Shiiba Takuro, Takano Kazuki, Takaki Akihiro, Suwazono Shugo
Department of Molecular Imaging, School of Medical Sciences, Fujita Health University, 1-98, Dengakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University, 6-22 Misakimachi, Omuta-shi, Fukuoka, 836-8505, Japan.
EJNMMI Res. 2022 Jun 27;12(1):39. doi: 10.1186/s13550-022-00910-1.
We hypothesised that the radiomics signature, which includes texture information of dopamine transporter single-photon emission computed tomography (DAT-SPECT) images for Parkinson's disease (PD), may assist semi-quantitative indices. Herein, we constructed a radiomics signature using DAT-SPECT-derived radiomics features that effectively discriminated PD from healthy individuals and evaluated its classification performance.
We analysed 413 cases of both normal control (NC, n = 101) and PD (n = 312) groups from the Parkinson's Progression Markers Initiative database. Data were divided into the training and two test datasets with different SPECT manufacturers. DAT-SPECT images were spatially normalised to the Montreal Neurologic Institute space. We calculated 930 radiomics features, including intensity- and texture-based features in the caudate, putamen, and pallidum volumes of interest. The striatum uptake ratios (SURs) of the caudate, putamen, and pallidum were also calculated as conventional semi-quantification indices. The least absolute shrinkage and selection operator was used for feature selection and construction of the radiomics signature. The four classification models were constructed using a radiomics signature and/or semi-quantitative indicator. Furthermore, we compared the classification performance of the semi-quantitative indicator alone and the combination with the radiomics signature for the classification models. The receiver operating characteristics (ROC) analysis was used to evaluate the classification performance. The classification performance of SUR was higher than that of other semi-quantitative indicators. The radiomics signature resulted in a slightly increased area under the ROC curve (AUC) compared to SUR in each test dataset. When combined with SUR and radiomics signature, all classification models showed slightly higher AUCs than that of SUR alone.
We constructed a DAT-SPECT image-derived radiomics signature. Performance analysis showed that the current radiomics signature would be helpful for the diagnosis of PD and has the potential to provide robust diagnostic performance.
我们假设，包含帕金森病（PD）多巴胺转运体单光子发射计算机断层扫描（DAT-SPECT）图像纹理信息的放射组学特征，可能有助于半定量指标。在此，我们使用源自DAT-SPECT的放射组学特征构建了一个放射组学特征，该特征可有效区分PD患者与健康个体，并评估其分类性能。
我们分析了来自帕金森病进展标志物倡议数据库的413例正常对照（NC，n = 101）和PD（n = 312）组病例。数据被分为训练集和两个不同SPECT制造商的测试数据集。DAT-SPECT图像在空间上被归一化到蒙特利尔神经病学研究所空间。我们计算了930个放射组学特征，包括尾状核、壳核和苍白球感兴趣体积中基于强度和纹理的特征。还计算了尾状核、壳核和苍白球的纹状体摄取率（SURs）作为传统的半定量指标。使用最小绝对收缩和选择算子进行特征选择和放射组学特征构建。使用放射组学特征和/或半定量指标构建了四种分类模型。此外，我们比较了单独的半定量指标以及与放射组学特征组合用于分类模型时的分类性能。使用受试者工作特征（ROC）分析来评估分类性能。SUR的分类性能高于其他半定量指标。在每个测试数据集中，与SUR相比，放射组学特征导致ROC曲线下面积（AUC）略有增加。当与SUR和放射组学特征结合时，所有分类模型的AUC均略高于单独使用SUR时。
我们构建了一个源自DAT-SPECT图像的放射组学特征。性能分析表明，当前的放射组学特征有助于PD的诊断，并有可能提供强大的诊断性能。
