ArticleOncotarget2017
Tumor image-derived texture features are associated with CD3 T-cell infiltration status in glioblastoma.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Biomarkers for predicting immunotherapy response and resistance in glioblastoma.Frontiers in immunology · 2026Review
- Preoperative MRI-based radiomics analysis of intra- and peritumoral regions for predicting CD3 expression in early cervical cancer.Scientific reports · 2025Article
- Computed Tomography-Based Habitat Analysis for Prognostic Stratification in Colorectal Liver Metastases.Cancer innovation · 2025Article
- Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma.Nature communications · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Myeloid response evaluated by noninvasive CT imaging predicts post-surgical survival and immune checkpoint therapy benefits in patients with hepatocellular carcinoma.Frontiers in immunology · 2024Article
- Phenomic Imaging.Phenomics (Cham, Switzerland) · 2023Review
- A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas.Cancers · 2023Article
- Radiomics for characterization of the glioma immune microenvironment.NPJ precision oncology · 2023Review
- Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine.Current oncology (Toronto, Ont.) · 2023Review
- Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.Gastroenterology · 2022Article
- Assessment of radiographic and prognostic characteristics of programmed death-ligand 1 expression in high-grade gliomas.Journal of neuro-oncology · 2022Article
- Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology.AJNR. American journal of neuroradiology · 2022Review
- MRI and PET of Brain Tumor Neuroinflammation in the Era of Immunotherapy, From theAJR. American journal of roentgenology · 2022Review
- Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective.Frontiers in oncology · 2022Review
- Review
- Radiomic biomarkers of tumor immune biology and immunotherapy response.Clinical and translational radiation oncology · 2021Review
- Comprehensive analysis reveals a prognostic and therapeutic biomarker CD3D in the breast carcinoma microenvironment.Bioscience reports · 2021Article
- Tumor Habitat-derived Radiomic Features at Pretreatment MRI That Are Prognostic for Progression-free Survival in Glioblastoma Are Associated with Key Morphologic Attributes at Histopathologic Examination: A Feasibility Study.Radiology. Artificial intelligence · 2020Article
- Magnetic resonance imaging-based radiomic features for extrapolating infiltration levels of immune cells in lower-grade gliomas.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
This study analyzed magnetic resonance imaging (MRI) scans of Glioblastoma (GB) patients to develop an imaging-derived predictive model for assessing the extent of intratumoral CD3 T-cell infiltration. Pre-surgical T1-weighted post-contrast and T2-weighted Fluid-Attenuated-Inversion-Recovery (FLAIR) MRI scans, with corresponding mRNA expression of CD3D/E/G were obtained through The Cancer Genome Atlas (TCGA) for 79 GB patients. The tumor region was contoured and 86 image-derived features were extracted across the T1-post contrast and FLAIR images. Six imaging features-kurtosis, contrast, small zone size emphasis, low gray level zone size emphasis, high gray level zone size emphasis, small zone high gray level emphasis-were found associated with CD3 activity and used to build a predictive model for CD3 infiltration in an independent data set of 69 GB patients (using a 50-50 split for training and testing). For the training set, the image-based prediction model for CD3 infiltration achieved accuracy of 97.1% and area under the curve (AUC) of 0.993. For the test set, the model achieved accuracy of 76.5% and AUC of 0.847. This suggests a relationship between image-derived textural features and CD3 T-cell infiltration enabling the non-invasive inference of intratumoral CD3 T-cell infiltration in GB patients, with potential value for the radiological assessment of response to immune therapeutics.
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Registered trials
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