ArticleRespiratory research2021
Combination of computed tomography imaging-based radiomics and clinicopathological characteristics for predicting the clinical benefits of immune checkpoint inhibitors in lung cancer.
Article in Respiratory research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
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- Imaging biomarkers related to tumor-associated macrophage in immunotherapy treatment planning for non-small cell lung cancer.Translational lung cancer research · 2025Article
- Integrated radiomics and immune infiltration analysis to decipher immunotherapy efficacy in lung adenocarcinoma.Quantitative imaging in medicine and surgery · 2025Article
- Establishment and validation of a convenient and efficient screening tool for active pulmonary tuberculosis in lung cancer patients based on common parameters.Therapeutic advances in medical oncology · 2025Article
- An Enhanced CT-based Radiomics Model for Predicting the Anaplastic Lymphoma Kinase Mutation Status in Lung Adenocarcinoma.Current medical imaging · 2025Article
- CT-based different regions of interest radiomics analysis for acute radiation pneumonitis in patients with locally advanced NSCLC after chemoradiotherapy.Clinical and translational radiation oncology · 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
- Multi-omics and artificial intelligence predict clinical outcomes of immunotherapy in non-small cell lung cancer patients.Clinical and experimental medicine · 2024Review
- A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?NPJ precision oncology · 2024Review
- Radiomics from dual-energy CT-derived iodine maps predict lymph node metastasis in head and neck squamous cell carcinoma.La Radiologia medica · 2024Article
- Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response.Breast cancer research : BCR · 2024Article
- Applications of CT-based radiomics for the prediction of immune checkpoint markers and immunotherapeutic outcomes in non-small cell lung cancer.Frontiers in immunology · 2024Review
- Radiomic biomarkers from chest computed tomography are assistive in immunotherapy response prediction for non-small cell lung cancer.Translational lung cancer research · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Clinical-radiomic model in advanced kidney cancer predicts response to tyrosine kinase inhibitors.Oncology letters · 2022Article
- Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy.Journal for immunotherapy of cancer · 2022Review
- Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?Journal for immunotherapy of cancer · 2022Review
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16 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundIn this study, we tested whether a combination of radiomic features extracted from baseline pre-immunotherapy computed tomography (CT) images and clinicopathological characteristics could be used as novel noninvasive biomarkers for predicting the clinical benefits of non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs).
methodsThe data from 92 consecutive patients with lung cancer who had been treated with ICIs were retrospectively analyzed. In total, 88 radiomic features were selected from the pretreatment CT images for the construction of a random forest model. Radiomics model 1 was constructed based on the Rad-score. Using multivariate logistic regression analysis, the Rad-score and significant predictors were integrated into a single predictive model (radiomics nomogram model 1) to predict the durable clinical benefit (DCB) of ICIs. Radiomics model 2 was developed based on the same Rad-score as radiomics model 1.Using multivariate Cox proportional hazards regression analysis, the Rad-score, and independent risk factors, radiomics nomogram model 2 was constructed to predict the progression-free survival (PFS).
resultsThe models successfully predicted the patients who would benefit from ICIs. For radiomics model 1, the area under the receiver operating characteristic curve values for the training and validation cohorts were 0.848 and 0.795, respectively, whereas for radiomics nomogram model 1, the values were 0.902 and 0.877, respectively. For the PFS prediction, the Harrell's concordance indexes for the training and validation cohorts were 0.717 and 0.760, respectively, using radiomics model 2, whereas they were 0.749 and 0.791, respectively, using radiomics nomogram model 2.
conclusionsCT-based radiomic features and clinicopathological factors can be used prior to the initiation of immunotherapy for identifying NSCLC patients who are the most likely to benefit from the therapy. This could guide the individualized treatment strategy for advanced NSCLC.
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