ArticleFrontiers in oncology2021
XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Performance of Artificial Intelligence in Detecting and Distinguishing Pancreatic Ductal Adenocarcinoma via Computed Tomography: A Systematic Review and Meta-Analysis.Journal of imaging informatics in medicine · 2026Pooled it
- Exploring free radiomics software tools: a multiparametric evaluation for cancer classification.BMC medical imaging · 2025Article
- Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis.Scientific reports · 2025Article
- Development and validation of a nomogram model of lung metastasis in breast cancer based on machine learning algorithm and cytokines.BMC cancer · 2025Article
- Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction.Journal for immunotherapy of cancer · 2025Article
- Different MRI-based radiomics machine learning models to predict CD3+ tumor-infiltrating lymphocytes in rectal cancer.Frontiers in oncology · 2025Article
- The Past, Present, and Future of Biomarkers for the Early Diagnosis of Pancreatic Cancer.Biomedicines · 2024Review
- MORE: a multi-omics data-driven hypergraph integration network for biomedical data classification and biomarker identification.Briefings in bioinformatics · 2024Article
- Understanding the Conundrum of Pancreatic Cancer in the Omics Sciences Era.International journal of molecular sciences · 2024Review
- Imaging-Based Biomarkers Predict Programmed Death-Ligand 1 and Survival Outcomes in Advanced NSCLC Treated With Nivolumab and Pembrolizumab: A Multi-Institutional Study.JTO clinical and research reports · 2023Article
- A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas.Cancers · 2023Article
- Tumor infiltrating lymphocytes and radiological picture of the tumor.Medical oncology (Northwood, London, England) · 2023Review
- Association between radiomics features of DCE-MRI and CD8Pathology oncology research : POR · 2023Article
- Non-enhanced magnetic resonance imaging-based radiomics model for the differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma.Frontiers in oncology · 2023Article
- Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer.Journal of translational medicine · 2022Article
- Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy.Journal for immunotherapy of cancer · 2022Review
- Radiomic Signatures Associated with CD8Cancers · 2022Review
- Review
- Machine learning: A non-invasive prediction method for gastric cancer based on a survey of lifestyle behaviors.Frontiers in artificial intelligence · 2022Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study constructed and validated a machine learning model to predict CD8 MATERIALS AND
methodsIn this retrospective study, 184 PDAC patients were randomly assigned to a training dataset (n =137) and validation dataset (n =47). All patients were divided into CD8
resultsThe cut-off value of the CD8
conclusionsWe developed a CT-based XGBoost classifier to extrapolate the infiltration levels of CD8
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.