Development and validation of a CECT-based radiomics model for predicting IL1B expression and prognosis of head and neck squamous cell carcinoma

Yang Xie; Min Wang; Haibin Xia; Huifang Sun; Yi Yuan; Jun Jia; Liangwen Chen

doi:10.3389/fonc.2023.1121485

Development and validation of a CECT-based radiomics model for predicting IL1B expression and prognosis of head and neck squamous cell carcinoma

Front Oncol. 2023 Mar 10:13:1121485. doi: 10.3389/fonc.2023.1121485. eCollection 2023.

Authors

Yang Xie¹, Min Wang², Haibin Xia², Huifang Sun¹, Yi Yuan³, Jun Jia⁴, Liangwen Chen¹

Affiliations

¹ The State Key Laboratory Breeding Base of Basic Science of Stomatology, Hubei Province and Key Laboratory of Oral Biomedicine (Hubei-MOST and KLOBM), School and Hospital of Stomatology, Wuhan University, Wuhan, China.
² Hubei-MOST and KLOBM, Department of Oral Implantology, School and Hospital of Stomatology, Wuhan University, Wuhan, China.
³ Department of Oral Radiology, School and Hospital of Stomatology, Wuhan University, Wuhan, China.
⁴ Department of Oral Maxillofacial-Head Neck Oncology, School and Hospital of Stomatology, Wuhan University, Wuhan, China.

Abstract

Introduction: It is necessary to explore a noninvasive method to stratify head and neck squamous cell carcinoma (HNSCC)'s prognosis and to seek new indicators for individualized precision treatment. As a vital inflammatory cytokine, IL1B might drive a new tumor subtype that could be reflected in overall survival (OS) and predicted using the radiomics method.

Methods: A total of 139 patients with RNA-Seq data from The Cancer Genome Atlas (TCGA) and matched CECT data from The Cancer Image Archive (TCIA) were included in the analysis. The prognostic value of IL1B expression in patients with HNSCC was analyzed using Kaplan-Meier analysis, Cox regression analysis and subgroup analysis. Furthermore, the molecular function of IL1B on HNSCC was explored using function enrichment and immunocytes infiltration analyses. Radiomic features were extracted with PyRadiomics and processed using max-relevance minredundancy, recursive feature elimination, and gradient boosting machine algorithm to construct aradiomics model for predicting IL1B expression. The area under the receiver operating characteristic curve (AUC), calibration curve, precision recall (PR) curve, and decision curve analysis (DCA) curve were used to examine the performance of the model.

Results: Increased IL1B expression in patients with HNSCC indicated a poor prognosis (hazard ratio [HR] = 1.56, P = 0.003) and was harmful in patients who underwent radiotherapy (HR = 1.87, P = 0.007) or chemotherapy (HR = 2.514, P < 0.001). Shape_Sphericity, glszm_SmallAreaEmphasis, and firstorder_Kurtosis were included in the radiomics model (AUC: training cohort, 0.861; validation cohort, 0.703). The calibration curves, PR curves and DCA showed good diagnostic effect of the model. The rad-score was close related to IL1B (P = 4.490*10-9), and shared the same corelated trend to EMT-related genes with IL1B. A higher rad-score was associated with worse overall survival (P = 0.041).

Discussion: The CECT-based radiomics model provides preoperative IL1B expression predictionand offers non-invasive instructions for the prognosis and individualized treatment of patients withHNSCC.

Keywords: IL1B; contrast-enhanced computed tomography; head and neck squamous cell carcinoma; prognosis; radiomics model.

Grants and funding

This research was funded by the Science and Technology Foundation of Hubei Province, grant number 2021CFB147.