Multimodality radiomics for tumor prognosis in nasopharyngeal carcinoma

by Sararas Khongwirotphan, Sornjarod Oonsiri, Sarin Kitpanit, Anussara Prayongrat, Danita Kannarunimit, Chakkapong Chakkabat, Chawalit Lertbutsayanukul, Sira Sriswasdi, Yothin Rakvongthai BackgroundThe prognosis of nasopharyngeal carcinoma (NPC) is challenging due to late-stage identification and frequently undetectable Epstein-Barr virus (EBV) DNA. Incorporating radiomic features, which quantify tumor characteristics from imaging, may enhance prognosis assessment. PurposeTo investigate the predictive power of radiomic features on overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) in NPC. Materials and methodsA retrospective analysis of 183 NPC patients treated with chemoradiotherapy from 2010 to 2019 was conducted. All patients were followed for at least three years. The pretreatment CT images with contrast medium, MR images (T1W and T2W), as well as gross tumor volume (GTV) contours, were used to extract radiomic features using PyRadiomics v.2.0. Robust and efficient radiomic features were chosen using the intraclass correlation test and univariate Cox proportional hazard regression analysis. They were then combined with clinical data including age, gender, tumor stage, and EBV DNA level for prognostic evaluation using Cox proportional hazard regression models with recursive feature elimination (RFE) and were optimized using 20 repetitions of a five-fold cross-validation scheme. ResultsIntegrating radiomics with clinical data...
Source: PLoS One - Category: Biomedical Science Authors: Source Type: research