A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning

A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning

Abstract To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent th...

Bibliográfalaš dieđut
Váldodahkkit:	Huibin Tan, Ye Wang, Yuanliang Jiang, Hanhan Li, Tao You, Tingting Fu, Jiaheng Peng, Yuxi Tan, Ran Lu, Biwen Peng, Wencai Huang, Fei Xiong
Materiálatiipa:	Artihkal
Giella:	English
Almmustuhtton:	Nature Portfolio 2023-04-01
Ráidu:	Scientific Reports
Liŋkkat:	https://doi.org/10.1038/s41598-023-32979-6

Geahča maid

Predicting pathological grade of stage I pulmonary adenocarcinoma: a CT radiomics approach
Dahkki: Xiaoni Huang, et al.
Almmustuhtton: (2024-09-01)

Tuberculous Granuloma
Dahkki: T M Uma Maheswari, et al.
Almmustuhtton: (2011-01-01)

Radiomics combined with clinical features in distinguishing non-calcifying tuberculosis granuloma and lung adenocarcinoma in small pulmonary nodules
Dahkki: Qing Dong, et al.
Almmustuhtton: (2022-10-01)

Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule
Dahkki: Yaoyao Zhuo, et al.
Almmustuhtton: (2021-10-01)

Comparison of Radiomic Models Based on Low-Dose and Standard-Dose CT for Prediction of Adenocarcinomas and Benign Lesions in Solid Pulmonary Nodules
Dahkki: Jieke Liu, et al.
Almmustuhtton: (2021-02-01)

Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features
Dahkki: Li Yi, et al.
Almmustuhtton: (2022-09-01)

The adding value of contrast-enhanced CT radiomics: Differentiating tuberculosis from non-tuberculous infectious lesions presenting as solid pulmonary nodules or masses
Dahkki: Wenjing Zhao, et al.
Almmustuhtton: (2022-10-01)

Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules
Dahkki: Yuting Zheng, et al.
Almmustuhtton: (2023-08-01)

Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
Dahkki: Fangyi Xu, et al.
Almmustuhtton: (2020-08-01)

Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma
Dahkki: Shuai ZHANG, et al.
Almmustuhtton: (2024-11-01)

Machine learning‐based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis
Dahkki: Yuan Li, et al.
Almmustuhtton: (2024-02-01)

CT radiomics to differentiate neuroendocrine neoplasm from adenocarcinoma in patients with a peripheral solid pulmonary nodule: a multicenter study
Dahkki: Xiaoyu Liu, et al.
Almmustuhtton: (2024-06-01)

Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas
Dahkki: Hui Feng, et al.
Almmustuhtton: (2023-02-01)

Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
Dahkki: Fangyi Xu, et al.
Almmustuhtton: (2020-10-01)

Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan
Dahkki: Xianwu Xia, et al.
Almmustuhtton: (2020-03-01)

Peri- and intra-nodular radiomic features based on 18F-FDG PET/CT to distinguish lung adenocarcinomas from pulmonary granulomas
Dahkki: Congna Tian, et al.
Almmustuhtton: (2024-08-01)

Development and validation of a preoperative CT‑based radiomics nomogram to differentiate tuberculosis granulomas from lung adenocarcinomas: an external validation study
Dahkki: Liping Yang, et al.
Almmustuhtton: (2024-06-01)

Stratified differentiation of 3 common pulmonary nodules with CT radiomics
Dahkki: MU Ke, et al.
Almmustuhtton: (2024-03-01)

Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact
Dahkki: Yining Jiang, et al.
Almmustuhtton: (2021-01-01)

The progress of radiomics in thyroid nodules
Dahkki: XiaoFan Gao, et al.
Almmustuhtton: (2023-03-01)

Radiomics for identifying lung adenocarcinomas with predominant lepidic growth manifesting as large pure ground-glass nodules on CT images.
Dahkki: Ziqi Xiong, et al.
Almmustuhtton: (2022-01-01)

Use of CT-derived radiomic features to preoperatively identify invasive mucinous adenocarcinoma in solitary pulmonary nodules ≤3 cm
Dahkki: Zhengyuan Xiao, et al.
Almmustuhtton: (2024-05-01)

Value of Whole-Thyroid CT-Based Radiomics in Predicting Benign and Malignant Thyroid Nodules
Dahkki: Han Xu, et al.
Almmustuhtton: (2022-05-01)

Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule
Dahkki: Jiabi Zhao, et al.
Almmustuhtton: (2021-11-01)

Artificial intelligence assisted discrimination between pulmonary tuberculous nodules and solid lung cancer nodules
Dahkki: Shihan Zeng, et al.
Almmustuhtton: (2022-12-01)

Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT
Dahkki: Minmini Selvam, et al.
Almmustuhtton: (2024-12-01)

Research progress on immunometabolism regulatory mechanisms of tuberculous granuloma
Dahkki: Yang Hua, et al.
Almmustuhtton: (2024-06-01)

L-Tyrosine Limits Mycobacterial Survival in Tuberculous Granuloma
Dahkki: Yaxian Gao, et al.
Almmustuhtton: (2023-04-01)

Tuberculous Granuloma: Emerging Insights From Proteomics and Metabolomics
Dahkki: Abisola Regina Sholeye, et al.
Almmustuhtton: (2022-03-01)

A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules
Dahkki: Yun-Ju Wu, et al.
Almmustuhtton: (2021-01-01)

CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features
Dahkki: Linyu Wu, et al.
Almmustuhtton: (2020-05-01)

Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodules
Dahkki: Junjie Zhang, et al.
Almmustuhtton: (2023-03-01)

Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter
Dahkki: Lili Shi, et al.
Almmustuhtton: (2021-04-01)

CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules
Dahkki: Jing-Xi Sun, et al.
Almmustuhtton: (2025-02-01)

The diagnostic value of CT-based radiomics nomogram for solitary indeterminate smoothly marginated solid pulmonary nodules
Dahkki: Chengzhou Zhang, et al.
Almmustuhtton: (2024-07-01)

Analysis of a simple mathematical model describing tuberculous granuloma
Dahkki: Yuqi Jin, et al.
Almmustuhtton: (2023-11-01)

Analysis of a simple mathematical model describing tuberculous granuloma
Dahkki: Yuqi Jin, et al.
Almmustuhtton: (2023-11-01)

Tuberculous granuloma formation is enhanced by a mycobacterium virulence determinant.
Dahkki: Hannah E Volkman, et al.
Almmustuhtton: (2004-11-01)

Radiomics signature on CECT as a predictive factor for invasiveness of lung adenocarcinoma manifesting as subcentimeter ground glass nodules
Dahkki: Wufei Chen, et al.
Almmustuhtton: (2021-02-01)

A delta-radiomics model for preoperative prediction of invasive lung adenocarcinomas manifesting as radiological part-solid nodules
Dahkki: Wufei Chen, et al.
Almmustuhtton: (2022-11-01)