ReviewAnnual review of biomedical engineering2016
Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology.
Review in Annual review of biomedical engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 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
74 citing papers in PubMed, 147 citations in OpenAlex.
- Digital Pathology Archives as Long-Term Clinical Memory: A Self-Sovereign Identity-Based Governance Model.Healthcare (Basel, Switzerland) · 2026Article
- Infrared Nano-Spectroscopy of Single Cell Sections at the Ultrastructural Level.Nanophotonics (Berlin, Germany) · 2026Article
- Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration.Frontiers in oncology · 2026Review
- Tumor cell- and infiltrating immune cell-based supervised learning artificial intelligence multimodal platform for tumor prognosis.NPJ precision oncology · 2025Article
- Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms.Haematologica · 2025Article
- Computational pathology approach for assessment of prognosis and immunotherapy response in pan-gastrointestinal cancer.Journal of translational medicine · 2025Article
- Multimodal Integration Enhances Tissue Image Information Content: A Deep Feature Perspective.Bioengineering (Basel, Switzerland) · 2025Article
- Automated determination of tumor cell percentages in whole slide images: A nuclear classification study for molecular pathology tests.Journal of pathology informatics · 2025Article
- Artificial intelligence in digital pathology - time for a reality check.Nature reviews. Clinical oncology · 2025Review
- Can radiomics in brain magnetic resonance imaging predict the mutational status of primary lung cancer based on brain metastasis?Polish journal of radiology · 2025Article
- DLCNBC-SA: a model for assessing axillary lymph node metastasis status in early breast cancer patients.Quantitative imaging in medicine and surgery · 2024Article
- Association of the pathomics-collagen signature with lymph node metastasis in colorectal cancer: a retrospective multicenter study.Journal of translational medicine · 2024Article
- Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images.Scientific reports · 2023Article
- Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment.The Journal of pathology · 2023Article
- Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer.NPJ breast cancer · 2023Article
- Predicting Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy Using a Deep Convolutional Neural Network-Based Artificial Intelligence Tool.JCO clinical cancer informatics · 2023Article
- Predicting colorectal cancer microsatellite instability with a self-attention-enabled convolutional neural network.Cell reports. Medicine · 2023Article
- An artificial intelligence model for the pathological diagnosis of invasion depth and histologic grade in bladder cancer.Journal of translational medicine · 2023Article
- Differentiation of Urothelial Carcinoma and Normal Bladder Tissues by Means of Fiber-Based ATR IR Spectroscopy.Cancers · 2023Article
- Sustainable development goals applied to digital pathology and artificial intelligence applications in low- to middle-income countries.Frontiers in medicine · 2023Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Pathology is essential for research in disease and development, as well as for clinical decision making. For more than 100 years, pathology practice has involved analyzing images of stained, thin tissue sections by a trained human using an optical microscope. Technological advances are now driving major changes in this paradigm toward digital pathology (DP). The digital transformation of pathology goes beyond recording, archiving, and retrieving images, providing new computational tools to inform better decision making for precision medicine. First, we discuss some emerging innovations in both computational image analytics and imaging instrumentation in DP. Second, we discuss molecular contrast in pathology. Molecular DP has traditionally been an extension of pathology with molecularly specific dyes. Label-free, spectroscopic images are rapidly emerging as another important information source, and we describe the benefits and potential of this evolution. Third, we describe multimodal DP, which is enabled by computational algorithms and combines the best characteristics of structural and molecular pathology. Finally, we provide examples of application areas in telepathology, education, and precision medicine. We conclude by discussing challenges and emerging opportunities in this area.
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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.