• 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

勘误:基于3.0T多参数磁共振成像的影像组学机器学习及外部验证用于预测不同比例的前列腺导管内癌

Corrigendum: Radiomic machine learning and external validation based on 3.0T mpMRI for prediction of intraductal carcinoma of prostate with different proportion.

作者信息

Yang Ling, Li Zhengyan, Liang Xu, Xu Jingxu, Cai Yusen, Huang Chencui, Zhang Mengni, Yao Jin, Song Bin

机构信息

Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.

Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.

出版信息

Front Oncol. 2024 Apr 3;14:1401121. doi: 10.3389/fonc.2024.1401121. eCollection 2024.

DOI:10.3389/fonc.2024.1401121
PMID:38634050
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11022688/
Abstract

[This corrects the article DOI: 10.3389/fonc.2022.934291.].

摘要

[本文更正了文章的数字对象标识符:10.3389/fonc.2022.934291。]

相似文献

1
Corrigendum: Radiomic machine learning and external validation based on 3.0T mpMRI for prediction of intraductal carcinoma of prostate with different proportion.勘误:基于3.0T多参数磁共振成像的影像组学机器学习及外部验证用于预测不同比例的前列腺导管内癌
Front Oncol. 2024 Apr 3;14:1401121. doi: 10.3389/fonc.2024.1401121. eCollection 2024.
2
Radiomic Machine Learning and External Validation Based on 3.0 T mpMRI for Prediction of Intraductal Carcinoma of Prostate With Different Proportion.基于3.0 T磁共振多参数成像的影像组学机器学习及外部验证对不同比例前列腺导管内癌的预测
Front Oncol. 2022 Jun 28;12:934291. doi: 10.3389/fonc.2022.934291. eCollection 2022.
3
Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features.比较机器学习算法,使用临床评估类别和放射组学特征,预测多参数 MRI 下外周带具有临床意义的前列腺癌。
Eur Radiol. 2020 Dec;30(12):6757-6769. doi: 10.1007/s00330-020-07064-5. Epub 2020 Jul 16.
4
Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.磁共振成像放射组学预测术前腋窝淋巴结转移以支持手术决策,并与浸润性乳腺癌的肿瘤微环境相关:一项机器学习、多中心研究。
EBioMedicine. 2021 Jul;69:103460. doi: 10.1016/j.ebiom.2021.103460. Epub 2021 Jul 4.
5
Integrative Machine Learning Prediction of Prostate Biopsy Results From Negative Multiparametric MRI.整合机器学习预测阴性多参数 MRI 的前列腺活检结果。
J Magn Reson Imaging. 2022 Jan;55(1):100-110. doi: 10.1002/jmri.27793. Epub 2021 Jun 23.
6
Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy.利用PSMA PET和多参数MRI中的放射组学特征检测局限性前列腺癌以进行生物靶向放射治疗。
EJNMMI Res. 2023 Apr 26;13(1):34. doi: 10.1186/s13550-023-00984-5.
7
Multiparametric Magnetic Resonance Imaging-Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension With Prostate Cancer.基于多参数磁共振成像的肿瘤周围放射组学在前列腺癌中预测囊外扩展存在的术前预测。
J Magn Reson Imaging. 2021 Oct;54(4):1222-1230. doi: 10.1002/jmri.27678. Epub 2021 May 10.
8
Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma.勘误: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 Oct 30;10:608365. doi: 10.3389/fonc.2020.608365. eCollection 2020.
9
Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters.用于前列腺癌特征描述的多参数磁共振成像:影像组学模型与前列腺影像报告和数据系统(PI-RADS)及临床参数的联合应用
Cancers (Basel). 2020 Jul 2;12(7):1767. doi: 10.3390/cancers12071767.
10
Multimodal radiomics based on 18F-Prostate-specific membrane antigen-1007 PET/CT and multiparametric MRI for prostate cancer extracapsular extension prediction.基于 18F-前列腺特异膜抗原-1007 PET/CT 和多参数 MRI 的多模态放射组学预测前列腺癌外膜侵犯。
Br J Radiol. 2024 Feb 2;97(1154):408-414. doi: 10.1093/bjr/tqad038.