Dataset Information

A benchmarking tool to evaluate computer tomography perfusion infarct core predictions against a DWI standard.

ABSTRACT: Differences in research methodology have hampered the optimization of Computer Tomography Perfusion (CTP) for identification of the ischemic core. We aim to optimize CTP core identification using a novel benchmarking tool. The benchmarking tool consists of an imaging library and a statistical analysis algorithm to evaluate the performance of CTP. The tool was used to optimize and evaluate an in-house developed CTP-software algorithm. Imaging data of 103 acute stroke patients were included in the benchmarking tool. Median time from stroke onset to CT was 185?min (IQR 180-238), and the median time between completion of CT and start of MRI was 36?min (IQR 25-79). Volumetric accuracy of the CTP-ROIs was optimal at an rCBF threshold of <38%; at this threshold, the mean difference was 0.3?ml (SD 19.8?ml), the mean absolute difference was 14.3 (SD 13.7) ml, and CTP was 67% sensitive and 87% specific for identification of DWI positive tissue voxels. The benchmarking tool can play an important role in optimizing CTP software as it provides investigators with a novel method to directly compare the performance of alternative CTP software packages.

SUBMITTER: Cereda CW

PROVIDER: S-EPMC5076783 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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A benchmarking tool to evaluate computer tomography perfusion infarct core predictions against a DWI standard.

Cereda Carlo W CW Christensen Søren S Campbell Bruce C V BCV Mishra Nishant K NK Mlynash Michael M Levi Christopher C Straka Matus M Wintermark Max M Bammer Roland R Albers Gregory W GW Parsons Mark W MW Lansberg Maarten G MG

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 20151019 10

Differences in research methodology have hampered the optimization of Computer Tomography Perfusion (CTP) for identification of the ischemic core. We aim to optimize CTP core identification using a novel benchmarking tool. The benchmarking tool consists of an imaging library and a statistical analysis algorithm to evaluate the performance of CTP. The tool was used to optimize and evaluate an in-house developed CTP-software algorithm. Imaging data of 103 acute stroke patients were included in the ...[more]

PMID: 26661203

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Project description:Background and purposeThe ISLES challenge (Ischemic Stroke Lesion Segmentation) enables globally diverse teams to compete to develop advanced tools for stroke lesion analysis with machine learning. Detection of irreversibly damaged tissue on computed tomography perfusion (CTP) is often necessary to determine eligibility for late-time-window thrombectomy. Therefore, the aim of ISLES-2018 was to segment infarcted tissue on CTP based on diffusion-weighted imaging as a reference standard.MethodsThe data, from 4 centers, consisted of 103 cases of acute anterior circulation large artery occlusion stroke who underwent diffusion-weighted imaging rapidly after CTP. Diffusion-weighted imaging lesion segmentation was performed manually and acted as a reference standard. The data were separated into 63 cases for training and 40 for testing, upon which quality metrics (dice score coefficient, Hausdorff distance, absolute lesion volume difference, etc) were computed to rank methods based on their overall performance.ResultsTwenty-four different teams participated in the challenge. Median time to CTP was 185 minutes (interquartile range, 180-238), the time between CTP and magnetic resonance imaging was 36 minutes (interquartile range, 25-79), and the median infarct lesion size was 15.2 mL (interquartile range, 5.7-45). The best performance for Dice score coefficient and absolute volume difference were 0.51 and 10.1 mL, respectively, from different teams. Based on the ranking criteria, the top team's algorithm demonstrated for average Dice score coefficient and average absolute volume difference 0.51 and 10.2 mL, respectively, outperforming the conventional threshold-based method (dice score coefficient, 0.3; volume difference, 15.3). Diverse algorithms were used, almost all based on deep learning, with top-ranked approaches making use of the raw perfusion data as well as methods to synthetically generate complementary information to boost prediction performance.ConclusionsMachine learning methods may predict infarcted tissue from CTP with improved accuracy compared with threshold-based methods used in clinical routine. This dataset will remain public and can be used to test improvement in algorithms over time.

Project description:Background and Purpose- The aim of this study is to determine the spatial and volumetric accuracy of infarct core estimates from relative cerebral blood flow (rCBF) by comparison with near-contemporaneous diffusion-weighted imaging (DWI), and evaluate whether it is sufficient for patient triage to reperfusion therapies. Methods- One hundred ninety-three patients enrolled in the DEFUSE 2 (Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution) and SENSE 3 (Sensitivity Encoding) stroke studies were screened, and 119 who underwent acute magnetic resonance imaging with DWI and perfusion imaging within 24 hours of onset were included. Infarct core was estimated using reduced rCBF at 12 thresholds (<0.20-<0.44) and compared against DWI (apparent diffusion coefficient <620 10-6mm2/s). For each threshold, volumetric agreement between the rCBF and DWI core estimates was assessed using Bland-Altman, correlation, and linear regression analyses; spatial agreement was assessed using receiver operating characteristic analysis. Results- An rCBF threshold of 0.32 yielded the smallest mean absolute volume difference (14.7 mL), best linear regression fit (R2=0.84), and best spatial agreement (Youden index, 0.38; 95% CI, 0.34-0.41) between rCBF and DWI, with high correlation (r=0.91, P<0.05), a small mean volume difference (1.3 mL) and no fixed bias (P<0.05). At this threshold, 110 of 119 (92.4%) patients were correctly triaged when applying 70 mL as the volume limit for thrombectomy. Spatial agreement was better for prediction of large infarcts (>70 mL) than small infarcts (≤70 mL), with Youden indices of 0.53 (95% CI, 0.49-0.56) and 0.34 (95% CI, 0.30-0.37), respectively. Conclusions- Strong correlation and agreement with near-contemporaneous DWI indicate that infarct core estimates obtained using rCBF are sufficiently accurate for patient triage to reperfusion therapies. The identified optimal rCBF threshold of 0.32 closely approximates the threshold currently used in clinical practice.

Dataset Information

A benchmarking tool to evaluate computer tomography perfusion infarct core predictions against a DWI standard.

Publications

A benchmarking tool to evaluate computer tomography perfusion infarct core predictions against a DWI standard.

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