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1

Corrigendum: Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan.勘误：基于临床放射组学的机器学习与三维卷积神经网络分析在胸部计算机断层扫描上鉴别胸腺上皮肿瘤与其他前纵隔肿瘤的比较。

Front Oncol. 2023 May 31;13:1220962. doi: 10.3389/fonc.2023.1220962. eCollection 2023.

2

Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan.基于临床影像组学的机器学习与三维卷积神经网络分析在胸部计算机断层扫描上鉴别胸腺上皮肿瘤与其他前纵隔肿瘤的比较

Front Oncol. 2023 Apr 18;13:1105100. doi: 10.3389/fonc.2023.1105100. eCollection 2023.

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Corrigendum: Ultrasound-based radiomics analysis for differentiating benign and malignant breast lesions: from static images to CEUS video analysis.勘误：基于超声的影像组学分析用于鉴别乳腺良恶性病变：从静态图像到CEUS视频分析

Front Oncol. 2024 Jun 6;14:1436117. doi: 10.3389/fonc.2024.1436117. eCollection 2024.

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Corrigendum: Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma.勘误：基于机器学习的磁共振影像组学分析用于胶质肉瘤和胶质母细胞瘤的分类

Front Oncol. 2021 Oct 18;11:774369. doi: 10.3389/fonc.2021.774369. eCollection 2021.

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A computer-aided diagnostic framework for coronavirus diagnosis using texture-based radiomics images.一种使用基于纹理的放射组学图像进行冠状病毒诊断的计算机辅助诊断框架。

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CT-Based Radiomics Nomogram for Differentiation of Anterior Mediastinal Thymic Cyst From Thymic Epithelial Tumor.基于CT的影像组学列线图用于鉴别前纵隔胸腺囊肿与胸腺上皮肿瘤

Front Oncol. 2021 Dec 10;11:744021. doi: 10.3389/fonc.2021.744021. eCollection 2021.

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Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas.基于 CT 的放射组学特征可区分前纵隔肿块为胸腺瘤与淋巴瘤。

Radiol Med. 2020 Oct;125(10):951-960. doi: 10.1007/s11547-020-01188-w. Epub 2020 Apr 18.

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CT-Based Radiomics Signatures for Predicting the Risk Categorization of Thymic Epithelial Tumors.基于CT的影像组学特征预测胸腺上皮肿瘤的风险分类

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Machine learning-based radiomic computed tomography phenotyping of thymic epithelial tumors: Predicting pathological and survival outcomes.基于机器学习的胸腺瘤计算机断层扫描影像组学表型分析：预测病理及生存结果

J Thorac Cardiovasc Surg. 2023 Feb;165(2):502-516.e9. doi: 10.1016/j.jtcvs.2022.05.046. Epub 2022 Jul 20.

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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.

勘误：基于临床放射组学的机器学习与三维卷积神经网络分析在胸部计算机断层扫描上鉴别胸腺上皮肿瘤与其他前纵隔肿瘤的比较。

Corrigendum: Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan.

作者信息

Chang Chao-Chun, Tang En-Kuei, Wei Yu-Feng, Lin Chia-Ying, Wu Fu-Zong, Wu Ming-Ting, Liu Yi-Sheng, Yen Yi-Ting, Ma Mi-Chia, Tseng Yau-Lin

机构信息

Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.

Division of Thoracic Surgery, Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.

出版信息

Front Oncol. 2023 May 31;13:1220962. doi: 10.3389/fonc.2023.1220962. eCollection 2023.

DOI:10.3389/fonc.2023.1220962

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10266095/

Abstract

[This corrects the article DOI: 10.3389/fonc.2023.1105100.].

摘要

[本文更正了文章的数字对象标识符：10.3389/fonc.2023.1105100。]