ArticleScientific reports2018
Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 2 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
78 citing papers in PubMed, 2 syntheses or guidelines pooled it, 157 citations in OpenAlex.
- Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.Seminars in cancer biology · 2022Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Imaging features of gadoxetic acid-enhanced MR imaging for evaluation of tumor-infiltrating CD8 cells and PD-L1 expression in hepatocellular carcinoma.Cancer immunology, immunotherapy : CII · 2022Trial
- Noninvasive Method for Predicting the Expression of Ki67 and Prognosis in Non-Small-Cell Lung Cancer Patients: Radiomics.Journal of healthcare engineering · 2022Trial
- Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH-2 trial intracerebral hemorrhage population.European journal of neurology · 2021Trial
- Imaging the hallmarks of cancer.Nature reviews. Cancer · 2026Review
- CT-based immune radiomic signature for prognosis and prediction of immunotherapy and anticancer drug response in NSCLC.Frontiers in immunology · 2026Article
- Integration of multi-omics approaches in exploring intra-tumoral heterogeneity.Cancer cell international · 2025Review
- A patient similarity-embedded Bayesian approach to prognostic biomarker inference with application to thoracic cancer immunity.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Article
- Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours.Nature reviews. Cancer · 2024Review
- Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC.Scientific reports · 2024Article
- Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer.NPJ precision oncology · 2023Review
- A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas.Cancers · 2023Article
- Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.La Radiologia medica · 2023Article
- CT radiomics analysis of primary colon cancer patients with or without liver metastases: a correlative study with [Abdominal radiology (New York) · 2023Article
- Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics.Cell reports. Medicine · 2023Article
- Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases.Journal of translational medicine · 2023Article
- Radiomics for the non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to non-small cell lung cancer.Journal of neuro-oncology · 2023Article
- CT radiomic predictors of local relapse after SBRT for lung oligometastases from colorectal cancer: a single institute pilot study.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2023Article
18 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
16 authors at 1 institution in 1 country.
Funding
Abstract
With increasing use of immunotherapy agents, pretreatment strategies for identifying responders and non-responders is useful for appropriate treatment assignment. We hypothesize that the local immune micro-environment of NSCLC is associated with patient outcomes and that these local immune features exhibit distinct radiologic characteristics discernible by quantitative imaging metrics. We assembled two cohorts of NSCLC patients treated with definitive surgical resection and extracted quantitative parameters from pretreatment CT imaging. The excised primary tumors were then quantified for percent tumor PDL1 expression and density of tumor-infiltrating lymphocyte (via CD3 count) utilizing immunohistochemistry and automated cell counting. Associating these pretreatment radiomics parameters with tumor immune parameters, we developed an immune pathology-informed model (IPIM) that separated patients into 4 clusters (designated A-D) utilizing 4 radiomics features. The IPIM designation was significantly associated with overall survival in both training (5 year OS: 61%, 41%, 50%, and 91%, for clusters A-D, respectively, P = 0.04) and validation (5 year OS: 55%, 72%, 75%, and 86%, for clusters A-D, respectively, P = 0.002) cohorts and immune pathology (all P < 0.05). Specifically, we identified a favorable outcome group characterized by low CT intensity and high heterogeneity that exhibited low PDL1 and high CD3 infiltration, suggestive of a favorable immune activated state. We have developed a NSCLC radiomics signature based on the immune micro-environment and patient outcomes. This manuscript demonstrates model creation and validation in independent cohorts.
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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.