ArticleJournal of clinical medicine2019
Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Diagnosis of Prostate Cancer: A Comparative Evaluation of Biological Techniques.Diseases (Basel, Switzerland) · 2026Review
- From Genes to Proteins: The Indispensable Role of Proteogenomics in Advancing Clear Cell Renal Cell Carcinoma Research.International journal of molecular sciences · 2026Review
- AI-based pathomics in kidney diseases: progress and application.Renal failure · 2025Review
- Microfluidics and molecular diagnostics in renal cell carcinoma: advances, challenges, and future directions.Frontiers in oncology · 2025Review
- Plasma proteomics of acute tubular injury.Nature communications · 2024Article
- A systematic pan-cancer study on deep learning-based prediction of multi-omic biomarkers from routine pathology images.Communications medicine · 2024Article
- Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis.Biomedicines · 2023Review
- Deep learning integrates histopathology and proteogenomics at a pan-cancer level.Cell reports. Medicine · 2023Article
- Non-annotated renal histopathological image analysis with deep ensemble learning.Quantitative imaging in medicine and surgery · 2023Article
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery.Proteomes · 2023Review
- RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features.Diagnostics (Basel, Switzerland) · 2022Article
- Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information.Experimental hematology & oncology · 2022Review
- Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images.Bioengineering (Basel, Switzerland) · 2022Article
- Machine Learning for Renal Pathologies: An Updated Survey.Sensors (Basel, Switzerland) · 2022Review
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Bayesian hierarchical finite mixture of regression for histopathological imaging-based cancer data analysis.Statistics in medicine · 2022Article
- Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery.Frontiers in medicine · 2021Review
- Artificial Intelligence Tools for Refining Lung Cancer Screening.Journal of clinical medicine · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Proteomics data encode molecular features of diagnostic value and accurately reflect key underlying biological mechanisms in cancers. Histopathology imaging is a well-established clinical approach to cancer diagnosis. The predictive relationship between large-scale proteomics and H&E-stained histopathology images remains largely uncharacterized. Here we investigate such associations through the application of machine learning, including deep neural networks, to proteomics and histology imaging datasets generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) from clear cell renal cell carcinoma patients. We report robust correlations between a set of diagnostic proteins and predictions generated by an imaging-based classification model. Proteins significantly correlated with the histology-based predictions are significantly implicated in immune responses, extracellular matrix reorganization, and metabolism. Moreover, we showed that the genes encoding these proteins also reliably recapitulate the biological associations with imaging-derived predictions based on strong gene-protein expression correlations. Our findings offer novel insights into the integrative modeling of histology and omics data through machine learning, as well as the methodological basis for new research opportunities in this and other cancer types.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.