ArticleThoracic cancer2024
Using MRI radiomics to predict the efficacy of immunotherapy for brain metastasis in patients with small cell lung cancer.
Article in Thoracic cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Alternative Splicing in Tumorigenesis and Cancer Therapy.Biomolecules · 2025Review
- Ultrasound-Based Deep Learning Radiomics Models for Predicting Primary and Secondary Salivary Gland Malignancies: A Multicenter Retrospective Study.Bioengineering (Basel, Switzerland) · 2025Article
- Prediction of intracranial response to PD-1/PD-L1 inhibitors therapy in brain metastases originating from non-small cell lung cancer using habitat imaging and peritumoral radiomics: a multicenter study.Frontiers in oncology · 2025Article
- Using MRI radiomics to predict the efficacy of immunotherapy for brain metastasis in patients with small cell lung cancer.Thoracic cancer · 2024Article
- Enhancing intracranial efficacy prediction of osimertinib in non-small cell lung cancer: a novel approach through brain MRI radiomics.Frontiers in neurology · 2024Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundBrain metastases (BMs) are common in small cell lung cancer (SCLC), and the efficacy of immune checkpoint inhibitors (ICIs) in these patients is uncertain. In this study we aimed to develop and validate a radiomics nomogram based on magnetic resonance imaging (MRI) for intracranial efficacy prediction of ICIs in patients with BMs from SCLC.
methodsThe training and validation cohorts consisted of 101 patients from two centers. The interclass correlation coefficient (ICC), logistic univariate regression analysis, and random forest were applied to select the radiomic features, generating the radiomics score (Rad-score) through the formula. Using multivariable logistic regression analysis, a nomogram was created by the combined model. The discrimination, calibration, and clinical utility were used to assess the performance of the nomogram. Kaplan-Meier curves were plotted based on the nomogram scores.
resultsTen radiomic features were selected for calculating the Rad-score as they could differentiate the intracranial efficacy in the training (area under the curve [AUC], 0.759) and the validation cohort (AUC, 0.667). A nomogram was created by combining Rad-score, treatment lines, and neutrophil-to-lymphocyte ratio (NLR). The training cohort obtained an AUC of 0.878 for the combined model, verified in the validation cohort (AUC = 0.875). Kaplan-Meier analyses showed the nomogram was associated with progression-free survival (PFS) (p = 0.0152) and intracranial progression-free survival (iPFS) (p = 0.0052) but not overall survival (OS) (p = 0.4894).
conclusionA radiomics nomogram model for predicting the intracranial efficacy of ICIs in SCLC patients with BMs can provide suggestions for exploring individual-based treatments for patients.
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