Automated Detection of Malignant Epithelial Cells in Effusions by DNA-Karyometry | Chapter 9 | Medical Science: Trends and Innovations Vol. 8

Background: The average sensitivity of conventional cytology for the identification of malignant epithelial cells in effusions is only about 58%. DNA-Image-Cytometry (DNA-ICM), which exploits the DNA content of morphologically suspicious nuclei measured on digital images, has a sensitivity for the detection of cells of up to 91%. Yet, when performed manually, an expert so far needs about 60 minutes for the analysis of a single slide.

Aim: This study presented and evaluated a novel solution for rapid, computer-assisted, semiautomated diagnostic DNA cytometry of serous effusion specimens: DNA karyometry (DNA-KM).

Methods: A novel method of supervised machine learning is presented for the automated identification of morphologically suspicious mesothelial and epithelial nuclei in Feulgen-stained effusions. This method was compared to manual DNA-ICM and a gold-standard cytological diagnosis for 121 cases. Furthermore, the potential of using the amount of morphometrically abnormal mesothelial or epithelial nuclei detected by the digital classifier as an additional diagnostic marker was analyzed. SPSS statistical software (version 22.0.001; IBM Corporation, Armonk, New York) was used.

Results: The mean number of lymphocytes automatically identified per slide by digital nuclear classifiers was approximately 100 times higher than the number selected manually (3734.1 vs 33.8). The presented semi-automated DNA-karyometric solution identified more diagnostically relevant abnormal nuclei than manual DNA-ICM, which led to a higher sensitivity (76.4 vs. 68.5%) at 100% specificity. The ratio between digitally abnormal and all mesothelial nuclei can identify cancer-cell positive slides at 100% sensitivity and 70% specificity. The time-effort for an expert is thus reduced to the morphological verification of a few nuclei with exceeding DNA-content, which can be accomplished within five minutes.

Conclusion: A computer-assisted bimodal karyometric approach has been created and validated for which both nuclear morphology and -DNA is quantified from a Feulgen-stained slide. DNA-karyometry thus increases the diagnostic accuracy and reduces the workload of an expert, compared to manual DNA-ICM. In most countries of the world, there are not sufficient numbers of well-trained cytotechnicians and cytopathologists available to screen slides for cancer cells from serous effusion sediment specimens. In addition, the manual procedure is time-consuming, and therefore a semiautomated procedure to improve diagnostic efficiency would be very useful.

 

Author (s) Details

 

Alfred Hermann Böcking
Institute of Cytopathology, University of Düsseldorf, Germany, Consultant cytopathologist, City Hospital Düren, Germany.

 

David Friedrich
Work Performed at Institute of Image Analysis and Computer Vision, RWTH-Aachen University, Germany, Now with Definiens AG, Munich, Germany.

 

Dietrich Meyer-Ebrecht
Institute of Imaging and Computer Vision, RWTH Aachen University, Germany.

 

Chenyan Zhu
Motic Medical Diagnostic Systems Co, Ltd, Xiamen, P.R. China.

 

Anna Feider
Praxis Dr. Link, Mettmann, Düsseldorf, Germany.

 

Stefan Biesterfeld
Institute of Pathology, Koblenz, Germany.

 

Please see the book here:- https://doi.org/10.9734/bpi/msti/v8/4556