A Stepwise decision tree model for differential diagnosis of Kimura's disease in the head and neck.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Rui Luo, Gongxin Yang, Huimin Shi, Yining He, Yongshun Han, Zhen Tian, Yingwei Wu
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引用次数: 0

Abstract

Objectives: This study aims to differentiate Kimura's disease (KD) from Sjogren's syndrome with mucosa-associated lymphoid tissue lymphoma (SS&MALT), neurofibromatosis (NF), and lymphoma in the head and neck by using a stepwise decision tree approach.

Materials and methods: A retrospective analysis of 202 patients with pathologically confirmed KD, SS&MALT, NF, or lymphoma was conducted. Demographic and magnetic resonance imaging (MRI) data were collected, with qualitative features (e.g., skin thickening, lesion morphology, lymphadenopathy, MRI signal intensity) and quantitative variables (e.g., age, lesion size, apparent diffusion coefficients (ADCs), wash-in rate, time to peak (TTP), time-signal intensity curve (TIC) patterns) examined. A stepwise decision-tree model using the classification and regression trees (CART) algorithm was developed to aid in the differential diagnosis of KD in the head and neck. The model's diagnostic accuracy and misclassification risk were assessed to evaluate its reliability and effectiveness.

Results: Key characteristics for KD included male predominance (91.7%), frequent lymphadenopathy (86.1%), and skin thickening (72.2%). Primary lesions of NF typically exhibited higher ADCs compared to those of KD, SS&MALT, and lymphoma. In lymphadenopathy, however, unique ADC patterns were observed: in KD, the ADCs of lymphadenopathy were lower than those of primary lesions, whereas in lymphoma, the ADCs of lymphadenopathy were comparable to those of primary lesions. Predictors for distinguishing KD included lesion's location, ADCs, lymphadenopathy, and sizes (all p < 0.001). The decision-tree model achieved an impressive 99.0% accuracy in the differential diagnosis across the overall cohort, with a 10-fold cross-validated misclassification risk of 0.079 ± 0.024.

Conclusion: The stepwise decision tree model, based on MRI features, showed high accuracy in differentiating KD from other head and neck diseases, offering a reliable diagnostic tool in clinical practice.

Clinical relevance: KD is characterized by male predominance, skin thickening, and high incidence of lymphadenopathy. ADCs and TIC patterns are distinguishable in differentiating KD from SS&MALT, NF, and lymphoma in the head and neck. The decision tree model enhances the understanding of KD imaging features and facilitates accurate KD diagnosis, offering an easily accessible and convenient diagnostic tool for radiologists and physicians in daily practice and guiding tailored clinical management plans for affected patients.

Clinical trial number: Not applicable.

用于头颈部木村病鉴别诊断的逐步决策树模型。
目的:本研究旨在通过逐步决策树方法区分木村病(KD)与干燥综合征合并粘膜相关淋巴组织淋巴瘤(SS&MALT)、神经纤维瘤病(NF)和头颈部淋巴瘤。材料和方法:回顾性分析202例病理证实的KD、SS&MALT、NF或淋巴瘤患者。收集人口统计学和磁共振成像(MRI)数据,检查定性特征(如皮肤增厚、病变形态、淋巴结病变、MRI信号强度)和定量变量(如年龄、病变大小、表观扩散系数(adc)、冲洗率、峰值时间(TTP)、时间-信号强度曲线(TIC)模式)。利用分类和回归树(CART)算法建立了一个逐步决策树模型,以帮助头颈部KD的鉴别诊断。通过对模型的诊断准确率和误分类风险进行评估,评价模型的可靠性和有效性。结果:KD的主要特征为男性占优势(91.7%)、多发淋巴结病变(86.1%)和皮肤增厚(72.2%)。与KD、SS&MALT和淋巴瘤相比,NF的原发性病变通常表现出更高的adc。然而,在淋巴结病中,观察到独特的ADC模式:在KD中,淋巴结病的ADC低于原发病变,而在淋巴瘤中,淋巴结病的ADC与原发病变相当。鉴别KD的预测因子包括病变位置、adc、淋巴结病变和大小(均为p)。结论:基于MRI特征的逐步决策树模型在鉴别KD与其他头颈部疾病方面具有较高的准确性,为临床提供了可靠的诊断工具。临床相关性:KD以男性为主,皮肤增厚,淋巴结病高发为特征。adc和TIC模式可用于区分KD与SS&MALT、NF和头颈部淋巴瘤。决策树模型增强了对KD影像特征的理解,促进了KD的准确诊断,为放射科医生和医生在日常实践中提供了一种易于获取和方便的诊断工具,并为受影响患者提供了量身定制的临床管理计划。临床试验号:不适用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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