Using MRI radiomics to predict the efficacy of immunotherapy for brain metastasis in patients with small cell lung cancer

• Published in: Thoracic cancer 2024-03, Vol.15 (9), p.738-748
• Main Authors: Shi, Xiaonan, Wang, Peiliang, Li, Yikun, Xu, Junhao, Yin, Tianwen, Teng, Feifei
• Format: Article
• Language: English
• Subjects: Biomarkers; Biopsy; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - drug therapy; Brain research; Cancer therapies; Chemotherapy; Clinical outcomes; Humans; immune checkpoint inhibitors; Immunotherapy; Lung cancer; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - drug therapy; Magnetic Resonance Imaging; Medical prognosis; Metastasis; nomogram; Nomograms; Oncology; Original; prediction; Radiomics; small cell lung cancer; Small Cell Lung Carcinoma - diagnostic imaging; Small Cell Lung Carcinoma - drug therapy; Survival analysis; Tomography; Tumors
• ISSN: 1759-7706; 1759-7714; 1759-7714
• DOI: 10.1111/1759-7714.15259

Background Brain 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. Methods The 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. Results Ten 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). Conclusion A 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. We developed and validated a radiomics nomogram prediction model for predicting the intracranial efficacy of immune checkpoint inhibitors (ICIs) in small cell lung cancer (SCLC) patients with brain metastases (BMs). This study can help tailor their treatment schedules, improve clinical decision‐making, and offer extensive potential applications.