The current and upcoming era of radiomics in phaeochromocytoma and paraganglioma

IF 6.1 1区 医学 Q1 ENDOCRINOLOGY & METABOLISM Best practice & research. Clinical endocrinology & metabolism Pub Date : 2025-01-01 DOI:10.1016/j.beem.2024.101923
Zbyněk Tüdös (Associate Professor in Radiology) , Lucia Veverková (Consultant in Radiology) , Jan Baxa (Professor in Radiology) , Igor Hartmann (Consultant in Urology) , Filip Čtvrtlík (Associate Professor in Radiology)
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Abstract

The topic of the diagnosis of phaeochromocytomas remains highly relevant because of advances in laboratory diagnostics, genetics, and therapeutic options and also the development of imaging methods. Computed tomography still represents an essential tool in clinical practice, especially in incidentally discovered adrenal masses; it allows morphological evaluation, including size, shape, necrosis, and unenhanced attenuation. More advanced post-processing tools to analyse digital images, such as texture analysis and radiomics, are currently being studied. Radiomic features utilise digital image pixels to calculate parameters and relations undetectable by the human eye. On the other hand, the amount of radiomic data requires massive computer capacity. Radiomics, together with machine learning and artificial intelligence in general, has the potential to improve not only the differential diagnosis but also the prediction of complications and therapy outcomes of phaeochromocytomas in the future. Currently, the potential of radiomics and machine learning does not match expectations and awaits its fulfilment.
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phaeochromocytoma 和副神经节瘤放射组学的当前和未来时代。
由于实验室诊断、遗传学和治疗方法的进步,以及成像方法的发展,有关肾上腺绒毛膜细胞瘤诊断的话题仍然具有很强的现实意义。计算机断层扫描仍然是临床实践中必不可少的工具,尤其是在偶然发现肾上腺肿块时;它可以进行形态学评估,包括大小、形状、坏死和未增强衰减。目前正在研究更先进的后处理工具来分析数字图像,如纹理分析和放射组学。放射组学特征利用数字图像像素计算人眼无法检测的参数和关系。另一方面,大量的辐射组学数据需要庞大的计算机容量。放射组学与机器学习和人工智能一起,不仅有可能改善鉴别诊断,还能在未来预测辉细胞瘤的并发症和治疗效果。目前,放射组学和机器学习的潜力尚未达到预期,有待发挥。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.90
自引率
0.00%
发文量
77
审稿时长
6-12 weeks
期刊介绍: Best Practice & Research Clinical Endocrinology & Metabolism is a serial publication that integrates the latest original research findings into evidence-based review articles. These articles aim to address key clinical issues related to diagnosis, treatment, and patient management. Each issue adopts a problem-oriented approach, focusing on key questions and clearly outlining what is known while identifying areas for future research. Practical management strategies are described to facilitate application to individual patients. The series targets physicians in practice or training.
期刊最新文献
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