ArticleCancers2023
Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 44 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
- Delta radiomics: an updated systematic review.La Radiologia medica · 2024Pooled it
- CT-based radiomics as a non-invasive virtual biopsy for high PD-L1 expression prediction in non-small cell lung cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Artificial Intelligence for Prognostic Modelling and Adaptive Treatment Monitoring in Radiation Oncology.Cureus · 2026Review
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- Integrated nomogram for predicting intracranial progression-free survival with EGFR-TKI combined with cranial radiotherapy: a multicenter study.European radiology · 2026Article
- Article
- Deep-Learning Serial CT Prediction of Survival in Immunotherapy-Treated Non-Small Cell Lung Cancer.JAMA network open · 2026Article
- Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.IEEE reviews in biomedical engineering · 2026Review
- Predictive value of 18F-FDG PET/CT delta radiomics model for prognosis after neoadjuvant therapy for locally advanced pancreatic cancer.Frontiers in oncology · 2026Article
- Quantification of head and neck cancer patients' anatomical changes during radiotherapy: Toward the prediction of replanning need.Journal of applied clinical medical physics · 2026Article
- Longitudinal CT-based deep learning radiomics for predicting prognosis in esophageal squamous cell carcinoma treated with definitive chemoradiotherapy: a two-center study.Journal of Cancer · 2026Article
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- Multimodal integration of longitudinal noninvasive diagnostics for survival prediction in immunotherapy using deep learning.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Article
- Prediction of radiosensitivity in non-small cell lung cancer based on computed tomography and tumor genomics: a multiple real world cohort study.Respiratory research · 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
- Delta Radiomics and Tumor Size: A New Predictive Radiomics Model for Chemotherapy Response in Liver Metastases from Breast and Colorectal Cancer.Tomography (Ann Arbor, Mich.) · 2025Article
- Radiomics-Based Prediction of Treatment Response to TRuC-T Cell Therapy in Patients with Mesothelioma: A Pilot Study.Cancers · 2025Article
- From images to clinical insights: an educational review on radiomics in lung diseases.Breathe (Sheffield, England) · 2025Review
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of our study was to determine the potential role of CT-based radiomics in predicting treatment response and survival in patients with advanced NSCLC treated with immune checkpoint inhibitors. We retrospectively included 188 patients with NSCLC treated with PD-1/PD-L1 inhibitors from two independent centers. Radiomics analysis was performed on pre-treatment contrast-enhanced CT. A delta-radiomics analysis was also conducted on a subset of 160 patients who underwent a follow-up contrast-enhanced CT after 2 to 4 treatment cycles. Linear and random forest (RF) models were tested to predict response at 6 months and overall survival. Models based on clinical parameters only and combined clinical and radiomics models were also tested and compared to the radiomics and delta-radiomics models. The RF delta-radiomics model showed the best performance for response prediction with an AUC of 0.8 (95% CI: 0.65-0.95) on the external test dataset. The Cox regression delta-radiomics model was the most accurate at predicting survival with a concordance index of 0.68 (95% CI: 0.56-0.80) (
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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.