Abstract: Combination treatment with Prussian blue nanoparticles and at least one immunotherapeutic treatment. Stable, functionalized Prussian blue nanoparticles, including those with enhanced stability under alkaline conditions, and methods of cancer, neoplasm, and tumor treatment using them, including photothermal treatment and combination immunotherapeutic treatments.

Abstract: The present invention, referred to as Oasis is Automated Statistical Inference for Segmentation (OASIS), is a fully automated and robust statistical method for cross-sectional MS lesion segmentation. Using intensity information from multiple modalities of MRI, a logistic regression model assigns voxel-level probabilities of lesion presence. The OASIS model produces interpretable results in the form of regression coefficients that can be applied to imaging studies quickly and easily. OASIS uses intensity-normalized brain MRI volumes, enabling the model to be robust to changes in scanner and acquisition sequence. OASIS also adjusts for intensity inhomogeneities that preprocessing bias field correction procedures do not remove, using BLUR volumes. This allows for more accurate segmentation of brain areas that are highly distorted by inhomogeneities, such as the cerebellum.

Abstract: A method of automatically detecting tissue abnormalities in images of a region of interest of a subject includes obtaining first image data for the region of interest of the subject, normalizing the first image data based on statistical parameters derived from at least a portion of the first image data to provide first normalized image data, obtaining second image data for the region of interest of the subject, normalizing the second image data based on statistical parameters derived from at least a portion of the second image data to provide second normalized image data, processing the first and second normalized image data to provide resultant image data, and generating a probability map for the region of interest based on the resultant image data and a predefined statistical model. The probability map indicates the probability of at least a portion of an abnormality being present at locations within the region of interest.

Abstract: A method of automatically detecting tissue abnormalities in images of a region of interest of a subject includes obtaining first image data for the region of interest of the subject, normalizing the first image data based on statistical parameters derived from at least a portion of the first image data to provide first normalized image data, obtaining second image data for the region of interest of the subject, normalizing the second image data based on statistical parameters derived from at least a portion of the second image data to provide second normalized image data, processing the first and second normalized image data to provide resultant image data, and generating a probability map for the region of interest based on the resultant image data and a predefined statistical model. The probability map indicates the probability of at least a portion of an abnormality being present at locations within the region of interest.

Abstract: The present invention, referred to as Oasis is Automated Statistical Inference for Segmentation (OASIS), is a fully automated and robust statistical method for cross-sectional MS lesion segmentation. Using intensity information from multiple modalities of MRI, a logistic regression model assigns voxel-level probabilities of lesion presence. The OASIS model produces interpretable results in the form of regression coefficients that can be applied to imaging studies quickly and easily. OASIS uses intensity-normalized brain MRI volumes, enabling the model to be robust to changes in scanner and acquisition sequence. OASIS also adjusts for intensity inhomogeneities that preprocessing bias field correction procedures do not remove, using BLUR volumes. This allows for more accurate segmentation of brain areas that are highly distorted by inhomogeneities, such as the cerebellum.