Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview
Abstract The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in harnessing multimodal data in the life sciences. However, most approaches are limited to unimodal data, leaving integrated approaches across modaliti...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-02-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-024-04915-3 |
| _version_ | 1797273496807538688 |
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| author | Xiaobing Feng Wen Shu Mingya Li Junyu Li Junyao Xu Min He |
| author_facet | Xiaobing Feng Wen Shu Mingya Li Junyu Li Junyao Xu Min He |
| author_sort | Xiaobing Feng |
| collection | DOAJ |
| description | Abstract The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in harnessing multimodal data in the life sciences. However, most approaches are limited to unimodal data, leaving integrated approaches across modalities relatively underdeveloped in computational pathology. Pathogenomics, as an invasive method to integrate advanced molecular diagnostics from genomic data, morphological information from histopathological imaging, and codified clinical data enable the discovery of new multimodal cancer biomarkers to propel the field of precision oncology in the coming decade. In this perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods in pathogenomics. It includes correlation between the pathological and genomic profile of cancer, fusion of histology, and genomics profile of cancer. We also present challenges, opportunities, and avenues for future work. |
| first_indexed | 2024-03-07T14:44:08Z |
| format | Article |
| id | doaj.art-e07527c9edfd44a3b1dae5347e8b573b |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-03-07T14:44:08Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-e07527c9edfd44a3b1dae5347e8b573b2024-03-05T20:06:50ZengBMCJournal of Translational Medicine1479-58762024-02-0122111410.1186/s12967-024-04915-3Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overviewXiaobing Feng0Wen Shu1Mingya Li2Junyu Li3Junyao Xu4Min He5College of Electrical and Information Engineering, Hunan UniversityCollege of Electrical and Information Engineering, Hunan UniversityCollege of Electrical and Information Engineering, Hunan UniversityCollege of Electrical and Information Engineering, Hunan UniversityZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesCollege of Electrical and Information Engineering, Hunan UniversityAbstract The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in harnessing multimodal data in the life sciences. However, most approaches are limited to unimodal data, leaving integrated approaches across modalities relatively underdeveloped in computational pathology. Pathogenomics, as an invasive method to integrate advanced molecular diagnostics from genomic data, morphological information from histopathological imaging, and codified clinical data enable the discovery of new multimodal cancer biomarkers to propel the field of precision oncology in the coming decade. In this perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods in pathogenomics. It includes correlation between the pathological and genomic profile of cancer, fusion of histology, and genomics profile of cancer. We also present challenges, opportunities, and avenues for future work.https://doi.org/10.1186/s12967-024-04915-3PathogenomicsPathomicsGenomicsComputational pathologyPrecision oncology |
| spellingShingle | Xiaobing Feng Wen Shu Mingya Li Junyu Li Junyao Xu Min He Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview Journal of Translational Medicine Pathogenomics Pathomics Genomics Computational pathology Precision oncology |
| title | Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview |
| title_full | Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview |
| title_fullStr | Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview |
| title_full_unstemmed | Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview |
| title_short | Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview |
| title_sort | pathogenomics for accurate diagnosis treatment prognosis of oncology a cutting edge overview |
| topic | Pathogenomics Pathomics Genomics Computational pathology Precision oncology |
| url | https://doi.org/10.1186/s12967-024-04915-3 |
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