ArticleCancers2019
Examination of Independent Prognostic Power of Gene Expressions and Histopathological Imaging Features in Cancer.
Article in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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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
19 citing papers in PubMed.
- Hierarchical structure-guided high-dimensional multi-view clustering.Journal of multivariate analysis · 2026Article
- Multi-platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinoma.Frontiers in oncology · 2025Article
- Article
- Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system.Cell reports. Medicine · 2024Article
- Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview.Journal of translational medicine · 2024Review
- Clustering on hierarchical heterogeneous data with prior pairwise relationships.BMC bioinformatics · 2024Article
- Graph-based multi-modality integration for prediction of cancer subtype and severity.Scientific reports · 2023Article
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- 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
- Histopathological imaging-based cancer heterogeneity analysis via penalized fusion with model averaging.Biometrics · 2021Article
- Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma.Cancer medicine · 2021Article
- Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma.Frontiers in oncology · 2021Article
- Histopathological imaging features- versus molecular measurements-based cancer prognosis modeling.Scientific reports · 2020Article
- Multiomics and machine learning in lung cancer prognosis.Journal of thoracic disease · 2020Article
- Integrative Models of Histopathological Image Features and Omics Data Predict Survival in Head and Neck Squamous Cell Carcinoma.Frontiers in cell and developmental biology · 2020Article
- Applications of Bioinformatics in Cancer.Cancers · 2019Article
- Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning.Journal of clinical medicine · 2019Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Cancer prognosis is of essential interest, and extensive research has been conducted searching for biomarkers with prognostic power. Recent studies have shown that both omics profiles and histopathological imaging features have prognostic power. There are also studies exploring integrating the two types of measurements for prognosis modeling. However, there is a lack of study rigorously examining whether omics measurements have independent prognostic power conditional on histopathological imaging features, and vice versa. In this article, we adopt a rigorous statistical testing framework and test whether an individual gene expression measurement can improve prognosis modeling conditional on high-dimensional imaging features, and a parallel analysis is conducted reversing the roles of gene expressions and imaging features. In the analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma and liver hepatocellular carcinoma data, it is found that multiple individual genes, conditional on imaging features, can lead to significant improvement in prognosis modeling; however, individual imaging features, conditional on gene expressions, only offer limited prognostic power. Being among the first to examine the independent prognostic power, this study may assist better understanding the "connectedness" between omics profiles and histopathological imaging features and provide important insights for data integration in cancer modeling.
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Registered trials
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