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CT 放射组学鉴别嗜酸细胞瘤与肾细胞癌:系统综述与荟萃分析。

CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis.

机构信息

Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA.

Urology Department, Clinical Center, National Cancer Institutes (NCI), National Institutes of Health, Bethesda, MD, USA.

出版信息

Clin Imaging. 2023 Feb;94:9-17. doi: 10.1016/j.clinimag.2022.11.007. Epub 2022 Nov 17.


DOI:10.1016/j.clinimag.2022.11.007
PMID:36459898
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9812928/
Abstract

BACKGROUND: Radiomics is a type of quantitative analysis that provides a more objective approach to detecting tumor subtypes using medical imaging. The goal of this paper is to conduct a comprehensive assessment of the literature on computed tomography (CT) radiomics for distinguishing renal cell carcinomas (RCCs) from oncocytoma. METHODS: From February 15th 2012 to 2022, we conducted a broad search of the current literature using the PubMed/MEDLINE, Google scholar, Cochrane Library, Embase, and Web of Science. A meta-analysis of radiomics studies concentrating on discriminating between oncocytoma and RCCs was performed, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies method. The pooled sensitivity, specificity, and diagnostic odds ratio were evaluated via a random-effects model, which was applied for the meta-analysis. This study is registered with PROSPERO (CRD42022311575). RESULTS: After screening the search results, we identified 6 studies that utilized radiomics to distinguish oncocytoma from other renal tumors; there were a total of 1064 lesions in 1049 patients (288 oncocytoma lesions vs 776 RCCs lesions). The meta-analysis found substantial heterogeneity among the included studies, with pooled sensitivity and specificity of 0.818 [0.619-0.926] and 0.808 [0.537-0.938], for detecting different subtypes of RCCs (clear cell RCC, chromophobe RCC, and papillary RCC) from oncocytoma. Also, a pooled sensitivity and specificity of 0.83 [0.498-0.960] and 0.92 [0.825-0.965], respectively, was found in detecting oncocytoma from chromophobe RCC specifically. CONCLUSIONS: According to this study, CT radiomics has a high degree of accuracy in distinguishing RCCs from RO, including chromophobe RCCs from RO. Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous. However, in order for this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols as well as inter-institutional sharing of software is warranted.

摘要

背景:放射组学是一种定量分析方法,它为使用医学影像检测肿瘤亚型提供了一种更客观的方法。本文的目的是对使用计算机断层扫描(CT)放射组学区分肾细胞癌(RCC)和嗜酸细胞瘤的文献进行全面评估。

方法:从 2012 年 2 月 15 日至 2022 年,我们使用 PubMed/MEDLINE、Google Scholar、Cochrane 图书馆、Embase 和 Web of Science 广泛搜索了当前的文献。对集中在区分嗜酸细胞瘤和 RCC 方面的放射组学研究进行了荟萃分析,并使用诊断准确性研究质量评估方法评估了偏倚风险。通过随机效应模型评估了汇总敏感性、特异性和诊断优势比,该模型用于荟萃分析。本研究已在 PROSPERO(CRD42022311575)上注册。

结果:在筛选搜索结果后,我们确定了 6 项使用放射组学区分嗜酸细胞瘤和其他肾肿瘤的研究;共有 1049 名患者的 1064 个病变(288 个嗜酸细胞瘤病变和 776 个 RCC 病变)。荟萃分析发现纳入研究之间存在很大的异质性,用于检测不同亚型的 RCC(透明细胞 RCC、嫌色细胞 RCC 和乳头状 RCC)与嗜酸细胞瘤的汇总敏感性和特异性分别为 0.818 [0.619-0.926] 和 0.808 [0.537-0.938]。此外,还发现检测嗜酸细胞瘤与嫌色细胞 RCC 时的汇总敏感性和特异性分别为 0.83 [0.498-0.960] 和 0.92 [0.825-0.965]。

结论:根据这项研究,CT 放射组学在区分 RCC 和 RO,包括区分嫌色细胞 RCC 和 RO 方面具有很高的准确性。放射组学算法有可能改善传统上存在模糊性的诊断情况。然而,为了使该方法在临床环境中得到实施,需要标准化图像采集和分割协议,并在机构间共享软件。

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Life (Basel). 2023-9-23

[8]
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Sci Rep. 2023-8-3

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本文引用的文献

[1]
Renal Oncocytoma: The Diagnostic Challenge to Unmask the Double of Renal Cancer.

Int J Mol Sci. 2022-2-26

[2]
A CT-based radiomics model for predicting renal capsule invasion in renal cell carcinoma.

BMC Med Imaging. 2022-1-30

[3]
A CT-based radiomics nomogram for differentiation of renal oncocytoma and chromophobe renal cell carcinoma with a central scar-matched study.

Br J Radiol. 2022-1-1

[4]
How can SHAP values help to shape metabolic stability of chemical compounds?

J Cheminform. 2021-9-27

[5]
Radiomics in Renal Cell Carcinoma-A Systematic Review and Meta-Analysis.

Cancers (Basel). 2021-3-17

[6]
Distinguishing Benign Renal Tumors with an Oncocytic Gene Expression (ONEX) Classifier.

Eur Urol. 2021-1

[7]
MRI radiomics-based nomogram for individualised prediction of synchronous distant metastasis in patients with clear cell renal cell carcinoma.

Eur Radiol. 2021-2

[8]
Does renal tumor biopsies for small renal carcinoma increase the risk of upstaging on final surgery pathology report and the risk of recurrence?

Urol Oncol. 2020-10

[9]
Machine and deep learning methods for radiomics.

Med Phys. 2020-6

[10]
Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.

Eur Radiol. 2020-9

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