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. 2022 Oct 13;12:955866. doi: 10.3389/fonc.2022.955866

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Copyright © 2022 Li, Wu, Li, Dai, Liao, Yang, Chen and Huang

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Workflow of radiomics analysis for predicting the prognosis of advanced nasopharyngeal carcinoma. The steps were: (1) Three-dimensional manual segmentation on K^trans and V_e images, (2) the calculation of six types of features for each patient from the defined segmentation, (3) LASSO Cox regression for feature selection and data dimension reduction, and (4) multivariate Cox regression to develop a radiomics nomogram model. Kaplan-Meier analyses were then performed to assess the prognostic value of the model. Abbreviations: VOI, volume of interest; GLSZM, gray-level size zone matrix; GLCM, gray-level co-occurrence matrix; RLM, run-length matrix; LASSO, least absolute shrinkage and selection operator; C-index, Harrell’s concordance index; K–M curve, Kaplan–Meier analysis curve.