ArticleEuropean journal of radiology open2025
CT-based deep learning radiomics analysis for preoperative Lauren classification in gastric cancer and explore the tumor microenvironment.
Article in European journal of radiology open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Molecular classification and precision therapy in gastric cancer: Current advances and future perspectives (Review).Oncology reports · 2026Review
- Development and validation of an interpretable CT-based scoring model for gastric cancer aggressiveness.Abdominal radiology (New York) · 2026Article
- The impact of the PI3K/AKT/mTOR signaling pathway on trastuzumab resistance in HER2-positive gastric cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Lauren classification-based combined model integrating clinicopathological and spectral CT features for disease-free survival prediction in gastric cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Ultrasound-based deep learning radiomics for the differential diagnosis of benign and malignant subpleural pulmonary lesions.Frontiers in oncology · 2026Article
- Therapeutic vulnerability shaped by the microenvironment: multi-omics and AI biomarkers for precision surgical planning in gastrointestinal tumors.Frontiers in cell and developmental biology · 2026Article
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- A Multimodal MRI-Based Model for Colorectal Liver Metastasis Prediction: Integrating Radiomics, Deep Learning, and Clinical Features with SHAP Interpretation.Current oncology (Toronto, Ont.) · 2025Article
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6 authors.
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Abstract
Purpose: This study aimed to investigate the usefulness of CT-based deep learning radiomics analysis (DLRA) for preoperatively differentiating Lauren classification in gastric cancer (GC) patients and explore the tumor microenvironment. Methods: 578 patients were recruited from January 2015 to June 2024, and divided into the training cohort (n = 311), the internal validation cohort (n = 132), and the external validation cohort (n = 135). Clinical characteristics were collected. Radiomics features were extracted from CT images at arterial phase (AP) and venous phase (VP). A radiomics nomogram incorporating radiomics signature and clinical information was built for distinguishing Lauren classification, and its discrimination, calibration, and clinical usefulness were evaluated. RNA sequencing data from The Cancer Imaging Archive database were used to perform transcriptomics analysis. Results: The nomogram incorporating the two radiomics signatures and clinical characteristics exhibited good discrimination of Lauren classification on all cohorts [overall C-indexes 0.815 (95 % CI: 0.739-0.869) in the training cohort, 0.785 (95 % CI: 0.702-0.834) in the internal validation cohort, 0.756 (95 % CI: 0.685-0.816) in the external validation cohort]. It outperformed the clinical model in predictive ability. The calibration and decision curve substantiated the model's excellent fitness and clinical applicability. Further, transcriptomics analysis showed that the differentially expressed genes of different Lauren types were mainly enriched in pathways related to cell contraction and migration, and the infiltration degree of various immune cells was also significantly different. Conclusions: DLRA effectively differentiated Lauren classification in GC, and our analysis of transcriptomic data across different Lauren subtypes revealed the heterogeneity within the GC microenvironment.
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