ArticleBMC bioinformatics2016
Joint analysis of histopathology image features and gene expression in breast cancer.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 24 citations in OpenAlex.
- Integrating multimodal data to predict the progression of hormone-sensitive prostate cancer.Clinical proteomics · 2025Article
- Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer.Scientific reports · 2023Article
- Histopathology image classification: highlighting the gap between manual analysis and AI automation.Frontiers in oncology · 2023Article
- Artificial intelligence in histopathology: enhancing cancer research and clinical oncology.Nature cancer · 2022Review
- Future directions for precision oncology in prostate cancer.The Prostate · 2022Review
- Harnessing multimodal data integration to advance precision oncology.Nature reviews. Cancer · 2022Review
- Machine learning based tissue analysis reveals Brachyury has a diagnosis value in breast cancer.Bioscience reports · 2021Article
- Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer.BMC medical genomics · 2020Article
- Deep Learning for Whole-Slide Tissue Histopathology Classification: A Comparative Study in the Identification of Dysplastic and Non-Dysplastic Barrett's Esophagus.Journal of personalized medicine · 2020Article
- Article
- Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype.NPJ breast cancer · 2018Article
- Translational Bioinformatics for Cholangiocarcinoma: Opportunities and Challenges.International journal of biological sciences · 2018Review
- A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenomics and proteomics are nowadays the dominant techniques for novel biomarker discovery. However, histopathology images contain a wealth of information related to the tumor histology, morphology and tumor-host interactions that is not accessible through these techniques. Thus, integrating the histopathology images in the biomarker discovery workflow could potentially lead to the identification of new image-based biomarkers and the refinement or even replacement of the existing genomic and proteomic signatures. However, extracting meaningful and robust image features to be mined jointly with genomic (and clinical, etc.) data represents a real challenge due to the complexity of the images.
resultsWe developed a framework for integrating the histopathology images in the biomarker discovery workflow based on the bag-of-features approach - a method that has the advantage of being assumption-free and data-driven. The images were reduced to a set of salient patterns and additional measurements of their spatial distribution, with the resulting features being directly used in a standard biomarker discovery application. We demonstrated this framework in a search for prognostic biomarkers in breast cancer which resulted in the identification of several prognostic image features and a promising multimodal (imaging and genomic) prognostic signature. The source code for the image analysis procedures is freely available.
conclusionsThe framework proposed allows for a joint analysis of images and gene expression data. Its application to a set of breast cancer cases resulted in image-based and combined (image and genomic) prognostic scores for relapse-free survival.
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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.