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

Radiology, Journal Year: 2020, Volume and Issue: 295(2), P. 328 - 338

Published: March 10, 2020

The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of biomarkers from acquired imaging for purpose high-throughput quantitative analysis (radiomics). Lack reproducibility and validation studies considered to be a major challenge field. Part this lies in scantiness consensus-based guidelines definitions process translating into biomarkers. IBSI therefore seeks provide nomenclature definitions, benchmark data sets, values verify processing calculations, as well reporting guidelines, analysis.

Language: Английский

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Machine and deep learning methods for radiomics

Medical Physics, Journal Year: 2020, Volume and Issue: 47(5)

Published: May 1, 2020

Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale extracted imaging information clinical and biological endpoints. The development of methods along with machine learning has enabled the opportunity move data science research towards translation for more personalized cancer treatments. Accumulating evidence indeed demonstrated noninvasive advanced analytics, is, radiomics, can reveal key components tumor phenotype multiple three‐dimensional lesions at time points over beyond course treatment. These developments use CT, PET, US, MR could augment patient stratification prognostication buttressing targeted therapeutic approaches. In recent years, deep architectures have their tremendous potential segmentation, reconstruction, recognition, classification. Many powerful open‐source commercial platforms are currently available embark new areas radiomics. Quantitative research, however, complex statistical principles should be followed realize its full potential. field particular, requires a renewed focus on optimal study design/reporting practices standardization acquisition, feature calculation, rigorous forward. this article, role as major computational vehicle model building radiomics‐based signatures or classifiers, diverse applications, working principles, opportunities, radiomics will reviewed examples drawn primarily from oncology. We also address issues related common applications medical physics, such standardization, extraction, building, validation.

Language: Английский

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The Biological Meaning of Radiomic Features

Radiology, Journal Year: 2021, Volume and Issue: 298(3), P. 505 - 516

Published: Jan. 5, 2021

Radiomic analysis offers a powerful tool for the extraction of clinically relevant information from radiologic imaging. Radiomics can be used to predict patient outcome through automated high-throughput feature extraction, using large training cohorts elucidate subtle relationships between image characteristics and disease status. However powerful, data-driven nature radiomics inherently no insight into biological underpinnings observed relationships. Early work was dominated by semantic, radiologist-defined features carried qualitative real-world meaning. Following rapid developments popularity machine learning approaches, field moved quickly toward agnostic analyses, resulting in increasingly sets. This trend took focus an increase predictive power further away understanding findings. Such disconnect predictor model meaning will limit broad clinical translation. Efforts reintroduce are gaining traction with distinct emerging approaches available, including genomic correlates, local microscopic pathologic textures, macroscopic histopathologic marker expression. These methods presented this review, their significance is discussed. The authors that following increasing pressure robust radiomics, validation become standard practice field, thus cementing role method decision making. © RSNA, 2021 An earlier incorrect version appeared online. article corrected on February 10, 2021.

Language: Английский

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The Biological Meaning of Radiomic Features

Radiology, Journal Year: 2021, Volume and Issue: 299(2), P. E256 - E256

Published: April 26, 2021

Language: Английский

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Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis

European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2019, Volume and Issue: 46(13), P. 2638 - 2655

Published: June 25, 2019

Language: Английский

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CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII

Insights into Imaging, Journal Year: 2023, Volume and Issue: 14(1)

Published: May 4, 2023

Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine practice. The workflow of complex due several methodological steps and nuances, which often leads inadequate reporting evaluation, poor reproducibility. Available guidelines checklists artificial intelligence predictive modeling include relevant good practices, but they are not tailored radiomic research. There a clear need complete checklist study planning, manuscript writing, evaluation during the review process facilitate repeatability reproducibility studies. We here present documentation standard research that guide authors reviewers. Our motivation improve quality reliability and, turn, name CLEAR (CheckList EvaluAtion Radiomics research), convey idea being more transparent. With 58 items, should be considered standardization tool providing minimum requirements presenting In addition dynamic online version checklist, public repository has also been set up allow community comment on items adapt future versions. Prepared revised by an international group experts using modified Delphi method, we hope will serve well as single scientific reviewers literature.

Language: Английский

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Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2019, Volume and Issue: 46(13), P. 2656 - 2672

Published: June 18, 2019

The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological non-oncological applications, in order assess how far the image mining research stands from routine application. To do this, we applied a trial phases classification inspired drug development process. Among articles considered inclusion PubMed were multimodality AI radiomics investigations, with validation analysis aimed at relevant clinical objectives. Quality assessment selected papers performed according QUADAS-2 criteria. We developed criteria studies. Overall 34,626 retrieved, 300 applying inclusion/exclusion criteria, 171 high-quality (QUADAS-2 ≥ 7) identified analysed. In 27/171 (16%), 141/171 (82%), 3/171 (2%) studies an AI-based algorithm, model, combined radiomics/AI approach, respectively, described. A total 26/27(96%) 1/27 (4%) classified as phase II III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), 7/141 (5%) 0, I, II, All three categorised trials. results are promising but still not mature enough tools be implemented setting widely used. transfer learning well-known process, some specific adaptations discipline could represent most effective way algorithms become standard care tools.

Language: Английский

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Assessing robustness of radiomic features by image perturbation

Scientific Reports, Journal Year: 2019, Volume and Issue: 9(1)

Published: Jan. 24, 2019

Image features need to be robust against differences in positioning, acquisition and segmentation ensure reproducibility. Radiomic models that only include can used analyse new images, whereas with non-robust may fail predict the outcome of interest accurately. Test-retest imaging is recommended assess robustness, but not available for phenotype interest. We therefore investigated 18 methods determine feature robustness based on image perturbations. perturbation were compared 4032 computed from gross tumour volume two cohorts tomography imaging: I) 31 non-small-cell lung cancer (NSCLC) patients; II): 19 head-and-neck squamous cell carcinoma (HNSCC) patients. Robustness was measured using intraclass correlation coefficient (1,1) (ICC). Features ICC$\geq0.90$ considered robust. The NSCLC cohort contained more test-retest than HNSCC ($73.5\%$ vs. $34.0\%$). A chain consisting noise addition, affine translation, growth/shrinkage supervoxel-based contour randomisation identified fewest false positive (NSCLC: $3.3\%$; HNSCC: $10.0\%$). Thus, this robustness.

Language: Английский

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Machine learning in cardiovascular magnetic resonance: basic concepts and applications

Journal of Cardiovascular Magnetic Resonance, Journal Year: 2019, Volume and Issue: 21(1), P. 61 - 61

Published: Jan. 1, 2019

Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas CMR where ML, and deep particular, can assist clinicians engineers improving imaging efficiency, quality, image analysis interpretation, as well patient evaluation. We discuss recent developments field of ML relevant acquisition & reconstruction, analysis, diagnostic evaluation derivation prognostic information. To date, main has been significantly reduce time required for segmentation analysis. Accurate reproducible fully automated quantification left right ventricular mass volume now available commercial products. Active research include reduction reconstruction time, spatial temporal resolution, perfusion myocardial mapping. Although large cohort studies are providing valuable data sets training, care must be taken extending applications specific groups. Since algorithms fail unpredictable ways, it important mitigate this by open source publication computational processes datasets. Furthermore, controlled trials needed evaluate methods across multiple centers

Language: Английский

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

European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2018, Volume and Issue: 46(4), P. 864 - 877

Published: Dec. 7, 2018

Language: Английский

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