Performance of multi-modality and multi-classifier fusion models for predicting radiation-induced oral mucositis in patients with nasopharyngeal carcinoma.
10.12122/j.issn.1673-4254.2024.12.20
- Author:
Yue HU
1
;
Yu ZENG
2
;
Linjing WANG
3
;
Zhiwei LIAO
3
;
Jianming TAN
3
;
Yanhao KUANG
2
;
Pan GONG
2
;
Bin QI
3
;
Xin ZHEN
1
Author Information
1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
2. Department of Stomatology.
3. Department of Radiation Oncology, Guangzhou Institute of Cancer Research, Affiliated Cancer Hospital of Guangzhou Medical University, Guangzhou 510095, China.
- Publication Type:Journal Article
- Keywords:
artificial intelligence;
multi classifier;
multi-criterion decision-making;
nasopharyngeal carcinoma;
radiation-induced oral mucositis
- MeSH:
Humans;
Nasopharyngeal Carcinoma/radiotherapy*;
Nasopharyngeal Neoplasms/radiotherapy*;
Retrospective Studies;
Stomatitis/diagnosis*;
Algorithms;
Radiation Injuries/diagnosis*;
Female;
Male;
ROC Curve
- From:
Journal of Southern Medical University
2024;44(12):2434-2442
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:To evaluate the performance of different multi-modality fusion models for predicting radiation-induced oral mucositis (RIOM) following radiotherapy in patients with nasopharyngeal carcinoma (NPC).
METHODS:We retrospectively collected the data from 198 patients with locally advanced NPC who experienced RIOM following radiotherapy at the Affiliated Tumor Hospital of Guangzhou Medical University from September, 2022 to February, 2023. Based on oral radiation dose-volume parameters and clinical features of NPC, basic classification models were developed using different combinations of feature selection algorithms and classifiers and integrated using a multi-criterion decision-making (MCDM)-based classifier fusion (MCF) strategy and its variant, the H-MCF model. The basic classification models, MCF model, the H-MCF model with a single modality or multiple modalities and other ensemble classifiers were compared for performances for predicting RIOM by assessing the area under the ROC curve (AUC), accuracy, sensitivity, and specificity.
RESULTS:The H-MCF model, which integrated multi-modality features, achieved the highest accuracy for predicting severe RIOM with an AUC of 0.883, accuracy of 0.850, sensitivity of 0.933, and specificity of 0.800.
CONCLUSIONS:Compared with each of the individual classifiers, the multimodal multi-classifier fusion algorithm combining clinical and dosimetric modalities demonstrates superior performance in predicting the incidence of severe RIOM in NPC patients following radiotherapy.
