• 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

相似文献

1
Dependence of radiomic features on pixel size affects the diagnostic performance of radiomic signature for the invasiveness of pulmonary ground-glass nodule.基于像素大小的影像组学特征依赖性影响肺部磨玻璃结节侵袭性的影像组学特征的诊断性能。
Br J Radiol. 2021 Feb 1;94(1118):20200089. doi: 10.1259/bjr.20200089. Epub 2020 Dec 22.
2
A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules.一项基于 CT 的语义和放射组学特征在术前诊断表现为亚实性结节的浸润性肺腺癌的对比研究。
Sci Rep. 2021 Jan 18;11(1):66. doi: 10.1038/s41598-020-79690-4.
3
Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study.基于部分实性肺结节 CT 放射组学特征对浸润性肺腺癌的诊断:一项多中心研究。
Radiology. 2020 Nov;297(2):451-458. doi: 10.1148/radiol.2020192431. Epub 2020 Aug 25.
4
A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.基于深度残差学习的 CT 图像磨玻璃结节肺腺癌预测网络
Eur Radiol. 2020 Apr;30(4):1847-1855. doi: 10.1007/s00330-019-06533-w. Epub 2019 Dec 6.
5
A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules.用于确定表现为部分实性结节的孤立性肺结节侵袭性的放射组学模型。
Clin Radiol. 2019 Dec;74(12):933-943. doi: 10.1016/j.crad.2019.07.026. Epub 2019 Sep 11.
6
A comparative study for the evaluation of CT-based conventional, radiomic, combined conventional and radiomic, and delta-radiomic features, and the prediction of the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules.基于 CT 的常规、放射组学、常规联合放射组学和 delta 放射组学特征评估的对比研究,以及预测表现为磨玻璃结节的肺腺癌侵袭性。
Clin Radiol. 2022 Oct;77(10):e741-e748. doi: 10.1016/j.crad.2022.06.004. Epub 2022 Jul 12.
7
Lung Adenocarcinoma Invasiveness Risk in Pure Ground-Glass Opacity Lung Nodules Smaller than 2 cm.直径小于2cm的纯磨玻璃密度肺结节的肺腺癌侵袭风险
Thorac Cardiovasc Surg. 2019 Jun;67(4):321-328. doi: 10.1055/s-0037-1612615. Epub 2018 Jan 22.
8
A computerized tomography-based radiomic model for assessing the invasiveness of lung adenocarcinoma manifesting as ground-glass opacity nodules.基于计算机断层扫描的放射组学模型评估表现为磨玻璃密度结节的肺腺癌侵袭性。
Respir Res. 2022 Apr 16;23(1):96. doi: 10.1186/s12931-022-02016-7.
9
The Value of Topological Radiomics Analysis in Predicting Malignant Risk of Pulmonary Ground-Glass Nodules: A Multi-Center Study.基于拓扑特征的影像组学分析对预测肺磨玻璃结节恶性风险的价值:多中心研究
Technol Cancer Res Treat. 2024 Jan-Dec;23:15330338241287089. doi: 10.1177/15330338241287089.
10
Contrast analysis of the relationship between the HRCT sign and new pathologic classification in small ground glass nodule-like lung adenocarcinoma.小磨玻璃结节样肺腺癌 HRCT 征象与新病理分类的相关性对比分析。
Radiol Med. 2019 Jan;124(1):8-13. doi: 10.1007/s11547-018-0936-x. Epub 2018 Sep 6.

引用本文的文献

1
Deep learning-based super-resolution US radiomics to differentiate testicular seminoma and non-seminoma: an international multicenter study.基于深度学习的超分辨率超声放射组学鉴别睾丸精原细胞瘤和非精原细胞瘤:一项国际多中心研究
Insights Imaging. 2025 Aug 1;16(1):165. doi: 10.1186/s13244-025-02045-y.
2
A metabolomics study on carcinogenesis of ground-glass nodules.磨玻璃结节癌变的代谢组学研究
Cytojournal. 2024 Mar 18;21:12. doi: 10.25259/Cytojournal_68_2023. eCollection 2024.
3
Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix.肺浸润性腺癌实性和微乳头状成分的预测:高空间分辨率 CT 数据的 1024 矩阵的放射组学分析。
Jpn J Radiol. 2024 Jun;42(6):590-598. doi: 10.1007/s11604-024-01534-2. Epub 2024 Feb 28.
4
Longitudinal prediction of lung nodule invasiveness by sequential modelling with common clinical computed tomography (CT) measurements: a prediction accuracy study.通过使用常见临床计算机断层扫描(CT)测量值的序列建模对肺结节侵袭性进行纵向预测:一项预测准确性研究。
Transl Lung Cancer Res. 2022 May;11(5):845-857. doi: 10.21037/tlcr-22-319.
5
Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study.利用深度学习通过随访计算机断层扫描对肺结节未来影像进行预测作为生长建模:一项回顾性队列研究
Transl Lung Cancer Res. 2022 Feb;11(2):250-262. doi: 10.21037/tlcr-22-59.

本文引用的文献

1
Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma.基于机器学习的多参数 MRI 放射组学预测甲状腺乳头状癌侵袭性。
Eur J Radiol. 2020 Jan;122:108755. doi: 10.1016/j.ejrad.2019.108755. Epub 2019 Nov 20.
2
Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma.基于影像组学工具的鼻咽癌图像分析和临床预测比较。
Br J Radiol. 2019 Oct;92(1102):20190271. doi: 10.1259/bjr.20190271. Epub 2019 Aug 27.
3
Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis.基于 CT 图像特征分析的机器学习算法预测非小细胞肺癌病理分期。
BMC Cancer. 2019 May 17;19(1):464. doi: 10.1186/s12885-019-5646-9.
4
Differentiating minimally invasive and invasive adenocarcinomas in patients with solitary sub-solid pulmonary nodules with a radiomics nomogram.基于影像组学列线图鉴别单发亚实性肺结节患者的微创与浸润性腺癌
Clin Radiol. 2019 Jul;74(7):570.e1-570.e11. doi: 10.1016/j.crad.2019.03.018. Epub 2019 May 2.
5
High-Resolution Chest Computed Tomography Imaging of the Lungs: Impact of 1024 Matrix Reconstruction and Photon-Counting Detector Computed Tomography.肺部高分辨率胸部计算机断层成像:1024 矩阵重建和光子计数探测器计算机断层成像的影响。
Invest Radiol. 2019 Mar;54(3):129-137. doi: 10.1097/RLI.0000000000000524.
6
A simple prediction model using size measures for discrimination of invasive adenocarcinomas among incidental pulmonary subsolid nodules considered for resection.用于鉴别偶然发现的拟切除肺部亚实性结节中浸润性腺癌的大小指标的简单预测模型。
Eur Radiol. 2019 Apr;29(4):1674-1683. doi: 10.1007/s00330-018-5739-x. Epub 2018 Sep 25.
7
Voxel size and gray level normalization of CT radiomic features in lung cancer.肺癌 CT 影像组学特征的体素大小和灰度值归一化。
Sci Rep. 2018 Jul 12;8(1):10545. doi: 10.1038/s41598-018-28895-9.
8
Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule.影像组学特征:一种术前鉴别表现为磨玻璃结节的肺浸润性腺癌的生物标志物。
Eur Radiol. 2019 Feb;29(2):889-897. doi: 10.1007/s00330-018-5530-z. Epub 2018 Jul 2.
9
Screening for Lung Cancer: CHEST Guideline and Expert Panel Report.肺癌筛查:CHEST 指南和专家小组报告。
Chest. 2018 Apr;153(4):954-985. doi: 10.1016/j.chest.2018.01.016. Epub 2018 Feb 17.
10
Computational Radiomics System to Decode the Radiographic Phenotype.用于解码影像学表型的计算放射组学系统
Cancer Res. 2017 Nov 1;77(21):e104-e107. doi: 10.1158/0008-5472.CAN-17-0339.

基于像素大小的影像组学特征依赖性影响肺部磨玻璃结节侵袭性的影像组学特征的诊断性能。

Dependence of radiomic features on pixel size affects the diagnostic performance of radiomic signature for the invasiveness of pulmonary ground-glass nodule.

机构信息

Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China.

Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.

出版信息

Br J Radiol. 2021 Feb 1;94(1118):20200089. doi: 10.1259/bjr.20200089. Epub 2020 Dec 22.

DOI:10.1259/bjr.20200089
PMID:33353396
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7934291/
Abstract

OBJECTIVE

To investigate the effect of reducing pixel size on the consistency of radiomic features and the diagnostic performance of the downstream radiomic signatures for the invasiveness for pulmonary ground-glass nodules (GGNs) on CTs.

METHODS

We retrospectively collected the clinical data of 182 patients with GGNs on high resolution CT (HRCT). The CT images of different pixel sizes (0.8mm, 0.4mm, 0.18 mm) were obtained by reconstructing the single HRCT scan using three combinations of field of view and matrix size. For each pixel size setting, radiomic features were extracted for all GGNs and radiomic signatures for the invasiveness of GGNs were built through two modeling pipelines for comparison.

RESULTS

The study finally extracted 788 radiomic features. 87% radiomic features demonstrated inter pixel size variation. By either modeling pipeline, the radiomic signature under small pixel size performed significantly better than those under middle or large pixel sizes in predicting the invasiveness of GGNs ('s value <0.05 by Delong test). With the independent modeling pipeline, the three pixel size bounded radiomic signatures shared almost no common features.

CONCLUSIONS

Reducing pixel size could cause inconsistency in most radiomic features and improve the diagnostic performance of the downstream radiomic signatures. Particularly, super HRCTs with small pixel size resulted in more accurate radiomic signatures for the invasiveness of GGNs.

ADVANCES IN KNOWLEDGE

The dependence of radiomic features on pixel size will affect the performance of the downstream radiomic signatures. The future radiomic studies should consider this effect of pixel size.

摘要

目的

探究降低像素尺寸对 CT 上肺磨玻璃结节(GGN)侵袭性的放射组学特征一致性和下游放射组学特征诊断性能的影响。

方法

我们回顾性收集了 182 例高分辨率 CT(HRCT)上 GGN 的临床资料。通过三种视野和矩阵大小的组合,对单次 HRCT 扫描进行重建,获得不同像素尺寸(0.8mm、0.4mm、0.18mm)的 CT 图像。对于每个像素尺寸设置,从所有 GGN 中提取放射组学特征,并通过两种建模管道构建 GGN 侵袭性的放射组学特征进行比较。

结果

本研究最终提取了 788 个放射组学特征。87%的放射组学特征表现出像素尺寸间的差异。通过两种建模管道,小像素尺寸下的放射组学特征在预测 GGN 的侵袭性方面均显著优于中或大像素尺寸下的特征(DeLong 检验's 值<0.05)。通过独立建模管道,三种像素尺寸限定的放射组学特征几乎没有共享的特征。

结论

降低像素尺寸会导致大多数放射组学特征不一致,并提高下游放射组学特征的诊断性能。特别是,小像素尺寸的超高分辨率 CT 可获得更准确的 GGN 侵袭性放射组学特征。

知识进展

放射组学特征对像素尺寸的依赖性会影响下游放射组学特征的性能。未来的放射组学研究应考虑像素尺寸的这种影响。