Development and validation of a radiomic prediction model for TACC3 expression and prognosis in non-small cell lung cancer using contrast-enhanced CT imaging

Contrast-enhanced CT-based radiomics can non-invasively predict TACC3 expression and provide valuable prognostic information, contributing to personalized treatment strategies.

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Publication Details

Journal
Translational Oncology
Published
2024-11-27
DOI
https://doi.org/10.1016/j.tranon.2024.102211
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.81

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article

Development and validation of a radiomic prediction model for TACC3 expression and prognosis in non-small cell lung cancer using contrast-enhanced CT imaging

Weichao Bai, Xinhan Zhao, Qian Ning
2 citations
Translational Oncology
Radiomics and Machine Learning in Medical Imaging
0.81
article

Development and validation of a radiomic prediction model for TACC3 expression and prognosis in non-small cell lung cancer using contrast-enhanced CT imaging

Weichao Bai, Xinhan Zhao, Qian Ning
article en
2 citations

Abstract

Contrast-enhanced CT-based radiomics can non-invasively predict TACC3 expression and provide valuable prognostic information, contributing to personalized treatment strategies.

Translational OncologyVol. 51
First Affiliated Hospital of Xi'an Jiaotong University (CN)
Key Research and Development Projects of Shaanxi Province
Good health and well-being
Openalex Percentile: Top 24%
Radiomics and Machine Learning in Medical Imaging
0.81
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