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

Abstract

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 SURputamen 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 SURputamen in each test dataset. When combined with SURputamen and radiomics signature, all classification models showed slightly higher AUCs than that of SURputamen 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.

The differential diagnosis value of radiomics-based machine learning in Parkinson’s disease: a systematic review and meta-analysis

“…Following the exclusion of 121 duplicate studies, 43 studies were screened based on their titles or abstracts. Ultimately, a total of 28 articles ( Cheng et al, 2019 ; Shinde et al, 2019 ; Wu et al, 2019 ; Xiao et al, 2019 ; Cao et al, 2020 , 2021 ; Liu et al, 2020 ; Pang et al, 2020 , 2022 ; Shu et al, 2020 ; Dhinagar et al, 2021 ; Hu et al, 2021 ; Li et al, 2021 , 2022 ; Ren et al, 2021 ; Shi et al, 2021 , 2022a , 2022b ; Sun et al, 2021 , 2022 ; Tupe-Waghmare et al, 2021 ; Zhang et al, 2021 ; Ben Bashat et al, 2022 ; Guan et al, 2022 ; Kang et al, 2022 ; Kim et al, 2022 ; Shiiba et al, 2022 ; Zhao et al, 2022 ) were deemed eligible and included in this meta-analysis.…”

“…The characteristics of the studies included in this research are shown in Table 1 and Supplementary Table 4 . The original 28 studies were published between 2019 and 2022, with 27 of them from Asia ( Cheng et al, 2019 ; Shinde et al, 2019 ; Wu et al, 2019 ; Xiao et al, 2019 ; Cao et al, 2020 , 2021 ; Liu et al, 2020 ; Pang et al, 2020 , 2022 ; Shu et al, 2020 ; Hu et al, 2021 ; Li et al, 2021 , 2022 ; Ren et al, 2021 ; Shi et al, 2021 , 2022a , 2022b ; Sun et al, 2021 , 2022 ; Tupe-Waghmare et al, 2021 ; Zhang et al, 2021 ; Ben Bashat et al, 2022 ; Guan et al, 2022 ; Kang et al, 2022 ; Kim et al, 2022 ; Shiiba et al, 2022 ; Zhao et al, 2022 ) and one from North America ( Dhinagar et al, 2021 ). The study comprised a total of 6,057 participants, with 3,422 patients diagnosed with PD, 1,983 healthy controls, and 652 cases of APS (476 with MSA and 176 with PSP).…”

Neuro-Vulnerability in Energy Metabolism Regulation: A Comprehensive Narrative Review

“…For this reason, some researchers have developed algorithms to solve these problems. In this line, Shiiba et al [ 231 ] developed a DAT-SPECT image-derived radiomics signature and reported that their model would be useful for the diagnosis of PD and would have the potential to provide robust diagnostics. In addition, SPECT has been used in the diagnosis of AD due to its potential to provide information on regional cerebral blood flow, which has been postulated as a determinant of this disease.…”

Radiomics insight into the neurodegenerative “hot” brain: A narrative review from the nuclear medicine perspective

“…Notably, no model was built here for subtyping the dopaminergic denervation into PD/ atypical-parkinsonisms. Shiiba et al compared DAT-SPECT conventional features and a radiomics signature for the classification of 413 PPMI patients into PD or HC (31). Radiomics signature included features mostly from putamen and pallidum and was up to 96.8% accurate in the discrimination of PD patients.…”