SynthesisFrontiers in medicine2023
Role of radiomics in predicting lymph node metastasis in gastric cancer: a systematic review.
Synthesis in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Current Role of Artificial Intelligence in the Management of Gastric Cancer.Biomedicines · 2025Review
- Developing and validating a computed tomography radiomics strategy to predict lymph node metastasis in pancreatic cancer.World journal of radiology · 2025Article
- Artificial intelligence in advanced gastric cancer: a comprehensive review of applications in precision oncology.Frontiers in oncology · 2025Review
- Article
- A radiomics model for predicting perineural invasion in stage II-III colon cancer based on computer tomography.BMC cancer · 2024Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Gastric cancer (GC) is an aggressive and clinically heterogeneous tumor, and better risk stratification of lymph node metastasis (LNM) could lead to personalized treatments. The role of radiomics in the prediction of nodal involvement in GC has not yet been systematically assessed. This study aims to assess the role of radiomics in the prediction of LNM in GC. Methods: A PubMed/MEDLINE systematic review was conducted to assess the role of radiomics in LNM. The inclusion criteria were as follows: i. original articles, ii. articles on radiomics, and iii. articles on LNM prediction in GC. All articles were selected and analyzed by a multidisciplinary board of two radiation oncologists and one surgeon, under the supervision of one radiation oncologist, one surgeon, and one medical oncologist. Results: A total of 171 studies were obtained using the search strategy mentioned on PubMed. After the complete selection process, a total of 20 papers were considered eligible for the analysis of the results. Radiomics methods were applied in GC to assess the LNM risk. The number of patients, imaging modalities, type of predictive models, number of radiomics features, TRIPOD classification, and performances of the models were reported. Conclusions: Radiomics seems to be a promising approach for evaluating the risk of LNM in GC. Further and larger studies are required to evaluate the clinical impact of the inclusion of radiomics in a comprehensive decision support system (DSS) for GC.
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