Radiomics Features Extracted From Pre- and Postprocedural Imaging in Early Prediction of Treatment Response in Patients Undergoing Transarterial Radioembolization of Hepatic Lesions: A Systematic Review, Meta-Analysis, and Quality Appraisal Study

Mirza-Aghazadeh-Attari, Mohammad; Srinivas, Tara; Kamireddy, Arun; Kim, Alan; Weiss, Clifford R.

doi:10.1016/j.jacr.2023.12.029

Journal of the American College of Radiology

2024

DOI: 10.1016/j.jacr.2023.12.029

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Radiomics Features Extracted From Pre- and Postprocedural Imaging in Early Prediction of Treatment Response in Patients Undergoing Transarterial Radioembolization of Hepatic Lesions: A Systematic Review, Meta-Analysis, and Quality Appraisal Study

Mohammad Mirza-Aghazadeh-Attari,

Tara Srinivas,

Arun Kamireddy

et al.

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Cited by 4 publications

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Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature

Matsui,

Ueda,

Fujita

et al. 2024

Jpn J Radiol

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Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intelligence (AI) in interventional oncology has garnered significant attention. This narrative review describes the current state of AI applications in interventional oncology based on recent literature. A literature search revealed a rapid increase in the number of studies relevant to this topic recently. Investigators have attempted to use AI for various tasks, including automatic segmentation of organs, tumors, and treatment areas; treatment simulation; improvement of intraprocedural image quality; prediction of treatment outcomes; and detection of post-treatment recurrence. Among these, the AI-based prediction of treatment outcomes has been the most studied. Various deep and conventional machine learning algorithms have been proposed for these tasks. Radiomics has often been incorporated into prediction and detection models. Current literature suggests that AI is potentially useful in various aspects of interventional oncology, from treatment planning to post-treatment follow-up. However, most AI-based methods discussed in this review are still at the research stage, and few have been implemented in clinical practice. To achieve widespread adoption of AI technologies in interventional oncology procedures, further research on their reliability and clinical utility is necessary. Nevertheless, considering the rapid research progress in this field, various AI technologies will be integrated into interventional oncology practices in the near future.

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Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature

Matsui,

Ueda,

Fujita

et al. 2024

Jpn J Radiol

View full text Add to dashboard Cite

show abstract

CLEAR guideline for radiomics: Early insights into current reporting practices endorsed by EuSoMII

Kocak,

Ponsiglione,

Stanzione

et al. 2024

European Journal of Radiology

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The Evolution of Interventional Radiology: How Far Will the Apple Land From the Tree?

Trivedi,

Young

2024

Journal of the American College of Radiology

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Radiomics Features Extracted From Pre- and Postprocedural Imaging in Early Prediction of Treatment Response in Patients Undergoing Transarterial Radioembolization of Hepatic Lesions: A Systematic Review, Meta-Analysis, and Quality Appraisal Study

Cited by 4 publications

References 47 publications

Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature

Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature

CLEAR guideline for radiomics: Early insights into current reporting practices endorsed by EuSoMII

The Evolution of Interventional Radiology: How Far Will the Apple Land From the Tree?

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