ArticleOncoimmunology2022
A short-term follow-up CT based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer.
Article in Oncoimmunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Trial
- Guiding the Application of Immunotherapy in Nonsmall Cell Lung Cancer: The Role of Biomarkers.Thoracic cancer · 2026Review
- Risk stratification of PD-L1 expression in non-small cell lung cancer: a predictive model for prognosis.Journal of thoracic disease · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Enhanced CT-based deep learning radiomics and biological correlations for predicting immunotherapy efficacy in advanced non-small cell lung cancer.Translational cancer research · 2026Article
- Predicting homologous recombination deficiency and treatment responses using a computed tomography-based foundation model: a preclinical study.NPJ precision oncology · 2026Article
- Deep-Learning Serial CT Prediction of Survival in Immunotherapy-Treated Non-Small Cell Lung Cancer.JAMA network open · 2026Article
- The capability of deep-radiomics to predict pathological response to neoadjuvant immunochemotherapy in non-small cell lung cancer: a retrospective multicenter study.Frontiers in immunology · 2026Article
- Multimodal integration of longitudinal noninvasive diagnostics for survival prediction in immunotherapy using deep learning.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Preliminary Evaluation of Radiomics in Contrast-Enhanced Mammography for Prognostic Prediction of Breast Cancer.Cancers · 2025Article
- A CT-based radiomics model for predicting progression-free survival in patients with epithelial ovarian cancer.BMC cancer · 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
- Imaging patterns and recommendations for diagnosis, staging, and management of lung cancer.BJR open · 2025Review
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Development and validation of an LDCT-based deep learning radiomics nomogram for predicting postoperative recurrence of stage Ia lung adenocarcinoma.Frontiers in oncology · 2025Article
- Early prediction of immunotherapy efficacy for advanced NSCLC based on clinical and pre-treatment contrast-enhanced CT radiomics features.Frontiers in oncology · 2025Article
- Fusion of CT radiomics and autoantibody biomarkers for enhanced prediction of lung cancer diagnosis: a comprehensive study.Frontiers in oncology · 2025Article
- A novel radiomics model combining GTVp, GTVnd, and clinical data for chemoradiotherapy response prediction in patients with advanced NSCLC.Frontiers in medicine · 2025Article
- CT-based delta-radiomics for predicting pathological response to neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma: a multicenter study.BMC medical imaging · 2024Article
- Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
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8 authors.
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Abstract
To develop a short-term follow-up CT-based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer (NSCLC) and investigate the prognostic value of radiomics features in predicting progression-free survival (PFS) and overall survival (OS). We first retrospectively collected 224 advanced NSCLC patients from two centers, and divided them into a primary cohort and two validation cohorts respectively. Then, we processed CT scans with a series of image preprocessing techniques namely, tumor segmentation, image resampling, feature extraction and normalization. To select the optimal features, we applied the feature ranking with recursive feature elimination method. After resampling the training dataset with a synthetic minority oversampling technique, we applied the support vector machine classifier to build a machine-learning-based classification model to predict response to immunotherapy. Finally, we used Kaplan-Meier (KM) survival analysis method to evaluate prognostic value of rad-score generated by CT-radiomics model. In two validation cohorts, the delta-radiomics model significantly improved the area under receiver operating characteristic curve from 0.64 and 0.52 to 0.82 and 0.87, respectively (P < .05). In sub-group analysis, pre- and delta-radiomics model yielded higher performance for adenocarcinoma (ADC) patients than squamous cell carcinoma (SCC) patients. Through the KM survival analysis, the rad-score of delta-radiomics model had a significant prognostic for PFS and OS in validation cohorts (P < .05). Our results demonstrated that (1) delta-radiomics model could improve the prediction performance, (2) radiomics model performed better on ADC patients than SCC patients, (3) delta-radiomics model had prognostic values in predicting PFS and OS of NSCLC patients.
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