A Clinical-Radiomics Nomogram Based on Computed Tomography for Predicting Risk of Local Recurrence After Radiotherapy in Nasopharyngeal Carcinoma

Chaohua Zhu; Huixian Huang; Xu Liu; Hao Chen; Hailan Jiang; Chaolong Liao; Qiang Pang; Junming Dang; Pei Liu; Heming Lu

doi:10.3389/fonc.2021.637687

A Clinical-Radiomics Nomogram Based on Computed Tomography for Predicting Risk of Local Recurrence After Radiotherapy in Nasopharyngeal Carcinoma

Front Oncol. 2021 Mar 18:11:637687. doi: 10.3389/fonc.2021.637687. eCollection 2021.

Authors

Chaohua Zhu¹, Huixian Huang¹, Xu Liu¹, Hao Chen¹, Hailan Jiang¹, Chaolong Liao¹, Qiang Pang¹, Junming Dang², Pei Liu³, Heming Lu¹

Affiliations

¹ Department of Radiation Oncology, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
² Department of Clinical Oncology, Guangxi University of Chinese Medicine, Nanning, China.
³ Department of Clinical Oncology, Youjiang Medical University of Nationalities, Baise, China.

Abstract

Purpose: We aimed to establish a nomogram model based on computed tomography (CT) imaging radiomic signature and clinical factors to predict the risk of local recurrence in nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). Methods: This was a retrospective study consisting of 156 NPC patients treated with IMRT. Radiomics features were extracted from the gross tumor volume for nasopharynx (GTVnx) in pretreatment CT images for patients with or without local recurrence. Discriminative radiomics features were selected after t-test and the least absolute shrinkage and selection operator (LASSO) analysis. The most stable model was obtained to generate radiomics signature (Rad_Score) by using machine learning models including Logistic Regression, K-Nearest neighbor, Naive Bayes, Decision Tree, Stochastic Gradient Descent, Gradient Booting Tree and Linear Support Vector Classification. A nomogram for local recurrence was established based on Rad_Score and clinical factors. The predictive performance of nomogram was evaluated by discrimination ability and calibration ability. Decision Curve Analysis (DCA) was used to evaluate the clinical benefits of the multi-factor nomogram in predicting local recurrence after IMRT. Results: Local recurrence occurred in 42 patients. A total of 1,452 radiomics features were initially extracted and seven stable features finally selected after LASSO analysis were used for machine learning algorithm modeling to generate Rad_Score. The nomogram showed that the greater Rad_Score was associated with the higher risk of local recurrence. The concordance index, specificity and sensitivity in the training cohort were 0.931 (95%CI:0.8765-0.9856), 91.2 and 82.8%, respectively; whereas, in the validation cohort, they were 0.799 (95%CI: 0.6458-0.9515), 79.4, and 69.2%, respectively. Conclusion: The nomogram based on radiomics signature and clinical factors can predict the risk of local recurrence after IMRT in patients with NPC and provide evidence for early clinical intervention.

Keywords: local recurrence; nasopharyngeal carcinoma; nomogram; prognostic prediction; radiomics.