Dopamine transporter single-photon emission computed tomography-derived radiomics signature for detecting Parkinson’s disease

• Published in: EJNMMI research 2022-06, Vol.12 (1), p.39-39, Article 39
• Main Authors: Shiiba, Takuro, Takano, Kazuki, Takaki, Akihiro, Suwazono, Shugo
• Format: Article
• Language: English
• Subjects: Cardiac Imaging; Classification; Computed tomography; Datasets; Dopamine; Dopamine transporter; Imaging; Medical imaging; Medicine; Medicine & Public Health; Nuclear Medicine; Oncology; Original Research; Orthopedics; Parkinson's disease; Performance evaluation; Photon emission; Photons; Radiology; Radiomics; Radiomics signature; SPECT; Texture; Tomography
• ISSN: 2191-219X; 2191-219X
• DOI: 10.1186/s13550-022-00910-1

Background 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. Results 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 putamen 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 putamen in each test dataset. When combined with SUR putamen and radiomics signature, all classification models showed slightly higher AUCs than that of SUR putamen alone. Conclusion 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.