ArticleCancers2019
Histopathological Imaging⁻Environment Interactions in Cancer Modeling.
Article in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Machine-learning models based on histological images from healthy donors identify imageQTLs and predict chronological age.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Ordinal Sparse Neural Networks for Modeling Gene- and Imaging-Environment Interactions.Statistics in medicine · 2025Article
- Machine Learning Models Based on Histological Images from Healthy Donors Identify ImageQTLs and Predict Chronological Age.bioRxiv : the preprint server for biology · 2025Article
- GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.Bioinformatics (Oxford, England) · 2025Article
- Article
- Bayesian finite mixture of regression analysis for cancer based on histopathological imaging-environment interactions.Biostatistics (Oxford, England) · 2023Article
- 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
- Applications of Bioinformatics in Cancer.Cancers · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Histopathological imaging has been routinely conducted in cancer diagnosis and recently used for modeling other cancer outcomes/phenotypes such as prognosis. Clinical/environmental factors have long been extensively used in cancer modeling. However, there is still a lack of study exploring possible interactions of histopathological imaging features and clinical/environmental risk factors in cancer modeling. In this article, we explore such a possibility and conduct both marginal and joint interaction analysis. Novel statistical methods, which are "borrowed" from gene⁻environment interaction analysis, are employed. Analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) data is conducted. More specifically, we examine a biomarker of lung function as well as overall survival. Possible interaction effects are identified. Overall, this study can suggest an alternative way of cancer modeling that innovatively combines histopathological imaging and clinical/environmental data.
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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.