ArticleFrontiers in oncology2021
A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer.
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 23 papers.
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Who cites it
23 citing papers in PubMed, 40 citations in OpenAlex.
- Associations between CT Radiomics Analyses and Hematopoietic Cell Mobilization in Patients with Multiple Myeloma: An Exploratory Analysis.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2026Article
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- Predicting PD-L1 expression status in NSCLC using radiomic analysis of 18 F-FDG-PET/CT images.European journal of nuclear medicine and molecular imaging · 2026Article
- Prediction of lymph node metastasis in lung adenocarcinoma using a PET/CT radiomics-based ensemble learning model and its pathological basis.Frontiers in oncology · 2025Article
- Artificial intelligence in immunotherapy PET/SPECT imaging.European radiology · 2024Review
- Texture Features ofJournal of clinical medicine · 2024Article
- Integrating Artificial Intelligence and PET Imaging for Drug Discovery: A Paradigm Shift in Immunotherapy.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Application of Nanoparticles in the Diagnosis and Treatment of Colorectal Cancer.Anti-cancer agents in medicinal chemistry · 2024Review
- Radiomics based onFrontiers in oncology · 2024Article
- Development and External Validation ofCancers · 2023Article
- PET Radiomics and Response to Immunotherapy in Lung Cancer: A Systematic Review of the Literature.Cancers · 2023Review
- Tumor infiltrating lymphocytes and radiological picture of the tumor.Medical oncology (Northwood, London, England) · 2023Review
- Radiomics models based on multisequence MRI for predicting PD-1/PD-L1 expression in hepatocellular carcinoma.Scientific reports · 2023Article
- Prediction of CD3 T cells and CD8 T cells expression levels in non-small cell lung cancer based on radiomic features of CT images.Frontiers in oncology · 2023Article
- Current status and prospect of PET-related imaging radiomics in lung cancer.Frontiers in oncology · 2023Review
- What does radiomics do in PD-L1 blockade therapy of NSCLC patients?Thoracic cancer · 2022Review
- Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy.Journal for immunotherapy of cancer · 2022Review
- Radiomic Signatures Associated with CD8Cancers · 2022Review
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Evaluation of Short-Term Efficacy of PD-1 Monoclonal Antibody Immunotherapy for Lymphoma by Positron Emission Tomography/Computed Tomography Imaging with Convolutional Neural Network Image Registration Algorithm.Contrast media & molecular imaging · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTumor microenvironment immune types (TMITs) are closely related to the efficacy of immunotherapy. We aimed to assess the predictive ability of
methodsA retrospective analysis was performed in 103 patients with NSCLC who underwent
resultsFour radiomic features (GLRLM_LRHGE, GLZLM_SZE, SUVmax, NGLDM_Contrast) were selected to build the rad-score. The rad-score showed a significant ability to discriminate between TMITs in both sets (
conclusionThe FDG-PET/CT-based radiomic features showed good performance in predicting TMIT-I tumors in NSCLC, providing a promising approach for the choice of immunotherapy in a clinical setting.
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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.