Development of a neoadjuvant chemotherapy efficacy prediction model for nasopharyngeal carcinoma integrating magnetic resonance radiomics and pathomics: a multi-center retrospective study

Development of a neoadjuvant chemotherapy efficacy prediction model for nasopharyngeal carcinoma integrating magnetic resonance radiomics and pathomics: a multi-center retrospective study

Abstract Objective This study aimed to develop and validate a predictive model for assessing the efficacy of neoadjuvant chemotherapy (NACT) in nasopharyngeal carcinoma (NPC) by integrating radiomics and pathomics features using a particle swarm optimization-supported support vector machine (PSO-SVM...

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Bibliographic Details
Main Authors: Yiren Wang, Huaiwen Zhang, Huan Wang, Yiheng Hu, Zhongjian Wen, Hairui Deng, Delong Huang, Li Xiang, Yun Zheng, Lu Yang, Lei Su, Yunfei Li, Fang Liu, Peng Wang, Shengmin Guo, Haowen Pang, Ping Zhou
Format: Article
Language:English
Published: BMC 2024-12-01
Series:BMC Cancer
Subjects:
Multimodal
Radiomics
Machine learning
Pathomics
Prediction model
Nasopharyngeal Carcinoma
Online Access:https://doi.org/10.1186/s12885-024-13235-0

Internet

https://doi.org/10.1186/s12885-024-13235-0

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