Multiparametric MRI for characterization of the tumour microenvironment

Nature Reviews Clinical Oncology, Journal Year: 2024, Volume and Issue: 21(6), P. 428 - 448

Published: April 19, 2024

Language: Английский

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Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative

European Radiology Experimental, Journal Year: 2024, Volume and Issue: 8(1)

Published: May 14, 2024

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, can be accomplished with systematic use guidelines. The CheckList for EvaluAtion Radiomics (CLEAR) was previously developed assist authors their radiomic reviewers evaluation. To take full advantage CLEAR, further explanation elaboration each item, well literature examples, may useful. main goal this work, Explanation Elaboration Examples CLEAR (CLEAR-E3), improve CLEAR's usability dissemination. In international collaborative effort, members the European Society Medical Imaging Informatics-Radiomics Auditing Group searched identify representative examples item. At least two demonstrating optimal reporting, were presented All selected from open-access articles, allowing users easily consult corresponding full-text articles. addition these, item's expanded elaborated. For easier access, resulting document available at https://radiomic.github.io/CLEAR-E3/ . As complementary effort we anticipate that initiative will greater ease transparency, editors reviewing manuscripts.Relevance statement Along original checklist, CLEAR-E3 expected provide more in-depth understanding items, concrete evaluating research.Key points• aims research, manuscripts.• Based on positive by EuSoMII Group, item elaborated CLEAR-E3.• accessed

Language: Английский

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Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes

European Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

Abstract Objectives To investigate how studies determine the sample size when developing radiomics prediction models for binary outcomes, and whether meets estimates obtained by using established criteria. Methods We identified that were published from 01 January 2023 to 31 December in seven leading peer-reviewed radiological journals. reviewed justification methods, actual used. calculated compared used three criteria proposed Riley et al. investigated which characteristics factors associated with sufficient Results included 116 studies. Eleven out of one hundred sixteen justified size, 6/11 performed a priori calculation. The median (first third quartile, Q1, Q3) total is 223 (130, 463), those training are 150 (90, 288). (Q1, difference between minimum according −100 (−216, 183), differences more restrictive approach based on −268 (−427, −157). presence external testing specialty topic size. Conclusion Radiomics often designed without justification, whose may be too small avoid overfitting. Sample encouraged model. Key Points Question critical help minimize overfitting model, but overlooked underpowered research . Findings Few justified, calculated, or reported their most them did not meet recent formal Clinical relevance justification. Consequently, many It should justify, perform, report considerations

Language: Английский

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Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases

La radiologia medica, Journal Year: 2022, Volume and Issue: 127(7), P. 763 - 772

Published: June 2, 2022

Language: Английский

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Improving radiology workflow using ChatGPT and artificial intelligence

Clinical Imaging, Journal Year: 2023, Volume and Issue: 103, P. 109993 - 109993

Published: Oct. 6, 2023

Language: Английский

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The impact of the novel CovBat harmonization method on enhancing radiomics feature stability and machine learning model performance: A multi-center, multi-device study

European Journal of Radiology, Journal Year: 2025, Volume and Issue: 184, P. 111956 - 111956

Published: Jan. 29, 2025

Language: Английский

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Clinical applications of artificial intelligence in liver imaging

La radiologia medica, Journal Year: 2023, Volume and Issue: 128(6), P. 655 - 667

Published: May 10, 2023

Language: Английский

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Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

Magnetic Resonance in Medical Sciences, Journal Year: 2023, Volume and Issue: 22(4), P. 401 - 414

Published: Jan. 1, 2023

Due primarily to the excellent soft tissue contrast depictions provided by MRI, widespread application of head and neck MRI in clinical practice serves assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition been extensively investigated research for their applicability across a range categories within medical imaging, including MRI. Analytical approaches AI shown potential addressing limitations associated with In this review, we focus on technical advancements deep-learning-based methodologies utility field encompassing aspects such as image acquisition reconstruction, lesion segmentation, disease classification diagnosis, prognostic prediction patients presenting We then discuss current offer insights regarding future challenges field.

Language: Английский

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Quality of radiomics research: comprehensive analysis of 1574 unique publications from 89 reviews

European Radiology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 6, 2024

Language: Английский

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A radiomics signature associated with underlying gene expression pattern for the prediction of prognosis and treatment response in hepatocellular carcinoma

European Journal of Radiology, Journal Year: 2023, Volume and Issue: 167, P. 111086 - 111086

Published: Sept. 6, 2023

Language: Английский

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