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一种基于整合放射学,骨形态测量和霍恩斯菲尔德单元的新方法 - - 来自常规胸部CT,用于骨矿物密度评估

Mahmoud Mohammadi-Sadr¹, Mohsen Cheki², Masoud Moslehi¹

¹Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran (M.M.S., M.M., M.R.S.).

Academic radiology

|November 19, 2024

概括

来自胸部CT的放射学和骨形态学特征有效地评估骨矿物质密度 (BMD) 状态. 一个多层感知子模型准确地区分患者的正常,骨质疏松症和骨质疏松症.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
定量成像技术定量成像技术

背景情况:

骨矿物质密度 (BMD) 评估对于诊断骨质疏松症和相关骨折至关重要.
目前的方法,如双能X射线吸收度 (DXA) 有局限性.
常规的胸部计算机断层扫描 (CT) 为BMD评估提供了一个潜在的替代方案.

研究的目的:

为了评估使用放射学特征,骨形态测量 (BM) 特征和从常规胸部CT进行BMD评估的Hounsfield单位 (HU) 值的可行性.
开发和验证一个分类模型来区分不同的BMD状态.

主要方法:

对120名同时进行胸部CT和DXA扫描的患者进行了回顾性分析.
胸椎椎体的半自动细分和放射性,BM和HU特征的提取.
使用类内相关系数和Boruta算法进行特征选择,然后使用六种机器学习模型进行分类.

主要成果:

观测者内部和观测者之间的高度细分协议 (迪斯相似系数>0.88).
在逐步选择后,选择了21个放射学特征.
多层感知子 (MLP) 模型整合了放射学和BM特征,在正常 (0.981),骨质疏松症 (0.896) 和骨质疏松症 (0.927) BMD类中实现了高AUC值.

结论:

常规胸部CT的放射学和BM特征对于BMD评估是可行的.
该MLP模型有效地分类不同的BMD状态,显示高诊断性能.
这种方法提供了一种使用现有的CT数据进行骨损伤评估的非侵入性方法.

关键词:

骨矿物质密度骨矿物质密度胸部CT CT 的情况.机器学习是机器学习.骨质疏松症是一种骨质疏松症.无线电学 (Radiomics) 是一种无线电学.

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