{"id":15160,"date":"2024-01-22T09:44:46","date_gmt":"2024-01-22T08:44:46","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=15160"},"modified":"2024-06-12T09:09:14","modified_gmt":"2024-06-12T07:09:14","slug":"brain-tumor-classification-and-image-translation","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/brain-tumor-classification-and-image-translation\/","title":{"rendered":"Brain tumor classification and image\u200b translation"},"content":{"rendered":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">Gliomas, aggressive, highly heterogeneous brain tumors, require precise grading for effective treatment. The role of myeloid cells in the tumor microenvironment (TME) is crucial for glioma progression and patient prognosis, emphasizing the need for advanced diagnostic tools.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">Current glioma grading relies on manual histological evaluations, which are time-consuming and subjective, often missing intricate TME characteristics. Existing methodologies using deep learning models, such as convolutional neural networks and vision transformers, face challenges in multiclass grading due to the need for large annotated datasets and their limited ability to capture complex TME.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">Our study in collaboration with Nencki Institute of Experimental Biology proposes using single-cell analysis with unsupervised deep learning to analyze glioma tissue microarrays stained for Human Leukocyte Antigens, focusing on myeloid cell accumulation. This method differentiates glioma grades by identifying unique TME phenotypic neighborhoods. Despite a small dataset and the challenge of distinguishing between WHO grades 2 and 3, our analysis successfully identified distinct phenotypic neighborhoods, particularly N2 and N4, which are significant in differentiating malignant gliomas.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">This approach demonstrates the potential of deep learning in accurately classifying gliomas and highlights the importance of myeloid cells in tumor progression. Our findings suggest that automatic grading based on phenotypic neighborhoods could significantly enhance intraoperative assessments and immunotherapy planning.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">Our next step of the scientific investigation includes elucidating the mechanisms of immune evasion and resistance to immunotherapy in gliomas using computational methods with spatial transcriptomic data.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">Additionally, in our computer vision studies for brain diagnostics, we propose a multimodal translation technique to generate brain histology from MRI, potentially avoiding invasive biopsy procedures. So far we tested generating synthetic histology of corpus callosum from MRI images.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\"><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01008-x\">link.springer.co<\/a><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01008-x\" target=\"_blank\" rel=\"noreferrer noopener\">m<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Gliomas, aggressive, highly heterogeneous brain tumors, require precise grading for effective treatment. The role of myeloid cells in the tumor microenvironment (TME) is crucial for glioma progression and patient prognosis, emphasizing the need for advanced diagnostic tools. We propose automatic multiclass histology classification with quantification and learning of TME features.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[7],"research_team":[15],"class_list":["post-15160","research","type-research","status-publish","hentry","research_type-research-topics","research_team-computational-neuroscience"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Brain tumor classification and image\u200b translation - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/research\/brain-tumor-classification-and-image-translation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Brain tumor classification and image\u200b translation\" \/>\n<meta property=\"og:description\" content=\"Gliomas, aggressive, highly heterogeneous brain tumors, require precise grading for effective treatment. The role of myeloid cells in the tumor microenvironment (TME) is crucial for glioma progression and patient prognosis, emphasizing the need for advanced diagnostic tools. We propose automatic multiclass histology classification with quantification and learning of TME features.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/brain-tumor-classification-and-image-translation\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2024-06-12T07:09:14+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/brain-tumor-classification-and-image-translation\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/brain-tumor-classification-and-image-translation\\\/\",\"name\":\"Brain tumor classification and image\u200b translation - Centre for Computational Personalized Medicine\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#website\"},\"datePublished\":\"2024-01-22T08:44:46+00:00\",\"dateModified\":\"2024-06-12T07:09:14+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/brain-tumor-classification-and-image-translation\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/sano.science\\\/research\\\/brain-tumor-classification-and-image-translation\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/brain-tumor-classification-and-image-translation\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/sano.science\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Research\",\"item\":\"https:\\\/\\\/sano.science\\\/research\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Research Topics\",\"item\":\"https:\\\/\\\/sano.science\\\/research-type\\\/research-topics\\\/\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"Brain tumor classification and image\u200b translation\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/sano.science\\\/#website\",\"url\":\"https:\\\/\\\/sano.science\\\/\",\"name\":\"Centre for Computational Personalized Medicine\",\"description\":\"Sano \u2013 Centre for Computational Medicine\",\"publisher\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/sano.science\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/sano.science\\\/#organization\",\"name\":\"Sano \u2013 Centre for Computational Medicine\",\"alternateName\":\"Sano\",\"url\":\"https:\\\/\\\/sano.science\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/sano.science\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/05\\\/logo_sano_podstawowe.png\",\"contentUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/05\\\/logo_sano_podstawowe.png\",\"width\":700,\"height\":265,\"caption\":\"Sano \u2013 Centre for Computational Medicine\"},\"image\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/sano.science\\\/\",\"https:\\\/\\\/x.com\\\/sanoscience\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/sanoscience\\\/\",\"https:\\\/\\\/www.youtube.com\\\/channel\\\/UCDZ_8TcjMWUG2ZcgKKgfpwQ\",\"https:\\\/\\\/bsky.app\\\/profile\\\/sanoscience.bsky.social\"]}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Brain tumor classification and image\u200b translation - Centre for Computational Personalized Medicine","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sano.science\/research\/brain-tumor-classification-and-image-translation\/","og_locale":"en_US","og_type":"article","og_title":"Brain tumor classification and image\u200b translation","og_description":"Gliomas, aggressive, highly heterogeneous brain tumors, require precise grading for effective treatment. The role of myeloid cells in the tumor microenvironment (TME) is crucial for glioma progression and patient prognosis, emphasizing the need for advanced diagnostic tools. 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The role of myeloid cells in the tumor microenvironment (TME) is crucial for glioma progression and patient prognosis, emphasizing the need for advanced diagnostic tools.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Gliomas, aggressive, highly heterogeneous brain tumors, require precise grading for effective treatment. The role of myeloid cells in the tumor microenvironment (TME) is crucial for glioma progression and patient prognosis, emphasizing the need for advanced diagnostic tools.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-z8hyxm","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-QkJ8XN","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Current glioma grading relies on manual histological evaluations, which are time-consuming and subjective, often missing intricate TME characteristics. Existing methodologies using deep learning models, such as convolutional neural networks and vision transformers, face challenges in multiclass grading due to the need for large annotated datasets and their limited ability to capture complex TME.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Current glioma grading relies on manual histological evaluations, which are time-consuming and subjective, often missing intricate TME characteristics. Existing methodologies using deep learning models, such as convolutional neural networks and vision transformers, face challenges in multiclass grading due to the need for large annotated datasets and their limited ability to capture complex TME.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-z8hyxm","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-8Ey8YN","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Our study in collaboration with Nencki Institute of Experimental Biology proposes using single-cell analysis with unsupervised deep learning to analyze glioma tissue microarrays stained for Human Leukocyte Antigens, focusing on myeloid cell accumulation. This method differentiates glioma grades by identifying unique TME phenotypic neighborhoods. Despite a small dataset and the challenge of distinguishing between WHO grades 2 and 3, our analysis successfully identified distinct phenotypic neighborhoods, particularly N2 and N4, which are significant in differentiating malignant gliomas.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Our study in collaboration with Nencki Institute of Experimental Biology proposes using single-cell analysis with unsupervised deep learning to analyze glioma tissue microarrays stained for Human Leukocyte Antigens, focusing on myeloid cell accumulation. This method differentiates glioma grades by identifying unique TME phenotypic neighborhoods. Despite a small dataset and the challenge of distinguishing between WHO grades 2 and 3, our analysis successfully identified distinct phenotypic neighborhoods, particularly N2 and N4, which are significant in differentiating malignant gliomas.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-z8hyxm","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-v1vCHk","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">This approach demonstrates the potential of deep learning in accurately classifying gliomas and highlights the importance of myeloid cells in tumor progression. Our findings suggest that automatic grading based on phenotypic neighborhoods could significantly enhance intraoperative assessments and immunotherapy planning.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">This approach demonstrates the potential of deep learning in accurately classifying gliomas and highlights the importance of myeloid cells in tumor progression. Our findings suggest that automatic grading based on phenotypic neighborhoods could significantly enhance intraoperative assessments and immunotherapy planning.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-z8hyxm","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-WbRNfw","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Our next step of the scientific investigation includes elucidating the mechanisms of immune evasion and resistance to immunotherapy in gliomas using computational methods with spatial transcriptomic data.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Our next step of the scientific investigation includes elucidating the mechanisms of immune evasion and resistance to immunotherapy in gliomas using computational methods with spatial transcriptomic data.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-z8hyxm","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Qficcp","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Additionally, in our computer vision studies for brain diagnostics, we propose a multimodal translation technique to generate brain histology from MRI, potentially avoiding invasive biopsy procedures. So far we tested generating synthetic histology of corpus callosum from MRI images.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Additionally, in our computer vision studies for brain diagnostics, we propose a multimodal translation technique to generate brain histology from MRI, potentially avoiding invasive biopsy procedures. So far we tested generating synthetic histology of corpus callosum from MRI images.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-z8hyxm","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Um72qd","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01008-x\">link.springer.co<\/a><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01008-x\" target=\"_blank\" rel=\"noreferrer noopener\">m<\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01008-x\">link.springer.co<\/a><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01008-x\" target=\"_blank\" rel=\"noreferrer noopener\">m<\/a><\/p>\n"]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/15160","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":5,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/15160\/revisions"}],"predecessor-version":[{"id":17160,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/15160\/revisions\/17160"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=15160"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=15160"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=15160"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}