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Karyotyping

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Updated: Aug 29, 2025

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

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Published on: December 16, 2014

DNA Karyometry for Automated Detection of Cancer Cells.

Alfred Böcking¹, David Friedrich², Martin Schramm³

¹Institute of Cytopathology, University Clinics, 40225 Düsseldorf, Germany.

|September 9, 2022

Summary

An automated microscope system accurately detects cancer cells in patient samples, matching expert accuracy. This technology can identify early prostate cancers unlikely to progress, aiding in treatment decisions.

Keywords:

Fanconi anemia automated microscope-based screening cancer cell detection computer assisted diagnosis grading prostate cancer oral smears supervised machine learning

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An Automated Differential Nuclear Staining Assay for Accurate Determination of Mitocan Cytotoxicity

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Last Updated: Aug 29, 2025

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Assessment of Global DNA Double-Strand End Resection using BrdU-DNA Labeling coupled with Cell Cycle Discrimination Imaging

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Published on: April 28, 2021

An Automated Differential Nuclear Staining Assay for Accurate Determination of Mitocan Cytotoxicity

An Automated Differential Nuclear Staining Assay for Accurate Determination of Mitocan Cytotoxicity

Published on: May 12, 2020

Area of Science:

Biomedical Engineering
Computational Pathology
Oncology

Background:

High-throughput cancer cell screening demands specialized personnel, limiting global accessibility.
Current cytological assessments require expert interpretation, posing a bottleneck in diagnostics.

Purpose of the Study:

To develop an automated microscope system for accurate cancer cell detection.
To assess the system's diagnostic accuracy across various sample types.
To identify non-progressing early prostate cancers in patients under active surveillance.

Main Methods:

Utilized automated microscope screeners (MotiCyte, EasyScan) with supervised machine learning software.
Classified Feulgen-stained nuclei based on morphology and DNA content (aneuploidy).
Validated the system on oral smears, serous effusion specimens, and prostate cancer patient data.

Main Results:

Achieved 91.3% diagnostic accuracy for oral cancer screening, surpassing conventional cytology (75.0%).
Automated screening of effusions yielded 84.3% accuracy, while conventional cytology was 95.9%.
Identified no disease progression in low-grade (DNA-grade 1) prostate cancers over 4.1 years.

Conclusions:

Developed an automated microscope system with diagnostic accuracy comparable to subjective cytological assessment.
The system effectively identifies malignant cells across diverse human specimens.
Enables identification of indolent early prostate cancers, potentially guiding active surveillance strategies.