ArticleOncology research2021
Biobanking in the digital pathology era.
Article in Oncology research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Cracking the Code: Computational Image Analysis Tools for Histopathological and Morphometric Insights.Journal of imaging · 2026Review
- Establishing a Cervical Cytology Biorepository: A Protocol for Advancing Translational Cervical Cancer Research through Biobanking.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Connecting biospecimens and data: a scoping review-informed conceptual framework for digital infrastructure in biobanking and genomic research in low- and middle-income countries.Frontiers in public health · 2026Article
- Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models.NPJ breast cancer · 2025Review
- The Regulatory Landscape of Biobanks In Europe: From Accreditation to Intellectual Property.Current genomics · 2025Review
- Deep learning algorithm on H&E whole slide images to characterizeComputational and structural biotechnology journal · 2024Article
- Review
- Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence.Cancers · 2024Review
- Unlocking the potential of big data and AI in medicine: insights from biobanking.Frontiers in medicine · 2024Article
- Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking.Journal of personalized medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital Pathology is becoming more and more important to achieve the goal of precision medicine. Advances in whole-slide imaging, software integration, and the accessibility of storage solutions have changed the pathologists' clinical practice, not only in terms of laboratory workflow but also for diagnosis and biomarkers analysis. In parallel with the pathology setting advancement, translational medicine is approaching the unprecedented opportunities unrevealed by artificial intelligence (AI). Indeed, the increased usage of biobanks' datasets in research provided new challenges for AI applications, such as advanced algorithms, and computer-aided techniques. In this scenario, machine learning-based approaches are being propose in order to improve biobanks from biospecimens collection repositories to computational datasets. To date, evidence on how to implement digital biobanks in translational medicine is still lacking. This viewpoint article summarizes the currently available literature that supports the biobanks' role in the digital pathology era, and to provide possible practical applications of digital biobanks.
Indexed as
Identifiers
What OpenQuestion holds
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.