Development of a multimodal predictive model using PET/CT radiomics and clinical data for preoperative assessment of lymphovascular invasion in gastric cancer

The multimodal predictive model demonstrated excellent diagnostic accuracy for preoperative assessment of LVI in GC patients and may support individualized treatment planning and risk stratification. Prospective multicenter studies are needed to further validate its clinical utility.

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Publication Details

Journal
American Journal of Translational Research
Published
2025-01-01
DOI
https://doi.org/10.62347/pbnu9406
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
2.72
Controls
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article

Development of a multimodal predictive model using PET/CT radiomics and clinical data for preoperative assessment of lymphovascular invasion in gastric cancer

Chunqiao Wu
2 citations
American Journal of Translational Research
Radiomics and Machine Learning in Medical Imaging
2.72
article

Development of a multimodal predictive model using PET/CT radiomics and clinical data for preoperative assessment of lymphovascular invasion in gastric cancer

Chunqiao Wu
article en
2 citations

Abstract

The multimodal predictive model demonstrated excellent diagnostic accuracy for preoperative assessment of LVI in GC patients and may support individualized treatment planning and risk stratification. Prospective multicenter studies are needed to further validate its clinical utility.

American Journal of Translational ResearchVol. 17(7)
Sir Run Run Shaw Hospital (CN)
Zero hunger
Openalex Percentile: Top 9%
Radiomics and Machine Learning in Medical Imaging
2.72
DOIOpen Access PDFOpenAlex
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Development of a multimodal predictive model using PET/CT radiomics and clinical data for preoperative assessment of lymphovascular invasion in gastric cancer — Chunqiao Wu · American Journal of Translational Research (2025) | TGRS Research Map | TGRS