ReviewBiomolecules2023
Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology.
Review in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed, 17 citations in OpenAlex.
- Artificial intelligence in pancreatic cancer: applications in early detection, tumor staging, and survival prediction-a comprehensive review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Article
- Standardized Instance-Level Quantification of CD34-Positive Vessels in Lymph Node Whole-Slide Images Using U-Net.Pathology international · 2026Article
- Trends in artificial intelligence and machine learning for renal cancer.Discover oncology · 2025Article
- Computer Vision-Assisted Spatial Analysis of Mitoses and Vasculature in Lung Cancer.Journal of clinical medicine · 2025Article
- Predicting Grade and Patient Survival in Renal Cancer Using Machine Learning Analysis of Nucleolar Prominence.Cancer medicine · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- LVI-PathNet: Segmentation-classification pipeline for detection of lymphovascular invasion in whole slide images of lung adenocarcinoma.Journal of pathology informatics · 2024Article
- ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts.Frontiers in bioengineering and biotechnology · 2024Article
- A Comprehensive Look at In Vitro Angiogenesis Image Analysis Software.International journal of molecular sciences · 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
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics.
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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.