Automated quantitative evaluation of thymic involution and hyperplasia on plain chest CT

Abstract

AbstractObjectiveTo establish an automatic method to quantify thymic involution and hyperplasia based on plain chest computed tomography (CT).MethodsWe defined the thymic region for quantification (TRQ) as the target region. We manually segmented the TRQ in 135 CT studies, followed by construction of segmentation neural network (NN) models based on the data. We developed the estimator of thymic volume (ETV), a measure of the thymic tissue volume in the segmented TRQ. The Hounsfield unit (HU) value and volume of the TRQ were measured, and the ETV was calculated in each CT study from 853 healthy subjects. We investigated how these measures were related to the age and sex using quantile additive regression models. We defined the ETV z-score, an age- and sex-adjusted version of ETV, to distinguish between subjects with thymic hyperplasia (18 cases) and healthy subjects. A receiver operating characteristic (ROC) curve analysis was conducted.ResultsA significant correlation between the NN-segmented and manually segmented TRQ was seen for both the HU value and volume of the TRQ (r= 0.996 andr= 0.986 respectively). The ETV could detect age-related decline in the thymic tissue volume (p< 0.001). No statistically significant difference was detected between male and female subjects (p= 0.19). The ETV was significantly higher in the thymic hyperplasia group as compared with that in the healthy control group (p< 0.001). The ETV z-score could distinguish between subjects with thymic hyperplasia and healthy subjects, with the ROC curve analysis revealing an area under the curve (AUC) of 0.88 (95% CI: 0.75-1.0).ConclusionOur method enabled robust quantification of thymic involution and hyperplasia. The results were consistent with the trends found in previous studies.Clinical Relevance StatementOur method allows reliable and automatic measurement of thymic involution and hyperplasia on CT images. This may aid in the early detection and monitoring of pathologies related to the thymus, including autoimmune diseases.Key Points-We defined the thymic region for quantification (TRQ) to fully automate the evaluation of thymic involution and hyperplasia. The neural networks could identify the TRQ with sufficient accuracy.-We developed the estimator of thymic volume (ETV) to quantify the thymic tissue in the TRQ. ETV captured age-related thymic involution and thymic hyperplasia.-The ETV could prove useful in the management of pathologies associated with involution or hyperplasia of the thymus.