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Published on: April 22, 2019
Radiomics signatures based on computed tomography for noninvasive prediction of ICOS expression and prognosis in head
Chen Peng1,2,3, Feng Liu1,2,3, Bin Wang1,2,3
1Department of Head and Neck Surgery, Shanxi Province Cancer Hospital, Taiyuan, China.
Background:
Head and neck squamous cell carcinoma (HNSCC) is highly invasive and heterogeneous, with significant differences in patients prognosis and immunotherapy efficacy. Studies have shown that inducible co-stimulator (ICOS) is a favorable prognostic factor for HNSCC.
Purpose:
This study aims to investigate the relationship between ICOS expression and the prognosis of HNSCC patients. Specifically, we aim to explore the potential of radiomic models, developed through radiomic feature extraction and selection, in predicting ICOS expression levels in HNSCC patients. By evaluating the predictive efficacy of these models, we seek to establish a noninvasive method for assessing ICOS expression, which may serve as a valuable prognostic factor in HNSCC and aid in personalized treatment strategies.
Methods:
A number of 483 HNSCC samples were extracted from The Cancer Genome Atlas (TCGA) database to investigate the relevance between ICOS expression and the survival of HNSCC patients. Moreover, 139 intersection cases from TCGA and The Cancer Imaging Archive (TCIA) databases were chosen for the extraction radiomic features and the development of radiomic models. Following the selection of radiomic features by recursive feature elimination (RFE), radiomic models were developed via logistic regression (LR) and support vector machine (SVM). Receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and decision curve analysis (DCA) were applied to evaluate the prediction efficacy of radiomic models.
Results:
ICOS was markedly relevant to the survival of HNCSS patients, with high expression of ICOS serving as a protective factor for their overall survival (HR = 0.584, 95%CI = 0.439-0.776, P < 0.001). After extraction and selection of radiomic features, LR and SVM radiomic models were developed based on the optimal five features. Furthermore, both radiomic models demonstrated strong predictive effectiveness for ICOS expression, with the SVM radiomic model exhibiting superior predictive performance.
Conclusions:
Radiomic models can noninvasively predict the expression of ICOS, which influences the prognosis of HNSCC patients.
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