Annika Plate1, Juliana Maiostre2, Johannes Levin3, Kai Bötzel4, Seyed-Ahmad Ahmadi5. 1. Department of Neurology, Ludwig-Maximilians-University, Marchioninistr. 15, 81667 Munich, Germany. Electronic address: Annika.Plate@med.uni-muenchen.de. 2. Department of Neurology, Ludwig-Maximilians-University, Marchioninistr. 15, 81667 Munich, Germany. 3. Department of Neurology, Ludwig-Maximilians-University, Marchioninistr. 15, 81667 Munich, Germany. Electronic address: Johannes.Levin@med.uni-muenchen.de. 4. Department of Neurology, Ludwig-Maximilians-University, Marchioninistr. 15, 81667 Munich, Germany. Electronic address: Boetzel@med.uni-muenchen.de. 5. Department of Neurology, Ludwig-Maximilians-University, Marchioninistr. 15, 81667 Munich, Germany; German Center for Vertigo and Balance Disorders, Ludwig-Maximilians-University, Munich, Germany. Electronic address: Ahmad.Ahmadi@med.uni-muenchen.de.
Abstract
INTRODUCTION: TCS is a well-established technique for diagnosis of Parkinson's disease (PD). Volumetric 3D-TCS is a promising complementary approach for objective acquisition and analysis, in particular for less experienced sonographers. This study provides baselines for Parkinson detection (sensitivity and specificity), cutoff values and inter-rater agreement in 3D-TCS. METHODS: We performed 3D-TCS in 52 subjects (healthy controls and PD) bilaterally, and reconstructed in 3D space uni-laterally. Ipsi-lateral hyperechogenicities in the substantia nigra are manually segmented slice-by-slice in the 3D volume by two raters at different experience levels. ROC threshold analysis is performed and compared on features representing 3D volume and axial cross-sections (2.5D) of hyperechogenicities. Pearson correlation and intra-class correlation coefficients were evaluated for assessment of inter-rater agreement. RESULTS: 50 subjects were included. Both raters achieved high classification accuracy with 2.5D/3D features extracted from 3D-TCS volumes (best results sensitivity/specificity/cut-off per rater: 84.6%/88.9%/25.0mm2; 77.8%/88.9%/95.9mm3). The inter-rater agreement in 3D was high (ICC(A,1) = 0.777, p < 10-3), the classification performance of both sonographers was statistically not significantly different. CONCLUSION: The study presents first baseline values for uni-lateral 3D-TCS examination, and finds no disadvantage of uni-lateral reconstructions compared to previous bi-lateral fusion. Volumetric 3D-TCS has potential for a high inter-rater agreement and accuracy in detection of PD, in particular for sonographers with less experience.
INTRODUCTION:TCS is a well-established technique for diagnosis of Parkinson's disease (PD). Volumetric 3D-TCS is a promising complementary approach for objective acquisition and analysis, in particular for less experienced sonographers. This study provides baselines for Parkinson detection (sensitivity and specificity), cutoff values and inter-rater agreement in 3D-TCS. METHODS: We performed 3D-TCS in 52 subjects (healthy controls and PD) bilaterally, and reconstructed in 3D space uni-laterally. Ipsi-lateral hyperechogenicities in the substantia nigra are manually segmented slice-by-slice in the 3D volume by two raters at different experience levels. ROC threshold analysis is performed and compared on features representing 3D volume and axial cross-sections (2.5D) of hyperechogenicities. Pearson correlation and intra-class correlation coefficients were evaluated for assessment of inter-rater agreement. RESULTS: 50 subjects were included. Both raters achieved high classification accuracy with 2.5D/3D features extracted from 3D-TCS volumes (best results sensitivity/specificity/cut-off per rater: 84.6%/88.9%/25.0mm2; 77.8%/88.9%/95.9mm3). The inter-rater agreement in 3D was high (ICC(A,1) = 0.777, p < 10-3), the classification performance of both sonographers was statistically not significantly different. CONCLUSION: The study presents first baseline values for uni-lateral 3D-TCS examination, and finds no disadvantage of uni-lateral reconstructions compared to previous bi-lateral fusion. Volumetric 3D-TCS has potential for a high inter-rater agreement and accuracy in detection of PD, in particular for sonographers with less experience.