ArticleJournal of pathology informatics2019
Automated Computational Detection, Quantitation, and Mapping of Mitosis in Whole-Slide Images for Clinically Actionable Surgical Pathology Decision Support.
Article in Journal of pathology informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 23 citations in OpenAlex.
- The evolving physician-AI relationship: a five-tier framework for integrating intelligent systems into clinical practice and medical education.ESMO real world data and digital oncology · 2026Review
- Integrated local and global Ki67 profiling for prognostic stratification and therapeutic guidance in high-grade neuroendocrine neoplasms: a multicenter validation study.International journal of surgery (London, England) · 2025Article
- An Evaluation Into the Robustness of Grading of Pleural Mesothelioma Outside of Specialist Centres.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2025Article
- Deep learning predicts patients outcome and mutations from digitized histology slides in gastrointestinal stromal tumor.NPJ precision oncology · 2023Article
- Surgical Instrument Detection Algorithm Based on Improved YOLOv7x.Sensors (Basel, Switzerland) · 2023Article
- Computer-assisted mitotic count using a deep learning-based algorithm improves interobserver reproducibility and accuracy.Veterinary pathology · 2022Article
- Counting mitoses: SI(ze) matters!Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2021Review
- Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses.Diagnostic pathology · 2020Article
- Agreement in Histological Assessment of Mitotic Activity Between Microscopy and Digital Whole Slide Images Informs Conversion for Clinical Diagnosis.Academic pathologyArticle
Corrections and comments
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Authors and funding
22 authors at 6 institutions in 1 country.
Funding
Abstract
backgroundDetermining mitotic index by counting mitotic figures (MFs) microscopically from tumor areas with most abundant MF (hotspots [HS]) produces a prognostically useful tumor grading biomarker. However, interobserver concordance identifying MF and HS can be poorly reproducible. Immunolabeling MF, coupled with computer-automated counting by image analysis, can improve reproducibility. A computational system for obtaining MF values across digitized whole-slide images (WSIs) was sought that would minimize impact of artifacts, generate values clinically relatable to counting ten high-power microscopic fields of view typical in conventional microscopy, and that would reproducibly map HS topography. MATERIALS AND
methodsRelatively low-resolution WSI scans (0.50 μm/pixel) were imported in grid-tile format for feature-based MF segmentation, from naturally occurring canine melanomas providing a wide range of proliferative activity. MF feature extraction conformed to anti-phospho-histone H3-immunolabeled mitotic (M) phase cells. Computer vision image processing was established to subtract key artifacts, obtain MF counts, and employ rotationally invariant feature extraction to map MF topography.
resultsThe automated topometric HS (TMHS) algorithm identified mitotic HS and mapped select tissue tiles with greatest MF counts back onto WSI thumbnail images to plot HS topographically. Influence of dye, pigment, and extraneous structure artifacts was minimized. TMHS diagnostic decision support included image overlay graphics of HS topography, as well as a spreadsheet and plot of tile-based MF count values. TMHS performance was validated examining both mitotic HS counting and mapping functions. Significantly correlated TMHS MF mapping and metrics were demonstrated using repeat analysis with WSI in different orientation (
conclusionsAutomated TMHS MF segmentation and feature engineering performance were interchangeable with both observer and OCA in digital mode. Moreover, enhanced HS location accuracy and superior method reproducibility were achieved using the automated TMHS algorithm compared to the current practice employing clinical microscopy.
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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.