• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

放射组学工具在肾肿物特征描述中的应用。

The Use of Radiomic Tools in Renal Mass Characterization.

作者信息

Gutiérrez Hidalgo Beatriz, Gómez Rivas Juan, de la Parra Irene, Marugán María Jesús, Serrano Álvaro, Hermida Gutiérrez Juan Fco, Barrera Jerónimo, Moreno-Sierra Jesús

机构信息

Department of Urology, Clínico San Carlos Hospital, Health Research Institute of Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain.

Radiodiagnosis Department, Clínico San Carlos Hospital, Complutense University, 28040 Madrid, Spain.

出版信息

Diagnostics (Basel). 2023 Aug 24;13(17):2743. doi: 10.3390/diagnostics13172743.

DOI:10.3390/diagnostics13172743
PMID:37685281
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10487148/
Abstract

The incidence of renal mass detection has increased during recent decades, with an increased diagnosis of small renal masses, and a final benign diagnosis in some cases. To avoid unnecessary surgeries, there is an increasing interest in using radiomics tools to predict histological results, using radiological features. We performed a narrative review to evaluate the use of radiomics in renal mass characterization. Conventional images, such as computed tomography (CT) and magnetic resonance (MR), are the most common diagnostic tools in renal mass characterization. Distinguishing between benign and malignant tumors in small renal masses can be challenging using conventional methods. To improve subjective evaluation, the interest in using radiomics to obtain quantitative parameters from medical images has increased. Several studies have assessed this novel tool for renal mass characterization, comparing its ability to distinguish benign to malign tumors, the results in differentiating renal cell carcinoma subtypes, or the correlation with prognostic features, with other methods. In several studies, radiomic tools have shown a good accuracy in characterizing renal mass lesions. However, due to the heterogeneity in the radiomic model building, prospective and external validated studies are needed.

摘要

近几十年来,肾脏肿块的检出率有所上升,小肾脏肿块的诊断增多,且部分病例最终诊断为良性。为避免不必要的手术,利用放射组学工具通过放射学特征预测组织学结果的兴趣日益浓厚。我们进行了一项叙述性综述,以评估放射组学在肾脏肿块特征描述中的应用。传统影像,如计算机断层扫描(CT)和磁共振成像(MR),是肾脏肿块特征描述中最常用的诊断工具。使用传统方法区分小肾脏肿块中的良性和恶性肿瘤可能具有挑战性。为了改善主观评估,利用放射组学从医学影像中获取定量参数的兴趣有所增加。多项研究评估了这种用于肾脏肿块特征描述的新工具,将其区分良性与恶性肿瘤的能力、区分肾细胞癌亚型的结果或与预后特征的相关性与其他方法进行了比较。在多项研究中,放射组学工具在描述肾脏肿块病变方面显示出良好的准确性。然而,由于放射组学模型构建的异质性,需要进行前瞻性和外部验证研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/061b/10487148/1567bbce0527/diagnostics-13-02743-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/061b/10487148/1567bbce0527/diagnostics-13-02743-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/061b/10487148/1567bbce0527/diagnostics-13-02743-g001.jpg

相似文献

1
The Use of Radiomic Tools in Renal Mass Characterization.放射组学工具在肾肿物特征描述中的应用。
Diagnostics (Basel). 2023 Aug 24;13(17):2743. doi: 10.3390/diagnostics13172743.
2
A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma.一种基于放射组学的机器学习算法,用于可靠地区分良性肾肿块与肾细胞癌。
Eur Urol Focus. 2022 Jul;8(4):988-994. doi: 10.1016/j.euf.2021.09.004. Epub 2021 Sep 16.
3
Differentiation of benign from malignant solid renal lesions with MRI-based radiomics and machine learning.基于 MRI 的放射组学和机器学习对良恶性实体性肾病变的鉴别诊断。
Abdom Radiol (NY). 2022 Aug;47(8):2896-2904. doi: 10.1007/s00261-022-03577-3. Epub 2022 Jun 20.
4
Radiomics to better characterize small renal masses.利用放射组学更好地描述小肾肿瘤的特征。
World J Urol. 2021 Aug;39(8):2861-2868. doi: 10.1007/s00345-021-03602-y. Epub 2021 Jan 26.
5
Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation.基于CT的影像组学和机器学习对肾脏实性病变良恶性的鉴别:与放射科医生解读的比较
Abdom Radiol (NY). 2023 Feb;48(2):642-648. doi: 10.1007/s00261-022-03735-7. Epub 2022 Nov 12.
6
Characterization of solid renal neoplasms using MRI-based quantitative radiomics features.基于 MRI 的定量放射组学特征对肾脏实体瘤的特征描述。
Abdom Radiol (NY). 2020 Sep;45(9):2840-2850. doi: 10.1007/s00261-020-02540-4.
7
Multi-marker quantitative radiomics for mass characterization in dedicated breast CT imaging.专用乳腺CT成像中用于肿块特征描述的多标记定量放射组学
Med Phys. 2021 Jan;48(1):313-328. doi: 10.1002/mp.14610. Epub 2020 Dec 10.
8
Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.基于形状和纹理的 CT 放射组学特征可有效区分良恶性肾肿块。
Eur Radiol. 2021 Feb;31(2):1011-1021. doi: 10.1007/s00330-020-07158-0. Epub 2020 Aug 15.
9
Discriminating malignant and benign clinical T1 renal masses on computed tomography: A pragmatic radiomics and machine learning approach.基于计算机断层扫描鉴别临床T1期肾肿块的良恶性:一种实用的放射组学和机器学习方法。
Medicine (Baltimore). 2020 Apr;99(16):e19725. doi: 10.1097/MD.0000000000019725.
10
Radiomics and Artificial Intelligence for Renal Mass Characterization.用于肾肿物特征描述的放射组学与人工智能
Radiol Clin North Am. 2020 Sep;58(5):995-1008. doi: 10.1016/j.rcl.2020.06.001. Epub 2020 Jul 16.

引用本文的文献

1
Utilisation of artificial intelligence to enhance the detection rates of renal cancer on cross-sectional imaging: protocol for a systematic review and meta-analysis.利用人工智能提高横断面成像中肾癌的检出率:系统评价与荟萃分析方案
BMJ Open. 2025 Aug 31;15(8):e090422. doi: 10.1136/bmjopen-2024-090422.
2
Ultrasound contrast-enhanced radiomics model for preoperative prediction of the tumor grade of clear cell renal cell carcinoma: an exploratory study.超声造影放射组学模型术前预测透明细胞肾细胞癌肿瘤分级的探索性研究。
BMC Med Imaging. 2024 Jun 6;24(1):135. doi: 10.1186/s12880-024-01317-1.

本文引用的文献

1
A CT-based radiomics nomogram for differentiation of benign and malignant small renal masses (≤4 cm).基于CT的鉴别肾小肿块(≤4 cm)良恶性的影像组学列线图。
Transl Oncol. 2023 Mar;29:101627. doi: 10.1016/j.tranon.2023.101627. Epub 2023 Jan 31.
2
Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation.基于CT的影像组学和机器学习对肾脏实性病变良恶性的鉴别:与放射科医生解读的比较
Abdom Radiol (NY). 2023 Feb;48(2):642-648. doi: 10.1007/s00261-022-03735-7. Epub 2022 Nov 12.
3
Artificial intelligence for renal cancer: From imaging to histology and beyond.
用于肾癌的人工智能:从影像学到组织学及其他领域。
Asian J Urol. 2022 Jul;9(3):243-252. doi: 10.1016/j.ajur.2022.05.003. Epub 2022 Jun 18.
4
Differentiation of benign from malignant solid renal lesions with MRI-based radiomics and machine learning.基于 MRI 的放射组学和机器学习对良恶性实体性肾病变的鉴别诊断。
Abdom Radiol (NY). 2022 Aug;47(8):2896-2904. doi: 10.1007/s00261-022-03577-3. Epub 2022 Jun 20.
5
Diagnosis and Treatment of Small Renal Masses: Where Do We Stand?小肾肿块的诊断和治疗:我们处于什么位置?
Curr Urol Rep. 2022 Jun;23(6):99-111. doi: 10.1007/s11934-022-01093-x. Epub 2022 May 4.
6
Apparent Diffusion Coefficient Map-Based Texture Analysis for the Differentiation of Chromophobe Renal Cell Carcinoma from Renal Oncocytoma.基于表观扩散系数图的纹理分析在鉴别嫌色性肾细胞癌与肾嗜酸细胞瘤中的应用
Diagnostics (Basel). 2022 Mar 26;12(4):817. doi: 10.3390/diagnostics12040817.
7
[Analysis of renal tumor size as a predictive factor of oncological aggressiveness.].[肾肿瘤大小作为肿瘤侵袭性预测因素的分析。]
Arch Esp Urol. 2022 Apr;75(3):248-255.
8
Renal Oncocytoma: The Diagnostic Challenge to Unmask the Double of Renal Cancer.肾嗜酸细胞瘤:揭开肾癌“双重伪装”的诊断挑战。
Int J Mol Sci. 2022 Feb 26;23(5):2603. doi: 10.3390/ijms23052603.
9
Use of artificial intelligence to characterize renal tumors.使用人工智能对肾肿瘤进行特征描述。
Investig Clin Urol. 2022 Mar;63(2):123-125. doi: 10.4111/icu.20220051.
10
Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review.肾肿物特征的新型成像方法:一项协作性综述
Eur Urol. 2022 May;81(5):476-488. doi: 10.1016/j.eururo.2022.01.040. Epub 2022 Feb 22.