ArticleAnnals of translational medicine2020
A CT-based radiomics signature for evaluating tumor infiltrating Treg cells and outcome prediction of gastric cancer.
Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 33 citations in OpenAlex.
- Cancer classification with radiomics in controlled preclinical models.Scientific reports · 2026Article
- Development and validation of CT-based fusion model for preoperative prediction of invasion and lymph node metastasis in adenocarcinoma of esophagogastric junction.BMC medical imaging · 2025Article
- Gastrointestinal tumor personalized immunotherapy: an integrated analysis from molecular genetics to imaging biomarkers.Therapeutic advances in gastroenterology · 2025Review
- Radiomics-based predictive model for preoperative risk classification of gastrointestinal stromal tumors using multiparametric magnetic resonance imaging: a retrospective study.Radiologie (Heidelberg, Germany) · 2024Article
- Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours.Nature reviews. Cancer · 2024Review
- MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma.Biomarker research · 2024Article
- Artificial intelligence applications in computed tomography in gastric cancer: a narrative review.Translational cancer research · 2023Review
- Preoperative prediction of vessel invasion in locally advanced gastric cancer based on computed tomography radiomics and machine learning.Oncology letters · 2023Article
- Radiomic-based machine learning model for predicting the surgical risk in children with abdominal neuroblastoma.World journal of pediatric surgery · 2023Article
- Article
- The predictive potential of contrast-enhanced computed tomography based radiomics in the preoperative staging of cT4 gastric cancer.Quantitative imaging in medicine and surgery · 2022Article
- Radiomics in precision medicine for gastric cancer: opportunities and challenges.European radiology · 2022Review
- Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy.Journal for immunotherapy of cancer · 2022Review
- Artificial Intelligence-based Radiomics in the Era of Immuno-oncology.The oncologist · 2022Review
- Role of radiomics in predicting immunotherapy response.Journal of medical imaging and radiation oncology · 2022Review
- CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.Frontiers in oncology · 2022Article
- Tumor-infiltrating lymphocyte enrichment predicted by CT radiomics analysis is associated with clinical outcomes of non-small cell lung cancer patients receiving immune checkpoint inhibitors.Frontiers in immunology · 2022Article
- YAP1/MMP7/CXCL16 axis affects efficacy of neoadjuvant chemotherapy via tumor environment immunosuppression in triple-negative breast cancer.Gland surgery · 2021Article
- Radiomic biomarkers of tumor immune biology and immunotherapy response.Clinical and translational radiation oncology · 2021Review
- Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma.Frontiers in oncology · 2021Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTumor infiltrating regulatory T (TITreg) cells are highly infiltrated in gastric cancer (GC) and associated with worse prognosis of GC patients. We aim to develop and validate a radiomics signature for evaluation of TITreg cells and outcome prediction of GC patients.
methodsA total of 165 GC patients from three independent cohorts were enrolled in this retrospective study. The abundance of TITreg cells were evaluated by using multispectral immunohistochemical analysis and CIBERSORT algorithm. The radiomics features were extracted by using PyRadiomics software and the radiomics signature was generated by using the least absolute shrinkage and selection operator (LASSO) logistic regression model. The receiver operator characteristic (ROC) curves were applied to assess the performance of radiomics signature for estimating TITreg cells. Univariable and multivariable Cox regression analysis were used for identifying risk factor of overall survival (OS). The prognostic value of the radiomics signature and the TITreg cells were evaluated by using the Kaplan-Meier method and log-rank test.
resultsSix robust features were selected for building the radiomics signature. The radiomics signature showed good ability for estimating TITreg in the training, validation and testing cohort, with area under the curve (AUC) of 0.884, 0.869 and 0.847, respectively. Multivariable Cox regression analysis showed that the radiomics signature was an independent risk factor of unfavorable OS of GC patients.
conclusionsThe proposed CT-based radiomics signature is a promising non-invasive biomarker of TITreg cells and outcome prediction of GC patients.
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