多模态多分类器融合模型预测放射性口腔黏膜炎的性能

doi:10.12122/j.issn.1673-4254.2024.12.20

南方医科大学学报 ›› 2024, Vol. 44 ›› Issue (12): 2434-2442.doi: 10.12122/j.issn.1673-4254.2024.12.20

• • 上一篇

多模态多分类器融合模型预测放射性口腔黏膜炎的性能

胡玥¹(), 曾玉²(), 王琳婧³, 廖志伟³, 谭剑明³, 邝燕好², 龚攀², 齐斌³, 甄鑫¹()

^1.南方医科大学生物医学工程学院，广东广州 510515
^2.广州医科大学附属肿瘤医院口腔科，广东广州 510095
^3.广州医科大学附属肿瘤医院放射肿瘤科，广东广州 510095

收稿日期:2024-09-06 出版日期:2024-12-20 发布日期:2024-12-26
通讯作者: 曾玉,甄鑫 E-mail:huyue058@gmail.com;apple02180717@126.com;xinzhen@smu.edu.cn
作者简介:胡玥，在读硕士研究生，E-mail: huyue058@gmail.com
第一联系人：胡玥、曾玉共同第一作者
基金资助:
国家自然科学基金(62106058);广东省自然科学基金(2024A1515012100);广州市科技计划项目(202201011662);广州市重点医学学科建设项目基金和广州市重点临床技术项目(20231A010060)

Performance of multi-modality and multi-classifier fusion models for predicting radiation-induced oral mucositis in patients with nasopharyngeal carcinoma

Yue HU¹(), Yu ZENG²(), Linjing WANG³, Zhiwei LIAO³, Jianming TAN³, Yanhao KUANG², Pan GONG², Bin QI³, Xin ZHEN¹()

^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

Received:2024-09-06 Online:2024-12-20 Published:2024-12-26
Contact: Yu ZENG, Xin ZHEN E-mail:huyue058@gmail.com;apple02180717@126.com;xinzhen@smu.edu.cn
Supported by:
National Natural Science Foundation of China(62106058)

摘要/Abstract

摘要：

目的评估不同放射性口腔黏膜炎（RIOM）预测模型的性能，对比分析分层多模态多分类器融合（H-MCF）模型的有效性。方法回顾性收集2022年9月~2023年2月在广州医科大学附属肿瘤医院接受观察和治疗的198例放射性口腔黏膜炎局部晚期鼻咽癌患者的数据。基于口腔放射剂量-体积参数与鼻咽癌相关的临床特征，针对不同特征选择算法和分类器两两组合得到基础分类模型。我们使用基于多准则决策的多分类器融合模型（MCF）和它的变体——H-MCF模型对基础分类模型进行融合。通过对各个模态的基础分类模型与MCF模型的性能、多模态的基础模型和MCF模型以及H-MCF模型的性能、单模态与多模态模型的性能、H-MCF与MCF以及其他集成分类器的性能进行分析比较，并通过ROC曲线下面积（AUC）、准确率（ACC）、灵敏度（SEN）和特异度（SPE）4种评价指标来评估模型的泛化性能，分析 RIOM预测模型有效性。结果结合多模态特征后，H-MCF模型在预测严重RIOM方面达到了最高的准确性（AUC=0.883，ACC=0.850，SEN=0.933，SPE=0.800）。结论相较于单个分类器的预测结果，结合临床和剂量两种模态的多分类器融合算法在预测严重RIOM发病率方面表现更优。

关键词: 鼻咽癌, 放射性口腔黏膜炎, 人工智能, 多分类器, 多准则决策

Abstract:

Objective 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. Conclusion 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.

Key words: nasopharyngeal carcinoma, radiation-induced oral mucositis, artificial intelligence, multi classifier, multi-criterion decision-making

胡玥, 曾玉, 王琳婧, 廖志伟, 谭剑明, 邝燕好, 龚攀, 齐斌, 甄鑫. 多模态多分类器融合模型预测放射性口腔黏膜炎的性能[J]. 南方医科大学学报, 2024, 44(12): 2434-2442.

Yue HU, Yu ZENG, Linjing WANG, Zhiwei LIAO, Jianming TAN, Yanhao KUANG, Pan GONG, Bin QI, Xin ZHEN. Performance of multi-modality and multi-classifier fusion models for predicting radiation-induced oral mucositis in patients with nasopharyngeal carcinoma[J]. Journal of Southern Medical University, 2024, 44(12): 2434-2442.

图/表 9

图1 患者纳入和排除标准

Fig.1 Flowchart of inclusion and exclusion of patients with nasopharyngeal carcinoma (NPC) who experienced radiation-induced oral mucositis (RIOM) following radiotherapy.

表1 患者临床特征

Tab.1 Demographic and clinical data of the included patients

Characteristics		Training/validation set			Independent testing set
Characteristics		Grade (1-2)	Grade (3-4)	P	Grade (1-2)	Grade (3-4)	P
Age (year)	Median (range)	47 (18-70)	49 (18-75)	0.37	48 (28-72)	46 (37-67)	0.43
Height (cm)	Mean±SD	163.66±7.36	164.16±8.15	0.58	163.60±8.81	164.00±7.68	0.89
Weight (kg)	Mean±SD	61.51±10.33	60.46±12.13	0.37	62.76±9.57	60.21±10.44	0.44
Body mass index	Mean±SD	22.91±3.22	22.30±3.28	0.16	23.43±2.97	22.24±2.74	0.22
Gender	Female	23	11	0.61	18	13	0.49
Gender	Male	78	46	0.61	7	2	0.49
Tobacco use	Never	69	44	0.46	16	10	0.74
	Former	24	9		8	5
	Current	8	4		1	0
Alcohol use	Never	94	53	0.33	23	13	0.42
	Former	2	3		2	1
	Current	5	1		0	1
Number of radiotherapy fractions	Mean±SD	32.53±0.72	32.79±0.59	0.03	32.60±0.58	32.47±0.52	0.56
Total dose (Gy)	Mean±SD	70.23±0.97	70.22±1.65	0.54	70.37±0.97	70.04±0.19	0.16
Cycle of induction chemotherapy	Mean±SD	3.06±0.86	2.93±0.90	0.20	2.84±1.21	2.53±1.06	0.49
Induced chemotherapy	No	2	0	0.54	1	1	1.00
Induced chemotherapy	Yes	99	57	0.54	24	14	1.00
Reduction of induced chemotherapy doses	No	91	49	0.43	24	14	1.00
Reduction of induced chemotherapy doses	Yes	10	8	0.43	1	1	1.00
Concurrent chemo-radiotherapy	No	8	3	0.76	2	2	0.62
Concurrent chemo-radiotherapy	Yes	93	54	0.76	23	13	0.62

图2 口腔整体勾画(OCC)示意图

Fig.2 Schematic diagram of oral cavity contour (OCC).

图3 H-MCF算法流程图

Fig.3 Flowchart of the H-MCF algorithm. H-MCF: Hierarchical multi-modality and multi-classifier fusion; MCF: Multi-criterion decision-making (MCDM)-based classifier fusion.

表2 基于多准则决策多分类器融合（MCF）伪代码

Tab.2 Multi-criterion decision-making （MCDM）-based classifier fusion （MCF） pseudocode

Algorithm 1 Multi-Criterion Decision-making (MCDM) Based Classifier Fusion (MCF) Pseudocode

Input:

An evaluation matrix E $∈ R P × Q$ where $P$ is the number of classifiers, and $Q$ is the number of evaluation metrics.

Process:

Step 1: Normalize the evaluation matrix E column-wise:

$e p q' = e p q ∑ p = 1 P (e p q) 2, f o r p = 1, 2, …, P a n d q = 1, 2, …, Q .$

Step 2: Compute the weighted evaluation matrix:

$e p q'' = e p q' × ω q,$ where $ω q = 1 Q, f o r a l l q = 1,2, …, Q .$

Step 3: Compute the distance of each classifier from the "worst" and "best" solutions:

$D p, m i n = ∑ q = 1 Q (e p q'' - m i n e p q'') 2, f o r p = 1, 2, …, P .$

$D p, m a x = ∑ q = 1 Q (e p q'' - m a x e p q'') 2, f o r p = 1, 2, …, P .$

Step 4: Compute the fusion weight for each classifier:

$∅ p = D p, m i n D p, m i n + D p, m a x, f o r p = 1, 2, …, P .$

Step 5: Normalize the fusion weights:

$∅ p' = ∅ p ∑ p = 1 P ∅ p, f o r p = 1, 2, …, P .$

Step 6: Calculate the final fusion score:

$P r o b = ∑ p = 1 P ∅ p' × P r o b p$ , where $P r o b p$ is predictive probability of classifier $p$ .

Output:

Final prediction probability $P r o b$ .

表2 基于多准则决策多分类器融合（MCF）伪代码

Tab.2 Multi-criterion decision-making （MCDM）-based classifier fusion （MCF） pseudocode

Algorithm 1 Multi-Criterion Decision-making (MCDM) Based Classifier Fusion (MCF) Pseudocode

Input:

An evaluation matrix E $∈ R P × Q$ where $P$ is the number of classifiers, and $Q$ is the number of evaluation metrics.

Process:

Step 1: Normalize the evaluation matrix E column-wise:

$e p q' = e p q ∑ p = 1 P (e p q) 2, f o r p = 1, 2, …, P a n d q = 1, 2, …, Q .$

Step 2: Compute the weighted evaluation matrix:

$e p q'' = e p q' × ω q,$ where $ω q = 1 Q, f o r a l l q = 1,2, …, Q .$

Step 3: Compute the distance of each classifier from the "worst" and "best" solutions:

$D p, m i n = ∑ q = 1 Q (e p q'' - m i n e p q'') 2, f o r p = 1, 2, …, P .$

$D p, m a x = ∑ q = 1 Q (e p q'' - m a x e p q'') 2, f o r p = 1, 2, …, P .$

Step 4: Compute the fusion weight for each classifier:

$∅ p = D p, m i n D p, m i n + D p, m a x, f o r p = 1, 2, …, P .$

Step 5: Normalize the fusion weights:

$∅ p' = ∅ p ∑ p = 1 P ∅ p, f o r p = 1, 2, …, P .$

Step 6: Calculate the final fusion score:

$P r o b = ∑ p = 1 P ∅ p' × P r o b p$ , where $P r o b p$ is predictive probability of classifier $p$ .

Output:

Final prediction probability $P r o b$ .

表3 单模态基础模型和MCF模型的预测性能

Tab.3 Predictive performance of the base models and MCF models for unimodal data

Models		AUC	ACC	SEN	SPE
D_OCC	LDA+MIFS	0.568	0.650	0.533	0.720
	MLP+MIFS	0.477	0.500	0.800	0.320
	MLP+MRMR	0.477	0.500	0.800	0.320
	MCF	0.592	0.600	0.600	0.600
C	LR+ll_l21	0.779	0.750	0.600	0.840
	MLP+ll_l21	0.851	0.800	0.667	0.880
	SVM+SPEC	0.741	0.725	0.667	0.760
	MCF	0.864	0.850	0.800	0.880

图4 各模态基础模型与MCF模型的受试者工作特征(ROC)曲线

Fig.4 Receiver operating characteristic (ROC) curves of the base models and MCF models for each modality. OCC: Oral cavity contour; DOCC: DVH parameters from OCC; C: Clinical features.

表4 多模态基础模型、MCF模型和H-MCF模型的预测性能

Tab.4 Predictive performance of base models, MCF models, and H-MCF models for multimodal data

Models		AUC	ACC	SEN	SPE
C+D_OCC	MLP+CIFE	0.808	0.800	0.533	0.960
	MLP+t_score	0.816	0.775	0.800	0.760
	LR+t_score	0.856	0.850	0.800	0.880
	MCF	0.875	0.850	0.733	0.920
	H-MCF	0.883	0.850	0.933	0.800

表5 H-MCF与MCF、8种集成分类器的预测性能

Tab.5 Predictive performance of H-MCF compared with MCF and eight ensemble classifiers

Models	AUC	ACC	SEN	SPE
Extra trees+NDFS	0.715	0.700	0.800	0.640
Random forest+NDFS	0.771	0.700	0.867	0.600
Bagging+reliefF	0.812	0.750	0.800	0.720
AdaBoost+NDFS	0.856	0.825	0.733	0.880
GradientBoosting+NDFS	0.736	0.725	0.733	0.720
LightGBM+NDFS	0.816	0.750	0.733	0.760
XGBoost+NDFS	0.749	0.700	0.600	0.760
CatBoost+MCFS	0.781	0.825	0.733	0.880
MCF	0.875	0.850	0.733	0.920
H-MCF	0.883	0.850	0.933	0.800

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多模态多分类器融合模型预测放射性口腔黏膜炎的性能

Performance of multi-modality and multi-classifier fusion models for predicting radiation-induced oral mucositis in patients with nasopharyngeal carcinoma

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