SynthesisEuropean journal of nuclear medicine and molecular imaging2021
Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis.
Synthesis in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 8 of them syntheses that pooled it.
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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
47 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis.World journal of surgical oncology · 2025Pooled it
- Accuracy of radiomics in the diagnosis and preoperative high-risk assessment of endometrial cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Predictive value ofFrontiers in oncology · 2024Pooled it
- Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative.European radiology · 2023Pooled it
- Systematic review of machine learning-based radiomics approach for predicting microsatellite instability status in colorectal cancer.La Radiologia medica · 2023Pooled it
- Imaging-based adipose biomarkers for predicting clinical outcomes of cancer patients treated with immune checkpoint inhibitors: a systematic review.Frontiers in oncology · 2023Pooled it
- Efficacy of non-enhanced computer tomography-based radiomics for predicting hematoma expansion: A meta-analysis.Frontiers in oncology · 2022Pooled it
- Immediate post-microwave ablation computed tomography model may better predict short-term response of stage I non-small cell lung cancer.World journal of radiology · 2026Article
- Integrating Lymph Node Metastasis and Programmed Death-Ligand 1 Prediction in Non-Small Cell Lung Cancer From a Single PET/CT Scan: Multicenter Radiomics Study.JMIR medical informatics · 2026Article
- Deep learning and radiomics models in patients with advanced non-small cell lung cancer treated with immunotherapy combined with stereotactic radiotherapy.Scientific reports · 2026Article
- Advances in Multi-Modal Biomarkers for Immunotherapy Response in Non-Small Cell Lung Cancer: ctDNA, Microbiome, and Radiomics.Cancers · 2026Review
- CT-based radiomics for predicting the treatment response to PD-1/PD-L1 inhibitors combined with chemotherapy in unresectable gastric cancer.Insights into imaging · 2026Article
- Review
- Evolving non-invasive biomarkers in NSCLC immunotherapy: integrating liquid biopsy and multi-omics profiling for precision oncology.Frontiers in immunology · 2026Review
- Multi-omics biomarkers for predicting resistance, hyperprogression, and immune-related toxicity during PD-1/PD-L1 therapy in lung cancer: a literature review.Frontiers in immunology · 2026Review
- 2.5D Deep Learning and Machine Learning for Discriminative DLBCL and IDC with Radiomics on PET/CT.Bioengineering (Basel, Switzerland) · 2025Article
- Association between age and lung cancer risk: evidence from lung lobar radiomics.BMC medical imaging · 2025Article
- Short-term peri- and intra-tumoral CT radiomics to predict immunotherapy response in advanced non-small cell lung cancer.Translational lung cancer research · 2025Article
- Letter to the editor: radiomics signature for dynamic monitoring of tumor-inflamed microenvironment and immunotherapy response prediction.Journal for immunotherapy of cancer · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePrediction of immunotherapy response and outcome in patients with non-small cell lung cancer (NSCLC) is challenging due to intratumoral heterogeneity and lack of robust biomarkers. The aim of this study was to systematically evaluate the methodological quality of radiomic studies for predicting immunotherapy response or outcome in patients with NSCLC.
methodsWe systematically searched for eligible studies in the PubMed and Web of Science datasets up to April 1, 2021. The methodological quality of included studies was evaluated using the phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool. A meta-analysis of studies regarding the prediction of immunotherapy response and outcome in patients with NSCLC was performed.
resultsFifteen studies were identified with sample sizes ranging from 30 to 228. Seven studies were classified as phase II, and the remaining as discovery science (n = 2), phase 0 (n = 4), phase I (n = 1), and phase III (n = 1). The mean RQS score of all studies was 29.6%, varying from 0 to 68.1%. The pooled diagnostic odds ratio for predicting immunotherapy response in NSCLC using radiomics was 14.99 (95% confidence interval [CI] 8.66-25.95). In addition, radiomics could divide patients into high- and low-risk group with significantly different overall survival (pooled hazard ratio [HR]: 1.96, 95%CI 1.61-2.40, p < 0.001) and progression-free survival (pooled HR: 2.39, 95%CI 1.69-3.38, p < 0.001).
conclusionsRadiomics has potential to noninvasively predict immunotherapy response and outcome in patients with NSCLC. However, it has not yet been implemented as a clinical decision-making tool. Further external validation and evaluation within clinical pathway can facilitate personalized treatment for patients with NSCLC.
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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.