ArticleCell systems2017
Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma.
Article in Cell systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed.
- Hierarchical structure-guided high-dimensional multi-view clustering.Journal of multivariate analysis · 2026Article
- Contrastive learning enhances fairness in pathology artificial intelligence systems.Cell reports. Medicine · 2025Article
- Mass spectrometry-based proteomics of FFPE tissues: progress, limitations, and clinical translation barriers.Clinical proteomics · 2025Review
- Prediction of radiosensitivity in non-small cell lung cancer based on computed tomography and tumor genomics: a multiple real world cohort study.Respiratory research · 2025Article
- Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models.Nature biomedical engineering · 2025Article
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- A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis.Frontiers in medicine · 2025Review
- Identification of metabolism-associated molecular classification for effect and prognosis in lung adenocarcinoma based on multidatabases including the cancer genome atlas and gene expression omnibus.SAGE open medicine · 2025Article
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- Information-incorporated sparse hierarchical cancer heterogeneity analysis.Statistics in medicine · 2024Article
- TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models.Patterns (New York, N.Y.) · 2024Article
- Clustering on hierarchical heterogeneous data with prior pairwise relationships.BMC bioinformatics · 2024Article
- Artificial Intelligence Reveals Distinct Prognostic Subgroups of Muscle-Invasive Bladder Cancer on Histology Images.Cancers · 2023Article
- Machine learning for cryosection pathology predicts the 2021 WHO classification of glioma.Med (New York, N.Y.) · 2023Article
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients.Nature communications · 2023Article
- HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES.Statistica Sinica · 2023Article
- Hierarchical cancer heterogeneity analysis based on histopathological imaging features.Biometrics · 2022Article
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Pan-Cancer Analysis IdentifiesCells · 2022Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Adenocarcinoma accounts for more than 40% of lung malignancy, and microscopic pathology evaluation is indispensable for its diagnosis. However, how histopathology findings relate to molecular abnormalities remains largely unknown. Here, we obtained H&E-stained whole-slide histopathology images, pathology reports, RNA sequencing, and proteomics data of 538 lung adenocarcinoma patients from The Cancer Genome Atlas and used these to identify molecular pathways associated with histopathology patterns. We report cell-cycle regulation and nucleotide binding pathways underpinning tumor cell dedifferentiation, and we predicted histology grade using transcriptomics and proteomics signatures (area under curve >0.80). We built an integrative histopathology-transcriptomics model to generate better prognostic predictions for stage I patients (p = 0.0182 ± 0.0021) compared with gene expression or histopathology studies alone, and the results were replicated in an independent cohort (p = 0.0220 ± 0.0070). These results motivate the integration of histopathology and omics data to investigate molecular mechanisms of pathology findings and enhance clinical prognostic prediction.
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