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High-throughput automated image analysis of neuroinflammation and neurodegeneration enables quantitative assessment of virus neurovirulence.

ABSTRACT: Historically, the safety of live attenuated vaccine candidates against neurotropic viruses was assessed by semi-quantitative analysis of virus-induced histopathology in the central nervous system of monkeys. We have developed a high-throughput automated image analysis (AIA) for the quantitative assessment of virus-induced neuroinflammation and neurodegeneration. Evaluation of the results generated by AIA showed that quantitative estimates of lymphocytic infiltration, microglial activation, and neurodegeneration strongly and significantly correlated with results of traditional histopathological scoring. In addition, we show that AIA is a targeted, objective, accurate, and time-efficient approach that provides reliable differentiation of virus neurovirulence. As such, it may become a useful tool in establishing consistent analytical standards across research and development laboratories and regulatory agencies, and may improve the safety evaluation of live virus vaccines. The implementation of this high-throughput AIA will markedly advance many fields of research including virology, neuroinflammation, neuroscience, and vaccinology.

SUBMITTER: Maximova OA

PROVIDER: S-EPMC2997939 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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High-throughput automated image analysis of neuroinflammation and neurodegeneration enables quantitative assessment of virus neurovirulence.

Maximova Olga A OA Murphy Brian R BR Pletnev Alexander G AG

Vaccine 20100803 52

Historically, the safety of live attenuated vaccine candidates against neurotropic viruses was assessed by semi-quantitative analysis of virus-induced histopathology in the central nervous system of monkeys. We have developed a high-throughput automated image analysis (AIA) for the quantitative assessment of virus-induced neuroinflammation and neurodegeneration. Evaluation of the results generated by AIA showed that quantitative estimates of lymphocytic infiltration, microglial activation, and n ...[more]

PMID: 20688036

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Project description:Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how these measures relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1840 raw T1-weighted volumes. These images included a training set of 1065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP), as well as FreeSurfer's Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored "unusable" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and out-performed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.

Project description:Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

Dataset Information

High-throughput automated image analysis of neuroinflammation and neurodegeneration enables quantitative assessment of virus neurovirulence.

Publications

High-throughput automated image analysis of neuroinflammation and neurodegeneration enables quantitative assessment of virus neurovirulence.

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