Cheng Qiye, Zhang Jingyi, Hu Mengting, Wang Shigeng, Liu Yijun, Li Jianying, Wei Wei
Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116000, China.
CT Research, GE Healthcare, Dalian 116000, China.
Bioengineering (Basel). 2024 Dec 12;11(12):1257. doi: 10.3390/bioengineering11121257.
The dual-energy spectral CT (DEsCT) employs material decomposition (MD) technology, opening up novel avenues for the opportunistic assessment of bone status. Radiomics, a powerful tool for elucidating the structural and textural characteristics of bone, aids in the detection of mineral loss. Therefore, this study aims to compare the efficacy of bone status assessment using both bone density measurements and radiomics models derived from MD images and to further explore the clinical value of radiomics models.
Retrospective data were collected from 307 patients who underwent both quantitative computed tomography (QCT) and full-abdomen DEsCT scans at our institution. Based on QCT measurements, patients were divided into three categories: normal bone mineral density (BMD), osteopenia, and osteoporosis. Using the abdominal DEsCT data, six types of MD images were reconstructed, including HAP (Water), HAP (Fat), Ca (Water), Ca (Fat), Fat (Ca), and Fat (HAP). Patients were randomly divided into a training cohort ( = 214) and a validation cohort (n = 93) at a ratio of 7:3. Focusing on the L1 to L3 vertebrae, density values from the six MD images were measured. Six density value models and six radiomics models were constructed using a random forest (RF) classifier. The performance of these models in assessing bone status was evaluated using the receiver operating characteristic (ROC) curves, and the DeLong test was employed to compare performance differences between the models.
The macro-area under the curve (AUC) values for the density value models based on HAP (Water), HAP (Fat), Ca (Water), and Ca (Fat) MD images were 0.870, 0.870, 0.847, and 0.765, respectively, which outperformed those of Fat (Ca) (AUC = 0.623) and Fat (HAP) (AUC = 0.618) density value models. In the comparison of radiomics models, the trends of model performance were consistent with the density value models across the six MD images. However, the models based on HAP (Water), Ca (Water), HAP (Fat), Ca (Fat), Fat (Ca), and Fat (HAP) images exhibited superior performance than those of the density value models with the corresponding MD images, with values of 0.946, 0.941, 0.934, 0.926, 0.831, and 0.824, respectively.
Bone status assessment can be accurately conducted using density values from HAP (Water), HAP (Fat), Ca (Water), and Ca (Fat) MD images. However, radiomics models derived from MD images surpass traditional density measurement methods in evaluating bone status, highlighting their superior diagnostic potential.
双能谱CT（DEsCT）采用物质分解（MD）技术，为骨状态的机会性评估开辟了新途径。放射组学是阐明骨结构和纹理特征的有力工具，有助于检测矿物质流失。因此，本研究旨在比较使用骨密度测量和从MD图像衍生的放射组学模型评估骨状态的疗效，并进一步探索放射组学模型的临床价值。
回顾性收集了307例在本机构接受定量计算机断层扫描（QCT）和全腹DEsCT扫描的患者的数据。根据QCT测量结果，患者分为三类：正常骨密度（BMD）、骨质减少和骨质疏松。利用腹部DEsCT数据，重建了六种类型的MD图像，包括HAP（水）、HAP（脂肪）、钙（水）、钙（脂肪）、脂肪（钙）和脂肪（HAP）。患者以7:3的比例随机分为训练队列（=214）和验证队列（n = 93）。聚焦于L1至L3椎体，测量六种MD图像的密度值。使用随机森林（RF）分类器构建了六个密度值模型和六个放射组学模型。使用受试者操作特征（ROC）曲线评估这些模型在评估骨状态方面的性能，并采用DeLong检验比较模型之间的性能差异。
基于HAP（水）、HAP（脂肪）、钙（水）和钙（脂肪）MD图像的密度值模型的曲线下宏观面积（AUC）值分别为0.870、0.870、0.847和0.765，优于脂肪（钙）（AUC = 0.623）和脂肪（HAP）（AUC = 0.618）密度值模型。在放射组学模型的比较中，六种MD图像上模型性能的趋势与密度值模型一致。然而，基于HAP（水）、钙（水）、HAP（脂肪）、钙（脂肪）、脂肪（钙）和脂肪（HAP）图像的模型表现优于相应MD图像的密度值模型，其值分别为0.946、0.941、0.934、0.926、0.831和0.824。
使用HAP（水）、HAP（脂肪）、钙（水）和钙（脂肪）MD图像的密度值可以准确地进行骨状态评估。然而，从MD图像衍生的放射组学模型在评估骨状态方面优于传统的密度测量方法，突出了它们卓越的诊断潜力。
