Quality and accuracy of radiomics models in predicting KRAS status in lung cancer: a systematic review and meta-analysis

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Publication Details

Journal
Frontiers in Oncology
Published
2026-01-09
DOI
https://doi.org/10.3389/fonc.2025.1701122
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Quality and accuracy of radiomics models in predicting KRAS status in lung cancer: a systematic review and meta-analysis

Di Lu, Yayuan Yang, Yaping Wang, Pengcheng Dong et al.
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
article

Quality and accuracy of radiomics models in predicting KRAS status in lung cancer: a systematic review and meta-analysis

Di Lu, Yayuan Yang, Yaping Wang, Pengcheng Dong, Ziqiang Wang, Yunjiu Gou, Xindong Luo
article en

Abstract

No abstract available for this paper.
Frontiers in OncologyVol. 15
Gansu University of Traditional Chinese Medicine (CN), Lanzhou Institute of Husbandry and Pharmaceutical Sciences (CN), Gansu Provincial Hospital (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 94%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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Quality and accuracy of radiomics models in predicting KRAS status in lung cancer: a systematic review and meta-analysis — Di Lu, Yayuan Yang, et al. · Frontiers in Oncology (2026) | TGRS Research Map | TGRS