基于部分实性肺结节胸部CT影像组学特征的浸润性肺腺癌诊断：一项多中心研究 - Suppr

Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study.

作者信息

Wu Guangyao, Woodruff Henry C, Shen Jing, Refaee Turkey, Sanduleanu Sebastian, Ibrahim Abdalla, Leijenaar Ralph T H, Wang Rui, Xiong Jingtong, Bian Jie, Wu Jianlin, Lambin Philippe

出版信息

Radiology. 2020 Nov;297(2):E282. doi: 10.1148/radiol.2020209019.

DOI:10.1148/radiol.2020209019

PMID:33074784

Abstract

摘要

相似文献

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胸部CT影像组学特征的浸润性肺腺癌诊断：一项多中心研究

Radiology. 2020 Nov;297(2):E282. doi: 10.1148/radiol.2020209019.

Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?预测亚实性结节的恶性潜能：放射组学能否预测纵向随访 CT？

Cancer Imaging. 2019 Jun 10;19(1):36. doi: 10.1186/s40644-019-0223-7.

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.

Computer-aided diagnosis of ground-glass opacity pulmonary nodules using radiomic features analysis.基于放射组学特征分析的磨玻璃密度肺结节计算机辅助诊断

Phys Med Biol. 2019 Jul 5;64(13):135015. doi: 10.1088/1361-6560/ab2757.

Deep Learning-based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses.基于深度学习的 CT 重建核图像转换可提高肺结节或肿块的放射组学可重复性。

Radiology. 2019 Aug;292(2):365-373. doi: 10.1148/radiol.2019181960. Epub 2019 Jun 18.

Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma.基于影像组学的纯磨玻璃结节定量CT分析以预测肺腺癌的侵袭性

Front Oncol. 2020 Aug 11;10:872. doi: 10.3389/fonc.2020.00872. eCollection 2020.

CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features.基于CT成像，利用结节周围和结节内的影像组学特征对表现为磨玻璃结节的浸润性肺腺癌进行分析。

Front Oncol. 2020 May 27;10:838. doi: 10.3389/fonc.2020.00838. eCollection 2020.

Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival.比较放射组学生物标志物与体积分析在解码不同疾病特异性生存的肺腺癌肿瘤表型方面的表现。

Eur Radiol. 2017 Nov;27(11):4857-4865. doi: 10.1007/s00330-017-4855-3. Epub 2017 May 18.

[Value of CT Features on Differential Diagnosis of Pulmonary Subsolid Nodules and Degree of invasion Prediction in Pulmonary Adenocarcinoma].[CT特征在肺亚实性结节鉴别诊断及肺腺癌侵袭程度预测中的价值]

Zhongguo Fei Ai Za Zhi. 2018 Jun 20;21(6):451-457. doi: 10.3779/j.issn.1009-3419.2018.06.05.

Effect of CT window settings on size measurements of the solid component in subsolid nodules: evaluation of prediction efficacy of the degree of pathological malignancy in lung adenocarcinoma.CT 窗宽设置对亚实性结节实性成分大小测量的影响：评估肺腺癌病理恶性程度预测效能。

Br J Radiol. 2018 Jul;91(1088):20180251. doi: 10.1259/bjr.20180251. Epub 2018 Jun 6.

引用本文的文献

Dose and local efficacy analysis of iodine-125 seed implantation therapy for lung tumors.碘-125粒子植入治疗肺癌的剂量及局部疗效分析

Front Oncol. 2025 Jun 25;15:1589325. doi: 10.3389/fonc.2025.1589325. eCollection 2025.

Combining CT radiomics and radiological features to predict pathological grade in stage I lung adenocarcinoma.结合CT影像组学和放射学特征预测Ⅰ期肺腺癌的病理分级

Ir J Med Sci. 2025 Jun 30. doi: 10.1007/s11845-025-03993-6.

Ensemble Machine Learning Classifiers Combining CT Radiomics and Clinical-Radiological Features for Preoperative Prediction of Pathological Invasiveness in Lung Adenocarcinoma Presenting as Part-Solid Nodules: A Multicenter Retrospective Study.基于CT影像组学和临床放射学特征的集成机器学习分类器对部分实性结节型肺腺癌术前病理侵袭性的预测：一项多中心回顾性研究

Technol Cancer Res Treat. 2025 Jan-Dec;24:15330338251351365. doi: 10.1177/15330338251351365. Epub 2025 Jun 17.

The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPA.非增强CT三维可视化在鉴别Ⅰ期浸润性肺腺癌中贴壁型与非贴壁型的价值

Eur J Radiol Open. 2024 Sep 21;13:100600. doi: 10.1016/j.ejro.2024.100600. eCollection 2024 Dec.

Integrated Multi-omics Analysis of Early Lung Adenocarcinoma Links Tumor Biological Features with Predicted Indolence or Aggressiveness.早期肺腺癌的综合多组学分析将肿瘤生物学特征与预测的惰性或侵袭性联系起来。

Cancer Res Commun. 2023 Jul 26;3(7):1350-1365. doi: 10.1158/2767-9764.CRC-22-0373. eCollection 2023 Jul.

Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules.基于人工智能的计算机断层扫描直方图分析预测表现为放射学部分实性结节的肺腺癌的肿瘤侵袭性

Front Oncol. 2023 Feb 23;13:1096453. doi: 10.3389/fonc.2023.1096453. eCollection 2023.

Relationship between the image characteristics of artificial intelligence and EGFR gene mutation in lung adenocarcinoma.人工智能图像特征与肺腺癌表皮生长因子受体基因突变之间的关系

Front Genet. 2023 Jan 4;13:1090180. doi: 10.3389/fgene.2022.1090180. eCollection 2022.

Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features.基于临床和定量影像组学特征构建小实性和亚实性肺结节的风险模型

J Thorac Dis. 2021 Jul;13(7):4156-4168. doi: 10.21037/jtd-21-80.

Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians.肺部疾病影像学中的放射组学：临床医生的最新技术水平

J Pers Med. 2021 Jun 25;11(7):602. doi: 10.3390/jpm11070602.

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics.高级别软组织肉瘤患者的预后评估：语义图像分析与放射组学的比较

Cancers (Basel). 2021 Apr 16;13(8):1929. doi: 10.3390/cancers13081929.

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

Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study.

作者信息

出版信息

相似文献

引用本文的文献

文献检索

文件翻译

深度研究

Suppr 超能文献

相似文献

引用本文的文献