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Publication

Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis.

Yuan Li | Baihan Lyu | Rong Wang | Yue Peng | Haoyu Ran | Bolun Zhou | Yang Liu | Guangyu Bai | Qilin Huai | Xiaowei Chen | Chun Zeng | Qingchen Wu | Cheng Zhang | Shugeng Gao
Thoracic cancer | 2024

Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results.

Pubmed ID: 38191149

Research resources used in this publication

None found

Additional research tools detected in this publication

Antibodies used in this publication

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Associated grants

  • Agency: National Key Research and Development Program of China,
    Id: 2021YFC2500900
  • Agency: National Natural Science Foundation of China,
    Id: 82273129

Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.

This is a list of tools and resources that we have found mentioned in this publication.

pyradiomics (tool)

RRID:SCR_026019

Software Python package for extraction of Radiomics features from 2D and 3D images and binary masks.

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The Stimulating Peripheral Activity to Relieve Conditions (SPARC) effort is the result of National Institutes of Health’s (NIH) drive to map out the neural circuitry responsible for visceral control in higher vertebrates. The SPARC Anatomy Working Group (SAWG) is responsible for the integrity of anatomical knowledge in SPARC

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