGING (2021)

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Article Multidisciplinary Sciences

Radiomics feature robustness as measured using an MRI phantom

Joonsang Lee et al.

Summary: The study investigated the robustness of radiomics features on MRI with different scanning parameters and scanners using a radiomics phantom. Variability of features with different parameters and repeatability of test-retest scheme were measured, showing dependence on scanning parameters and scanners. Low variation and high ICC features are good candidates for MRI radiomics studies.

SCIENTIFIC REPORTS (2021)

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Article Computer Science, Artificial Intelligence

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Erico Tjoa et al.

Summary: Artificial intelligence and machine learning have shown remarkable performances in various fields, but the challenge of interpretability remains. The medical sector requires higher levels of interpretability to ensure the reliability of machine decisions, and a deeper understanding of the mechanisms behind machine algorithms is needed to advance medical practices.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Reproducibility of radiomic features in SENSE and compressed SENSE: impact of acceleration factors

Minjae Kim et al.

Summary: Higher acceleration factors led to lower reproducibility of radiomic features in both SENSE and CS, with SENSE showing higher reproducibility compared to CS. The run percentage of gray-level run-length matrix (GLRLM) with wavelet D was identified as the most reproducible feature.

EUROPEAN RADIOLOGY (2021)

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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)

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Article Radiology, Nuclear Medicine & Medical Imaging

Prospective Evaluation of Repeatability and Robustness of Radiomic Descriptors in Healthy Brain Tissue Regions In Vivo Across Systematic Variations in T2-Weighted Magnetic Resonance Imaging Acquisition Parameters

Brendan Eck et al.

Summary: Radiomic descriptors from MRI scans of healthy brain tissue regions show good repeatability and robustness in controlled settings with systematic variations in imaging parameters. Significant associations were found between certain parameter changes and the robustness of radiomic descriptors, highlighting the importance of feature formulation and postprocessing in determining the reliability of radiomic analysis.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

Panagiotis Papadimitroulas et al.

Summary: Radiomics and Deep Neural Networks (DNNs) have shown promising applications in medical image analysis for clinical practices in terms of classification and prediction. However, most current research is limited by dataset availability and generalizability. The study stresses the importance of multicenter recruitment of large datasets to enhance biomarker variability, establish the potential clinical value of radiomics, and develop robust explainable AI models.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2021)

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Article Multidisciplinary Sciences

Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction

N. Koonjoo et al.

Summary: Recent advancements in magnet, coil, and gradient set designs have sparked a renewed interest in inexpensive low magnetic field MRI systems. Our study demonstrates the effectiveness of using an end-to-end deep neural network approach (AUTOMAP) to significantly improve image quality in highly noise-corrupted low-field MRI data, outperforming contemporary denoising algorithms and suppressing noise-like artifacts. Domain-specific training corpora play an important role in the reconstruction performance of the AUTOMAP approach.

SCIENTIFIC REPORTS (2021)

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Review Oncology

Understanding Sources of Variation to Improve the Reproducibility of Radiomics

Binsheng Zhao

Summary: Radiomics is the method of choice for investigating the association between cancer imaging phenotype, cancer genotype, and clinical outcome prediction in the era of precision medicine, but its clinical use faces challenges due to reasons such as reproducibility and generalizability issues. This article discusses sources of variation in radiomics signatures, reviews studies on feature reproducibility and model performance, and explores strategies to reduce feature variability and improve the quality of radiomics studies.

FRONTIERS IN ONCOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D-T2W-TSE sequence for MR-guided-radiotherapy

Jing Yuan et al.

Summary: MRI radiomics features are significantly influenced by MRI pulse sequences and imaging parameters, with some features showing more robustness to parameter adjustments. About 23.7% of the features were identified as robust to imaging parameters, aiding in reliable radiomic modeling.

QUANTITATIVE IMAGING IN MEDICINE AND SURGERY (2021)

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Article Medicine, General & Internal

Advanced Imaging Analysis in Prostate MRI: Building a Radiomic Signature to Predict Tumor Aggressiveness

Anna Damascelli et al.

Summary: This exploratory study implemented a reproducible radiomic-pipeline to extract MRI signatures for prostate cancer aggressiveness. By analyzing radiomic patterns and their association with Gleason Grade Group, extracapsular extension, and nodal involvement, a reliable multiparametric MRI radiomic signature potentially able to predict PCa aggressiveness was extracted. The best performance was achieved by combining imaging features from multiparametric images.

DIAGNOSTICS (2021)

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Article Computer Science, Interdisciplinary Applications

Normalization of breast MRIs using cycle-consistent generative adversarial networks

Gourav Modanwal et al.

Summary: This study proposes a method to normalize images between different MRI scanners to improve the diagnosis and detection of breast cancer. Through technical innovations, it successfully preserves the breast shape and tissue structures while achieving proper intensity normalization and noise distribution mapping.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

A Multi-Variate framework to assess reliability and discrimination power of Bayesian estimation of Intravoxel Incoherent Motion parameters

E. Scalco et al.

Summary: A multivariate multi-step framework was proposed to systematically assess the reliability and discriminability of IVIM model parameters. The study found that IVIM model is generally accurate in estimating diffusion, but uncertainties related to perfusion estimation can compromise the discrimination power when different populations should be differentiated.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2021)

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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)

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Article Multidisciplinary Sciences

Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network

Yasuhisa Kurata et al.

Summary: Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and preoperative risk stratification is essential for personalized medicine. A study using convolutional neural network for automatic segmentation of EC on MRI showed that using multi-sequence images and larger batch size improved segmentation accuracy, and the radiomics features exhibited high reliability.

SCIENTIFIC REPORTS (2021)

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Article Biotechnology & Applied Microbiology

Assessment of MRI-Based Radiomics in Preoperative T Staging of Rectal Cancer: Comparison between Minimum and Maximum Delineation Methods

Haidi Lu et al.

Summary: This study aimed to validate the effect of a radiomics model based on MRI derived from two delineation methods in the preoperative T staging of rectal cancer patients. Findings showed that MRI-based radiomics models demonstrated good performance, with maximum delineation method being more clinically beneficial.

BIOMED RESEARCH INTERNATIONAL (2021)

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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)

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Article Oncology

Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary

Kaushik Dutta et al.

Summary: Co-clinical trials combine clinical and preclinical trials to support corresponding clinical trials, with radiomics being widely explored in clinical imaging for predicting therapy response. This study developed a reproducible DL algorithm for automated segmentation and feature extraction from preclinical MR images. The D-R2UNet network architecture demonstrated high correlation and reproducibility with STAPLE-derived features, providing a sensitive analysis of radiomic features to tumor boundaries.

CANCERS (2021)

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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)

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Article Radiology, Nuclear Medicine & Medical Imaging

MRI texture feature repeatability and image acquisition factor robustness, a phantom study and in silico study

Joshua Shur et al.

Summary: In the phantom test-retest study, 80% of MRI features were found to be repeatable, with the majority showing strong or intermediate correlation with image acquisition parameters. Approximately 41% and 28% of features were highly robust to noise and resolution, respectively. The agreement between acquired and simulation data varied, indicating that the robustness of features depends on acquisition parameters and appropriate features can be selected for clinical validation studies.

EUROPEAN RADIOLOGY EXPERIMENTAL (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma

Ji Eun Park et al.

Summary: The study found that DLAS can robustly extract MRI features, assisting in the accurate assessment of treatment response in glioblastoma patients. The DLAS-based radiomics showed high reproducibility and comparable diagnostic performance to human manual segmentations, supporting its utility in quantitative MRI analysis.

EUROPEAN RADIOLOGY (2021)

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Radiomics Repeatability Pitfalls in a Scan-Rescan MRI Study of Glioblastoma

Katharina Hoebel et al.

Summary: The study investigates the impact of preprocessing on the repeatability and redundancy of radiomics features in glioblastoma MRI scans. Results show that shape features exhibit higher repeatability compared to intensity and texture features. Normalization improves the overlap of intensity histograms, but not in cases where brain extraction fails. The study highlights the importance of accurately reporting methodology and understanding the limitations of preprocessing choices in radiomics research.

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Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI

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MAGNETIC RESONANCE IN MEDICINE (2020)

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Sensitivity of radiomic features to inter-observer variability and image pre-processing in Apparent Diffusion Coefficient (ADC) maps of cervix cancer patients

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RADIOTHERAPY AND ONCOLOGY (2020)

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Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer

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Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer

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

Hajar Moradmand et al.

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T2w-MRI signal normalization affects radiomics features reproducibility

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Fast contour propagation for MR-guided prostate radiotherapy using convolutional neural networks

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A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion-weighted MRI

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Effects of MRI image normalization techniques in prostate cancer radiomics

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Prostate MRI radiomics: A systematic review and radiomic quality score assessment

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The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

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Intensity non-uniformity correction in MR imaging using residual cycle generative adversarial network

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MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability

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SCIENTIFIC REPORTS (2020)

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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

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Combining Multiple Magnetic Resonance Imaging Sequences Provides Independent Reproducible Radiomics Features

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

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