Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview

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 relati...

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Published inJournal of translational medicine Vol. 22; no. 1; pp. 131 - 14
Main Authors Feng, Xiaobing, Shu, Wen, Li, Mingya, Li, Junyu, Xu, Junyao, He, Min
Format Journal Article
LanguageEnglish
Published England BioMed Central Ltd 03.02.2024
BioMed Central
BMC
Subjects
Accuracy and precision
Algorithms
Analysis
Artificial intelligence
Biomarkers
Cancer
Care and treatment
Computational pathology
Deep learning
Diagnosis
DNA methylation
DNA sequencing
Gene expression
Genomics
Health aspects
Histopathology
Machine learning
Medical diagnosis
Medical prognosis
Methods
Morphology
Mutation
Nucleotide sequencing
Oncology
Pathogenomics
Pathology
Pathomics
Precision medicine
Precision oncology
Prognosis
Quantitative analysis
Tumors
China
Pathomics
Pathogenomics
Precision oncology
Genomics
Computational pathology
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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.
AbstractList 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.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.
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.
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.
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. Keywords: Pathogenomics, Pathomics, Genomics, Computational pathology, Precision oncology
ArticleNumber 131
Audience Academic
Author Shu, Wen
Feng, Xiaobing
Li, Mingya
He, Min
Li, Junyu
Xu, Junyao
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Pathogenomics
Precision oncology
Genomics
Computational pathology
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Snippet The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in harnessing...
Abstract The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in...
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StartPage 131
SubjectTerms Accuracy and precision
Algorithms
Analysis
Artificial intelligence
Biomarkers
Cancer
Care and treatment
Computational pathology
Deep learning
Diagnosis
DNA methylation
DNA sequencing
Gene expression
Genomics
Health aspects
Histopathology
Machine learning
Medical diagnosis
Medical prognosis
Methods
Morphology
Mutation
Nucleotide sequencing
Oncology
Pathogenomics
Pathology
Pathomics
Precision medicine
Precision oncology
Prognosis
Quantitative analysis
Tumors
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Title Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview
URI https://www.ncbi.nlm.nih.gov/pubmed/38310237
https://www.proquest.com/docview/2925689353
https://www.proquest.com/docview/2922448321
https://doaj.org/article/e07527c9edfd44a3b1dae5347e8b573b
Volume 22
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