ArticleJNCI cancer spectrum2021
Hypoxia-Related Radiomics and Immunotherapy Response: A Multicohort Study of Non-Small Cell Lung Cancer.
Article in JNCI cancer spectrum, 2021. 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, 40 citations in OpenAlex.
- Computed Tomography-Based Radiomics Features Characterize Low and High-Risk Lung Cancer Among People Living With HIV: An Early Report.Clinical lung cancer · 2026Article
- Radiomics models for predicting occult nodal metastasis in stage cT1a-bN0M0 lung adenocarcinoma: a multicenter study.Quantitative imaging in medicine and surgery · 2025Article
- Use of Radiomics in Characterizing Tumor Hypoxia.International journal of molecular sciences · 2025Review
- Machine Learning-Based Radiomics in Malignancy Prediction of Pancreatic Cystic Lesions: Evidence from Cyst Fluid Multi-Omics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- MRI-based artificial intelligence models for post-neoadjuvant surgery personalization in breast cancer: a narrative review of evidence from Western Pacific.The Lancet regional health. Western Pacific · 2025Review
- Radiomics nomogram for predicting chemo-immunotherapy efficiency in advanced non-small cell lung cancer.Scientific reports · 2024Article
- [Advancements in Radiomics for Immunotherapy of Non-small Cell Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2024Review
- Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours.Nature reviews. Cancer · 2024Review
- Clinical Variables and Radiomics Features for Predicting Pneumothorax in Patients Undergoing CT-guided Transthoracic Core Needle Biopsy.Radiology. Cardiothoracic imaging · 2024Article
- Utility of CT Radiomics and Delta Radiomics for Survival Evaluation in Locally Advanced Nasopharyngeal Carcinoma with Concurrent Chemoradiotherapy.Diagnostics (Basel, Switzerland) · 2024Article
- Immunotherapy and Cancer: The Multi-Omics Perspective.International journal of molecular sciences · 2024Review
- A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?NPJ precision oncology · 2024Review
- An arterial spin labeling-based radiomics signature and machine learning for the prediction and detection of various stages of kidney damage due to diabetes.Frontiers in endocrinology · 2024Article
- Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer.NPJ precision oncology · 2023Review
- Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies.Scientific reports · 2023Article
- Article
- Identification of Potential Biomarkers for Group I Pulmonary Hypertension Based on Machine Learning and Bioinformatics Analysis.International journal of molecular sciences · 2023Article
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence.Biology · 2023Review
- Immunotherapy Assessment: A New Paradigm for Radiologists.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 3 countries.
Funding
Abstract
Background: Immunotherapy yields survival benefit for some advanced stage non-small cell lung cancer (NSCLC) patients. Because highly predictive biomarkers of immunotherapy response are an unmet clinical need, we used pretreatment radiomics and clinical data to train and validate a parsimonious model associated with survival outcomes among NSCLC patients treated with immunotherapy. Methods: Three cohorts of NSCLC patients treated with immunotherapy were analyzed: training (n = 180), validation 1 (n = 90), and validation 2 (n = 62). The most informative clinical and radiomic features were subjected to decision tree analysis, which stratified patients into risk groups of low, moderate, high, and very high risk of death after initiation of immunotherapy. All statistical tests were 2-sided. Results: The very high-risk group was associated with extremely poor overall survival (OS) in validation cohorts 1 (hazard ratio [HR] = 5.35, 95% confidence interval [CI] = 2.14 to 13.36; 1-year OS = 11.1%, 95% CI = 1.9% to 29.8%; 3-year OS = 0%) and 2 (HR = 13.81, 95% CI = 2.58 to 73.93; 1-year OS = 47.6%, 95% CI = 18.2% to 72.4%; 3-year OS = 0%) when compared with the low-risk group (HR = 1.00) in validation cohorts 1 (1-year OS = 85.0%, 95% CI = 60.4% to 94.9%; 3-year OS = 38.9%, 95% CI = 17.1% to 60.3%) and 2 (1-year OS = 80.2%, 95% CI = 40.3% to 94.8%; 3-year OS = 40.1%, 95% CI = 1.3% to 83.5%). The most informative radiomic feature, gray-level co-occurrence matrix (GLCM) inverse difference, was positively associated with hypoxia-related carbonic anhydrase 9 using gene-expression profiling and immunohistochemistry. Conclusion: Utilizing standard-of-care imaging and clinical data, we identified and validated a novel parsimonious model associated with survival outcomes among NSCLC patients treated with immunotherapy. Based on this model, clinicians can identify patients who are unlikely to respond to immunotherapy.
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