Okamura Yuki T, Endo Katsuhiro, Toriihara Akira, Fukuda Issei, Isogai Jun, Sato Yasunori, Yasuoka Kenji, Kagami Shin-Ichiro
Department of Internal Medicine, Asahi General Hospital, 1326 I, Asahi, Chiba, Japan.
Department of Mechanical Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, Japan.
Ann Biomed Eng. 2025 Jul 19. doi: 10.1007/s10439-025-03805-z.
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.
胸腺是参与T细胞生成的重要免疫器官。近期研究表明，与年龄相关的胸腺退化与多种年龄相关的病理状况有关。然而，尚未有基于临床图像对与年龄相关的胸腺退化进行量化的方法被提出。本研究的目的是建立一种基于胸部平扫计算机断层扫描（CT）图像对与年龄相关的胸腺退化进行客观、自动量化的方法。我们新定义了用于量化的胸腺区域（TRQ）作为目标解剖区域。我们在135例CT研究中手动分割TRQ，随后使用这些数据构建分割神经网络（NN）模型。基于简单的数学建模，我们开发了胸腺体积估计器（ETV），它是分割后的TRQ内胸腺组织体积的定量指标。测量了NN分割的TRQ的亨氏单位（HU）值和体积，并在853名健康受试者的每项CT研究中计算ETV。我们使用分位数加法回归模型研究了这些测量值与年龄和性别的关系。对于HU值和体积，NN分割的TRQ与手动分割的TRQ之间均存在显著相关性（分别为r = 0.996和r = 0.986）。ETV随年龄呈指数下降（p < 0.001），这与胸腺组织体积随年龄的下降一致。总之，我们的方法能够对与年龄相关的胸腺退化进行可靠量化。我们的方法可能有助于预测与胸腺退化相关的病理状况并进行风险分类。
