ArticleScientific reports2018
Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Safety and immunogenicity of novel 5T4 viral vectored vaccination regimens in early stage prostate cancer: a phase I clinical trial.Journal for immunotherapy of cancer · 2020Trial
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology.Cancer reports (Hoboken, N.J.) · 2023Review
- A hitchhiker's guide to cancer models.Trends in biotechnology · 2022Review
- Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance.The American journal of pathology · 2021Review
- Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature.Journal of clinical medicine · 2021Review
- Assessment of Immunological Features in Muscle-Invasive Bladder Cancer Prognosis Using Ensemble Learning.Cancers · 2021Article
- Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development.International journal of environmental research and public health · 2021Article
- A high-throughput digital script for multiplexed immunofluorescent analysis and quantification of sarcolemmal and sarcomeric proteins in muscular dystrophies.Acta neuropathologica communications · 2020Article
- Specific immune cell and lymphatic vessel signatures identified by image analysis in renal cancer.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2019Article
- Automatic discovery of image-based signatures for ipilimumab response prediction in malignant melanoma.Scientific reports · 2019Article
- Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis.Scientific reports · 2019Article
- Review
- Data-Driven Discovery of Immune Contexture Biomarkers.Frontiers in oncology · 2018Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low- and intermediate-risk prostate cancer patients (Gleason scores 6-7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach.
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