Radiomics and Artificial Intelligence in Renal Lesion Assessment.

Q4 Biochemistry, Genetics and Molecular Biology Critical Reviews in Oncogenesis Pub Date : 2024-01-01 DOI:10.1615/CritRevOncog.2023051084
Michaela Cellina, Giovanni Irmici, Gianmarco Della Pepa, Maurizio Ce, Vittoria Chiarpenello, Marco Alì, Sergio Papa, Gianpaolo Carrafiello
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Abstract

Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments. AI models have shown remarkable accuracy in predicting tumor aggressiveness, treatment response, and patient outcomes. This review provides insights into the current state of radiomics and AI in renal lesion assessment and outlines future directions for research in this rapidly evolving field.

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放射组学和人工智能在肾脏病变评估中的应用
放射组学是从医学影像中提取和分析定量特征,它已成为放射学中一个前景广阔的领域,有望彻底改变肾脏病变的诊断和管理。本综述探讨了放射组学的工作流程,包括图像采集、特征提取、选择和分类,并重点介绍了其在区分肾脏良性和恶性病变方面的应用。将放射组学与机器学习和深度学习等人工智能(AI)技术相结合,有助于患者的管理,并可规划适当的治疗。人工智能模型在预测肿瘤侵袭性、治疗反应和患者预后方面表现出极高的准确性。本综述深入探讨了放射组学和人工智能在肾脏病变评估中的应用现状,并概述了这一快速发展领域的未来研究方向。
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来源期刊
Critical Reviews in Oncogenesis
Critical Reviews in Oncogenesis Biochemistry, Genetics and Molecular Biology-Cancer Research
CiteScore
1.70
自引率
0.00%
发文量
17
期刊介绍: The journal is dedicated to extensive reviews, minireviews, and special theme issues on topics of current interest in basic and patient-oriented cancer research. The study of systems biology of cancer with its potential for molecular level diagnostics and treatment implies competence across the sciences and an increasing necessity for cancer researchers to understand both the technology and medicine. The journal allows readers to adapt a better understanding of various fields of molecular oncology. We welcome articles on basic biological mechanisms relevant to cancer such as DNA repair, cell cycle, apoptosis, angiogenesis, tumor immunology, etc.
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Preface: Artificial Intelligence and the Revolution of Oncological Imaging. Radiomics and Artificial Intelligence in Renal Lesion Assessment. Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review. Disparities in Electronic Cigarette Use: A Narrative Review. Preface.
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