Room: Exhibit Hall | Forum 6
Purpose: In this study, we retrospectively evaluated the value of pre-treatment computed tomography (CT)-based radiomics features in prediction brain metastasis (BM) for small cell lung cancer (SCLC) patients
Methods: Totally, 129 patients were enrolled in this study. Clinical and pathological features were obtained from medical records and database. The phenotype of the primary tumor was quantified on pre-treatment CT scans by 435 radiomics features, which was extracted from segmented volumes of each tumor. 62 of these, which were considered stable and independent features, were included in this analysis.Univariate and multivariate analysis was performed to evaluate radiomics performance using the concordance index (CI).
Results: High level of NSEï¼ˆOR = 1.945ï¼Œp = 0.014ï¼‰, differentiationï¼ˆOR = 1.654ï¼Œp < 0.001ï¼‰and smoking status (OR = 1.452ï¼Œp = 0.032ï¼‰were independent clinical and pathological factors for brain metastasis prediction. Statistically significant differences were found in 21 radiomics features between brain metastasis and non-brain metastasis groups in univariate analysis (CI = 0.642, p = 0.031). A multiple logistic regression model illustrated that adding radiomics features to a clinical model resulted in a significant improvement of predicting power, the AUC increased from 0.682 to 0.711 (p = 0.023).
Conclusion: This study shown that radiomics features could capture useful information about tumor phenotype, and the model we discovered can be used to predict predictive brain metastasis through pre-treatment CT in SCLC patients. In our future work we focus on inner and external validations for this model.