ArticleBMC bioinformatics2016
Automated prostate tissue referencing for cancer detection and diagnosis.
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 10 papers.
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Who cites it
10 citing papers in PubMed, 31 citations in OpenAlex.
- Web engine for tumor pathology image retrievals on massive scales.bioRxiv : the preprint server for biology · 2025Article
- Digital analysis of the prostate tumor microenvironment with high-order chromogenic multiplexing.Journal of pathology informatics · 2024Article
- Fast and scalable search of whole-slide images via self-supervised deep learning.Nature biomedical engineering · 2022Article
- Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains.Scientific reports · 2022Article
- Deep learning-based image-analysis algorithm for classification and quantification of multiple histopathological lesions in rat liver.Journal of toxicologic pathology · 2022Article
- Quantification of histopathological findings using a novel image analysis platform.Journal of toxicologic pathology · 2019Article
- Similar image search for histopathology: SMILY.NPJ digital medicine · 2019Article
- An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2018Article
- Quantitative analysis of histopathological findings using image processing software.Journal of toxicologic pathology · 2017Article
- Multiview boosting digital pathology analysis of prostate cancer.Computer methods and programs in biomedicine · 2017Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundThe current practice of histopathology review is limited in speed and accuracy. The current diagnostic paradigm does not fully describe the complex and complicated patterns of cancer. To address these needs, we develop an automated and objective system that facilitates a comprehensive and easy information management and decision-making. We also develop a tissue similarity measure scheme to broaden our understanding of tissue characteristics.
resultsThe system includes a database of previously evaluated prostate tissue images, clinical information and a tissue retrieval process. In the system, a tissue is characterized by its morphology. The retrieval process seeks to find the closest matching cases with the tissue of interest. Moreover, we define 9 morphologic criteria by which a pathologist arrives at a histomorphologic diagnosis. Based on the 9 criteria, true tissue similarity is determined and serves as the gold standard of tissue retrieval. Here, we found a minimum of 4 and 3 matching cases, out of 5, for ~80 % and ~60 % of the queries when a match was defined as the tissue similarity score ≥5 and ≥6, respectively. We were also able to examine the relationship between tissues beyond the Gleason grading system due to the tissue similarity scoring system.
conclusionsProviding the closest matching cases and their clinical information with pathologists will help to conduct consistent and reliable diagnoses. Thus, we expect the system to facilitate quality maintenance and quality improvement of cancer pathology.
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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.