Multiplex Imaging Analysis in Pathology: a Comprehensive Review on Analytical Approaches and Digital Toolkits
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916465769185280 |
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| author | Omar, Mohamed Fanelli, Giuseppe Nicolo Socciarelli, Fabio Ullanat, Varun Puchala, Sreekar Reddy Wen, James Chowdhury, Alex Valencia, Itzel Scatena, Cristian Marchionni, Luigi Umeton, Renato Loda, Massimo |
| author_facet | Omar, Mohamed Fanelli, Giuseppe Nicolo Socciarelli, Fabio Ullanat, Varun Puchala, Sreekar Reddy Wen, James Chowdhury, Alex Valencia, Itzel Scatena, Cristian Marchionni, Luigi Umeton, Renato Loda, Massimo |
| contents | Conventional histopathology has long been essential for disease diagnosis, relying on visual inspection of tissue sections. Immunohistochemistry aids in detecting specific biomarkers but is limited by its single-marker approach, restricting its ability to capture the full tissue environment. The advent of multiplexed imaging technologies, like multiplexed immunofluorescence and spatial transcriptomics, allows for simultaneous visualization of multiple biomarkers in a single section, enhancing morphological data with molecular and spatial information. This provides a more comprehensive view of the tissue microenvironment, cellular interactions, and disease mechanisms - crucial for understanding disease progression, prognosis, and treatment response. However, the extensive data from multiplexed imaging necessitates sophisticated computational methods for preprocessing, segmentation, feature extraction, and spatial analysis. These tools are vital for managing large, multidimensional datasets, converting raw imaging data into actionable insights. By automating labor-intensive tasks and enhancing reproducibility and accuracy, computational tools are pivotal in diagnostics and research. This review explores the current landscape of multiplexed imaging in pathology, detailing workflows and key technologies like PathML, an AI-powered platform that streamlines image analysis, making complex dataset interpretation accessible for clinical and research settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00948 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multiplex Imaging Analysis in Pathology: a Comprehensive Review on Analytical Approaches and Digital Toolkits Omar, Mohamed Fanelli, Giuseppe Nicolo Socciarelli, Fabio Ullanat, Varun Puchala, Sreekar Reddy Wen, James Chowdhury, Alex Valencia, Itzel Scatena, Cristian Marchionni, Luigi Umeton, Renato Loda, Massimo Tissues and Organs Computer Vision and Pattern Recognition Cell Behavior Molecular Networks Quantitative Methods Conventional histopathology has long been essential for disease diagnosis, relying on visual inspection of tissue sections. Immunohistochemistry aids in detecting specific biomarkers but is limited by its single-marker approach, restricting its ability to capture the full tissue environment. The advent of multiplexed imaging technologies, like multiplexed immunofluorescence and spatial transcriptomics, allows for simultaneous visualization of multiple biomarkers in a single section, enhancing morphological data with molecular and spatial information. This provides a more comprehensive view of the tissue microenvironment, cellular interactions, and disease mechanisms - crucial for understanding disease progression, prognosis, and treatment response. However, the extensive data from multiplexed imaging necessitates sophisticated computational methods for preprocessing, segmentation, feature extraction, and spatial analysis. These tools are vital for managing large, multidimensional datasets, converting raw imaging data into actionable insights. By automating labor-intensive tasks and enhancing reproducibility and accuracy, computational tools are pivotal in diagnostics and research. This review explores the current landscape of multiplexed imaging in pathology, detailing workflows and key technologies like PathML, an AI-powered platform that streamlines image analysis, making complex dataset interpretation accessible for clinical and research settings. |
| title | Multiplex Imaging Analysis in Pathology: a Comprehensive Review on Analytical Approaches and Digital Toolkits |
| topic | Tissues and Organs Computer Vision and Pattern Recognition Cell Behavior Molecular Networks Quantitative Methods |
| url | https://arxiv.org/abs/2411.00948 |