[{"innerBlocks":[],"name":"core\/freeform","postId":207,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"Researchers at UT Southwestern have developed an artificial intelligence algorithm that is able to classify lung cancer cell types from digital pathology images.\u201cIt is time-consuming and difficult for pathologists to locate very small tumor regions in tissue images, so this could greatly reduce the time that pathologists need to spend on each image,\u201d says Guanghua Xiao, professor of population and data sciences at UT Southwestern."As there are usually millions of cells in a tissue sample, a pathologist can only analyze so many slides in a day,\u201d adds Xiao. \u201cTo make a diagnosis, pathologists usually only examine several \u2018representative\u2019 regions in detail, rather than the whole slide. However, some important details could be missed by this approach.\u201d The AI-based software tool\u2014called ConvPath\u2014automatically identifies each cell in the pathology image as tumor cells, stromal cells (connective tissue cells) and lymphocytes (white blood cells), then converts the image into a spatial map while clusters of tumor cells are further identified as tumor regions.

According to a study<\/a> published in the journal EBioMedicine<\/i>, ConvPath\u2019s overall classification accuracy was 92.9 percent and 90.1 percent in training and independent testing datasets, respectively.As a result, the study\u2019s authors conclude that ConvPath could potentially assist pathologists in clinical practice in a number of ways, including helping them to quickly pinpoint tumor cells. \u201cThis tool could help pathologists and clinicians to predict the patient prognosis, and therefore to tailor the treatment plan of individual patients using readily available tissue images,\u201d contend the authors.In addition, they make the case that ConvPath \u201ccould be used to quantify cell-cell interactions and distributions of different types of cells, especially the spatial distribution of lymphocytes and their interaction with the tumor region, which could potentially provide information for patient response to immunotherapy.\u201dThe software tool for lung adenocarcinoma digital pathological image analysis, which incorporates deep learning, has been made publicly accessible<\/a> by UT Southwestern to \u201cfacilitate users in leveraging this pipeline for their research.\u201d","saveContent":"Researchers at UT Southwestern have developed an artificial intelligence algorithm that is able to classify lung cancer cell types from digital pathology images.\u201cIt is time-consuming and difficult for pathologists to locate very small tumor regions in tissue images, so this could greatly reduce the time that pathologists need to spend on each image,\u201d says Guanghua Xiao, professor of population and data sciences at UT Southwestern."As there are usually millions of cells in a tissue sample, a pathologist can only analyze so many slides in a day,\u201d adds Xiao. \u201cTo make a diagnosis, pathologists usually only examine several \u2018representative\u2019 regions in detail, rather than the whole slide. However, some important details could be missed by this approach.\u201d The AI-based software tool\u2014called ConvPath\u2014automatically identifies each cell in the pathology image as tumor cells, stromal cells (connective tissue cells) and lymphocytes (white blood cells), then converts the image into a spatial map while clusters of tumor cells are further identified as tumor regions.According to a study<\/a> published in the journal EBioMedicine<\/i>, ConvPath\u2019s overall classification accuracy was 92.9 percent and 90.1 percent in training and independent testing datasets, respectively.As a result, the study\u2019s authors conclude that ConvPath could potentially assist pathologists in clinical practice in a number of ways, including helping them to quickly pinpoint tumor cells. \u201cThis tool could help pathologists and clinicians to predict the patient prognosis, and therefore to tailor the treatment plan of individual patients using readily available tissue images,\u201d contend the authors.In addition, they make the case that ConvPath \u201ccould be used to quantify cell-cell interactions and distributions of different types of cells, especially the spatial distribution of lymphocytes and their interaction with the tumor region, which could potentially provide information for patient response to immunotherapy.\u201dThe software tool for lung adenocarcinoma digital pathological image analysis, which incorporates deep learning, has been made publicly accessible<\/a> by UT Southwestern to \u201cfacilitate users in leveraging this pipeline for their research.\u201d","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

AI-based software tool could help pathologists identify cancer cells

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