ArticleFrontiers in oncology2021
A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study.
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 13 papers.
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13 citing papers in PubMed.
- Trial
- CT radiomics machine learning for predicting treatment response in unresectable stage III-IV NSCLC: insights from dual chemo-immunotherapy and immunotherapy cohorts.La Radiologia medica · 2026Article
- Early prediction of immunotherapy efficacy for advanced NSCLC based on clinical and pre-treatment contrast-enhanced CT radiomics features.Frontiers in oncology · 2025Article
- [Advancements in Radiomics for Immunotherapy of Non-small Cell Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2024Review
- A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?NPJ precision oncology · 2024Review
- A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma.Journal of translational medicine · 2024Article
- Machine learning-based ultrasomics for predicting response to tyrosine kinase inhibitor in combination with anti-PD-1 antibody immunotherapy in hepatocellular carcinoma: a two-center study.Frontiers in oncology · 2024Article
- Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer.NPJ precision oncology · 2023Review
- Contrast-enhanced CT-based radiomic analysis for determining the response to anti-programmed death-1 therapy in esophageal squamous cell carcinoma patients: A pilot study.Thoracic cancer · 2023Article
- Article
- Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy.Journal for immunotherapy of cancer · 2022Review
- CT-Based Radiomics Can Predict the Efficacy of Anlotinib in Advanced Non-Small-Cell Lung Cancer.Journal of oncology · 2022Article
- Artificial Intellgence in the Era of Precision Oncological Imaging.Technology in cancer research & treatmentReview
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18 authors.
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Abstract
objectiveBased on non-contrast-enhanced (NCE)/contrast-enhanced (CE) computed tomography (CT) images, we try to identify a combined-radiomics model and evaluate its predictive capacity regarding response to anti-PD1 immunotherapy of patients with non-small-cell lung cancer (NSCLC).
methods131 patients with NSCLC undergoing anti-PD1 immunotherapy were retrospectively enrolled from 7 institutions. Using largest lesion (LL) and target lesions (TL) approaches, we performed a radiomics analysis based on pretreatment NCE-CT (NCE-radiomics) and CE-CT images (CE-radiomics), respectively. Meanwhile, a combined-radiomics model based on NCE-CT and CE-CT images was constructed. Finally, we developed their corresponding nomograms incorporating clinical factors. ROC was used to evaluate models' predictive performance in the training and testing set, and a DeLong test was employed to compare the differences between different models.
resultsFor TL approach, both NCE-radiomics and CE-radiomics performed poorly in predicting response to immunotherapy. For LL approach, NCE-radiomics nomograms and CE-radiomics nomograms incorporating with clinical factor of distant metastasis all showed satisfactory results, reflected by the AUCs in the training (AUC=0.84, 95% CI: 0.75-0.92; AUC=0.77, 95% CI: 0.67-0.87) and test sets (AUC=0.78, 95% CI: 0.64-0.92, AUC=0.73, 95% CI: 0.57-0.88), respectively. Compared with the NCE-radiomics nomograms, the combined-radiomics nomogram showed incremental predictive capacity in the training set (AUC=0.85, 95% CI: 0.77-0.92) and test set (AUC=0.81, 95% CI: 0.67-0.94), respectively, but no statistical difference (
conclusionCompared with radiomics based on single NCE or CE-CT images, the combined-radiomics model has potential advantages to identify patients with NSCLC most likely to benefit from immunotherapy, and may effectively improve more precise and individualized decision support.
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