Automated Quantitative Evaluation of Age-Related Thymic Involution on Plain Chest CT

IF 5.4 2区医学 Q3 ENGINEERING, BIOMEDICAL

Annals of Biomedical Engineering Pub Date : 2025-07-19 DOI:10.1007/s10439-025-03805-z

Yuki T. Okamura, Katsuhiro Endo, Akira Toriihara, Issei Fukuda, Jun Isogai, Yasunori Sato, Kenji Yasuoka, Shin-Ichiro Kagami

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引用次数: 0

Abstract

The thymus is an important immune organ involved in T-cell generation. Age-related involution of the thymus has been linked to various age-related pathologies in recent studies. However, there has been no method proposed to quantify age-related thymic involution based on a clinical image. The purpose of this study was to establish an objective and automatic method to quantify age-related thymic involution based on plain chest computed tomography (CT) images. We newly defined the thymic region for quantification (TRQ) as the target anatomical region. We manually segmented the TRQ in 135 CT studies, followed by construction of segmentation neural network (NN) models using the data. We developed the estimator of thymic volume (ETV), a quantitative indicator of the thymic tissue volume inside the segmented TRQ, based on simple mathematical modeling. The Hounsfield unit (HU) value and volume of the NN-segmented TRQ were measured, and the ETV was calculated in each CT study from 853 healthy subjects. We investigated how these measures were related to age and sex using quantile additive regression models. A significant correlation between the NN-segmented and manually segmented TRQ was seen for both the HU value and volume (r = 0.996 and r = 0.986, respectively). ETV declined exponentially with age (p < 0.001), consistent with age-related decline in the thymic tissue volume. In conclusion, our method enabled robust quantification of age-related thymic involution. Our method may aid in the prediction and risk classification of pathologies related to thymic involution.

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胸腺退化在平胸CT上的自动定量评价。

胸腺是参与t细胞生成的重要免疫器官。在最近的研究中，年龄相关的胸腺退化与各种年龄相关的病理有关。然而，目前还没有一种方法可以根据临床图像来量化与年龄相关的胸腺退化。本研究的目的是建立一种客观、自动的方法来量化基于胸部CT图像的年龄相关胸腺退化。我们新的定义胸腺区定量（TRQ）作为目标解剖区域。我们对135项CT研究中的TRQ进行了人工分割，然后利用这些数据构建了分割神经网络（NN）模型。基于简单的数学模型，我们开发了胸腺体积（ETV）的估计器，这是分割后TRQ内胸腺组织体积的定量指标。在853名健康受试者的每项CT研究中，测量神经网络分段TRQ的Hounsfield单位（HU）值和体积，并计算ETV。我们使用分位数加性回归模型研究了这些测量与年龄和性别的关系。在HU值和体积上，神经网络分割的TRQ与人工分割的TRQ有显著的相关性（r = 0.996和r = 0.986）。ETV随年龄呈指数下降(p

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

Annals of Biomedical Engineering 工程技术-工程：生物医学

CiteScore

7.50

自引率

15.80%

发文量

212

审稿时长

3 months

期刊介绍： Annals of Biomedical Engineering is an official journal of the Biomedical Engineering Society, publishing original articles in the major fields of bioengineering and biomedical engineering. The Annals is an interdisciplinary and international journal with the aim to highlight integrated approaches to the solutions of biological and biomedical problems.