Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments

Abstract

Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected Parkinsonian syndromes. We performed a cross-sectional study to investigate whether assessment of texture in DAT SPECT radiotracer uptake enables enhanced correlations with severity of motor and cognitive symptoms in Parkinson's disease (PD), with the long-term goal of enabling clinical utility of DAT SPECT imaging, beyond standard diagnostic tasks, to tracking of progression in PD. Quantitative analysis in routine DAT SPECT imaging, if performed at all, has been restricted to assessment of mean regional uptake. We applied a framework wherein textural features were extracted from the images. Notably, the framework did not require registration to a common template, and worked in the subject-native space. Image analysis included registration of SPECT images onto corresponding MRI images, automatic region-of-interest (ROI) extraction on the MRI images, followed by computation of Haralick texture features. We analyzed 141 subjects from the Parkinson's Progressive Marker Initiative (PPMI) database, including 85 PD and 56 healthy controls (HC) (baseline scans with accompanying 3 T MRI images). We performed univariate and multivariate regression analyses between the quantitative metrics and different clinical measures, namely (i) the UPDRS (part III - motor) score, disease duration as measured from (ii) time of diagnosis (DD-diag.) and (iii) time of appearance of symptoms (DD-sympt.), as well as (iv) the Montreal Cognitive Assessment (MoCA) score. For conventional mean uptake analysis in the putamen, we showed significant correlations with clinical measures only when both HC and PD were included (Pearson correlation r = − 0.74, p-value < 0.001). However, this was not significant when applied to PD subjects only (r = − 0.19, p-value = 0.084), and no such correlations were observed in the caudate. By contrast, for the PD subjects, significant correlations were observed in the caudate when including texture metrics, with (i) UPDRS (p-values < 0.01), (ii) DD-diag. (p-values < 0.001), (iii) DD-sympt (p-values < 0.05), and (iv) MoCA (p-values < 0.01), while no correlations were observed for conventional analysis (p-values = 0.94, 0.34, 0.88 and 0.96, respectively). Our results demonstrated the ability to capture valuable information using advanced texture metrics from striatal DAT SPECT, enabling significant correlations of striatal DAT binding with clinical, motor and cognitive outcomes, and suggesting that textural features hold potential as biomarkers of PD severity and progression.

Robustness of Radiomic Features in [11C]Choline and [18F]FDG PET/CT Imaging of Nasopharyngeal Carcinoma: Impact of Segmentation and Discretization

“…For instance, Homogeneity_GLCM (Homogeneity2 in the present study) also presented small variability between different segmentation methods [23], was very robust with respect to delineation and PVC [24], and depicted good reproducibility on test-retest scans [19]. This also can explain the enhanced performance of such metrics in correlation with clinical measures [46]. We should also note that a given same name of a texture feature in different publications does not always correspond to the same definition [47].…”

Machine learning methods for optimal prediction of motor outcome in Parkinson’s disease

“…Importantly, we saw that not using imaging information (features selected by NSGAII) can lower performance to around 4.48 (in prediction error) which was not statistically significantly than our 4.32 performance (see Supplementary materials, section III, Supplemental Table 1&2). In our other investigations we also found that conventional imaging measures do not correlate well with clinical measures (correlation coefficient = −0.008, p-value = 0.94) [29] nor improve prediction (9 ± 0.88) [20]. However, radiomics analysis of DAT SPECT images, going beyond conventional imaging measures, was seen to provide significant improvements (4.12 ± 0.43, p < 0.001) in both tasks.…”

“…Approaches based on machine learning aim to build classification or prediction algorithms automatically by capturing statistically robust patterns present in the analyzed data [26]. In our past efforts we used radiomic (texture) measures to show improved correlation with clinical measures [29] and, when combined with clinical measures, improved prediction of motor outcome (MDS_UPDRS III) [20]. Furthermore, our recent studies confirmed significant improvement of outcome prediction based on discovering of patterns in images using deep learning (see the Section 4) [30,31].…”

Characterization of PET/CT images using texture analysis: the past, the present… any future?

“…Several dozen studies investigating the clinical value of PET uptake heterogeneity (using TA or other methods) in various tumour types (including oesophageal, lung, rectal, breast, head and neck, and brain cancer, and lymphoma) as well as more recently in neurodegenerative diseases with PET [16, 43] and DAT SPECT [44] have been published in the last 4 years alone. More recently, a few studies have also focused on extracting features from both the PET and CT components (see the section Promising clinical results below).…”