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

Abstract

IntroductionIt 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.MethodsA 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.ResultsIncreased 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).DiscussionThe CECT-based radiomics model provides preoperative IL1B expression predictionand offers non-invasive instructions for the prognosis and individualized treatment of patients withHNSCC.

The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma

“…Texture features were derived from analyzing the relative spatial distribution and intensity levels of voxels to characterize the structural texture of two-dimensional or three-dimensional images [ 33 ]. Numerous studies have demonstrated that texture features provide valuable insights into tumor differentiation, pathological typing, radiogenomics, prognostic prediction, and more, as they reflect structural texture differences in intratumoral tissue anatomy [ 34 – 36 ].…”

A radiomics model to predict γδ T‐cell abundance and overall survival in head and neck squamous cell carcinoma

“…Recently, radiomics has emerged into basic and clinical medicine and has expanded our knowledge of the radiomic and functional characteristics of HNSCC. Several studies have shown that CT‐based radiomics can be used to diagnose, classify, and predict the prognosis of HNSCC 23,47 . Hence, CT‐based radiomics is suitable for assessing the heterogeneity of HNSCC.…”

“…Several studies have shown that CT-based radiomics can be used to diagnose, classify, and predict the prognosis of HNSCC. 23,47 Hence, CT-based radiomics is suitable for assessing the heterogeneity of HNSCC.…”

“…Radiomics has been widely applied in clinical diagnosis and precision medicine. A growing body of studies has indicated the ability of noninvasive evaluation via radiomics for early diagnosis 20 and molecular typing 22 of HNSCC, as well as for predicting the tumor immune microenvironment (TIME) and heterogeneity 23 . However, to our knowledge, there are no published studies on the prediction of γδ T cell abundance in patients with HNSCC.…”

“…head and neck squamous cell carcinoma, machine learning, overall survival, radiomics, γδ T cells HNSCC, as well as for predicting the tumor immune microenvironment (TIME) and heterogeneity. 23 However, to our knowledge, there are no published studies on the prediction of γδ T cell abundance in patients with HNSCC.…”