Development of a radiomic model for cervical cancer staging based on pathologically verified, retrospective metastatic lymph node data

The constructed SVM imaging histology model for the merged LN group might be advantageous in predicting pelvic LN metastasis in cervical cancer.

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Publication Details

Journal
Acta Radiologica
Published
2024-11-21
DOI
https://doi.org/10.1177/02841851241291931
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 of a radiomic model for cervical cancer staging based on pathologically verified, retrospective metastatic lymph node data

Chin Siang Kue, Liang Liu, Deyue Meng, Bin Zhang
2 citations
Acta Radiologica
Radiomics and Machine Learning in Medical Imaging
0.81
article

Development of a radiomic model for cervical cancer staging based on pathologically verified, retrospective metastatic lymph node data

Chin Siang Kue, Liang Liu, Deyue Meng, Bin Zhang
article en
2 citations

Abstract

The constructed SVM imaging histology model for the merged LN group might be advantageous in predicting pelvic LN metastasis in cervical cancer.

Acta RadiologicaVol. 65(12)
Hebei Medical University (CN), Management and Science University (MY), Fourth Hospital of Hebei Medical University (CN)
Good health and well-being
Openalex Percentile: Top 24%
Radiomics and Machine Learning in Medical Imaging
0.81
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