Improving Diagnostic Performance for Head and Neck Tumors with Simple Diffusion Kurtosis Imaging and Machine Learning Bi-Parameter Analysis.

IF 3 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Suzuka Yoshida, Masahiro Kuroda, Yoshihide Nakamura, Yuka Fukumura, Yuki Nakamitsu, Wlla E Al-Hammad, Kazuhiro Kuroda, Yudai Shimizu, Yoshinori Tanabe, Masataka Oita, Irfan Sugianto, Majd Barham, Nouha Tekiki, Nurul N Kamaruddin, Miki Hisatomi, Yoshinobu Yanagi, Junichi Asaumi
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引用次数: 0

Abstract

Background/Objectives: Mean kurtosis (MK) values in simple diffusion kurtosis imaging (SDI)-a type of diffusion kurtosis imaging (DKI)-have been reported to be useful in the diagnosis of head and neck malignancies, for which pre-processing with smoothing filters has been reported to improve the diagnostic accuracy. Multi-parameter analysis using DKI in combination with other image types has recently been reported to improve the diagnostic performance. The purpose of this study was to evaluate the usefulness of machine learning (ML)-based multi-parameter analysis using the MK and apparent diffusion coefficient (ADC) values-which can be acquired simultaneously through SDI-for the differential diagnosis of benign and malignant head and neck tumors, which is important for determining the treatment strategy, as well as examining the usefulness of filter pre-processing. Methods: A total of 32 pathologically diagnosed head and neck tumors were included in the study, and a Gaussian filter was used for image pre-processing. MK and ADC values were extracted from pixels within the tumor area and used as explanatory variables. Five ML algorithms were used to create models for the prediction of tumor status (benign or malignant), which were evaluated through ROC analysis. Results: Bi-parameter analysis with gradient boosting achieved the best diagnostic performance, with an AUC of 0.81. Conclusions: The usefulness of bi-parameter analysis with ML methods for the differential diagnosis of benign and malignant head and neck tumors using SDI data were demonstrated.

应用简单弥散峰度成像和机器学习双参数分析提高头颈部肿瘤的诊断性能。
背景/目的:简单扩散峰度成像(SDI)的平均峰度(MK)值-一种扩散峰度成像(DKI)-已被报道用于头颈部恶性肿瘤的诊断,其中预处理平滑滤波器已被报道以提高诊断准确性。使用DKI与其他图像类型相结合的多参数分析最近被报道用于提高诊断性能。本研究的目的是评估基于机器学习(ML)的多参数分析的有用性,使用MK和表观扩散系数(ADC)值-可以通过sdi同时获得-用于良性和恶性头颈部肿瘤的鉴别诊断,这对于确定治疗策略很重要,以及检查过滤器预处理的有用性。方法:选取32例经病理诊断的头颈部肿瘤,采用高斯滤波对图像进行预处理。从肿瘤区域内的像素提取MK和ADC值作为解释变量。使用五种ML算法建立预测肿瘤状态(良性或恶性)的模型,并通过ROC分析对其进行评估。结果:梯度增强双参数分析诊断效果最佳,AUC为0.81。结论:双参数分析与ML方法对使用SDI数据鉴别头颈部良恶性肿瘤的有效性得到证实。
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
Diagnostics
Diagnostics Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
4.70
自引率
8.30%
发文量
2699
审稿时长
19.64 days
期刊介绍: Diagnostics (ISSN 2075-4418) is an international scholarly open access journal on medical diagnostics. It publishes original research articles, reviews, communications and short notes on the research and development of medical diagnostics. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodological details must be provided for research articles.
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