ArticleFrontiers in oncology2023
Are radiomic spleen features useful for assessing the differentiation status of advanced gastric cancer?
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- YTHDF3 Promoted Gastric Cancer Progression by Facilitating mAnalytical cellular pathology (Amsterdam) · 2026Article
- Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers.Journal of cancer research and clinical oncology · 2025Article
- Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT.Diagnostics (Basel, Switzerland) · 2023Article
- Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
9 authors.
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Abstract
Background: The differentiation status of gastric cancer is related to clinical stage, treatment and prognosis. It is expected to establish a radiomic model based on the combination of gastric cancer and spleen to predict the differentiation degree of gastric cancer. Thus, we aim to determine whether radiomic spleen features can be used to distinguish advanced gastric cancer with varying states of differentiation. Materials and methods: January 2019 to January 2021, we retrospectively analyzed 147 patients with advanced gastric cancer confirmed by pathology. The clinical data were reviewed and analyzed. Three radiomics predictive models were built from radiomics features based on gastric cancer (GC), spleen (SP) and combination of two organ position (GC+SP) images. Then, three Radscores (GC, SP and GC+SP) were obtained. A nomogram was developed to predict differentiation statue by incorporating GC+SP Radscore and clinical risk factors. The area under the curve (AUC) of operating characteristics (ROC) and calibration curves were assessed to evaluate the differential performance of radiomic models based on gastric cancer and spleen for advanced gastric cancer with different states of differentiation (poorly differentiated group and non- poorly differentiated group). Results: There were 147 patients evaluated (mean age, 60 years ± 11SD, 111 men). Univariate and multivariate logistic analysis identified three clinical features (age, cTNM stage and CT attenuation of spleen arterial phase) were independent risk factors for the degree of differentiation of GC ( Conclusion: By combining radiomic features (GC and spleen) with clinical risk factors, we develop a radiomic nomogram to predict differentiation status in patients with AGC, which can be used to guide treatment decisions.
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