ArticleCancers2023
A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.Diagnostics (Basel, Switzerland) · 2026Article
- Review
- Oxidative Stress and Inflammation in Colorectal Cancer-Redox-Immune Crosstalk, Biomarkers, and Translational Implications: A Qualitative Systematic Review.Life (Basel, Switzerland) · 2026Review
- Digital breast tomosynthesis-based radiomics for prediction of prognosis in breast cancer: a multicenter study.Gland surgery · 2026Article
- Global research trends on biomarkers for cancer immunotherapy: Visualization and bibliometric analysis.Human vaccines & immunotherapeutics · 2025Article
- Article
- Differential Radiomics-Based Signature Predicts Lung Cancer Risk Accounting for Imaging Parameters in NLST Cohort.Cancer medicine · 2024Article
- Review
- Article
- Novel cryopreservation medium for enhanced stability of T cells at -80°C.Frontiers in hematology · 2024Article
- Imaging-Based Biomarkers Predict Programmed Death-Ligand 1 and Survival Outcomes in Advanced NSCLC Treated With Nivolumab and Pembrolizumab: A Multi-Institutional Study.JTO clinical and research reports · 2023Article
- Predicting response to immunotherapy in non-small cell lung cancer- from bench to bedside.Frontiers in oncology · 2023Article
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Authors and funding
12 authors.
Funding
Abstract
backgroundImmune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy.
methodsThis retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort.
resultsWe developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively.
conclusionsWe developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted.
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