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Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility

  • The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists’ NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC)The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists’ NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists’ estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required.show moreshow less

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Metadaten
Author:Imaine GlahnORCiD, Andreas HaghoferORCiD, Taryn A. DonovanORCiD, Brigitte Degasperi, Alexander BartelORCiD, Theresa Kreilmeier-BergerORCiD, Philip S. HyndmanORCiD, Hannah JanoutORCiD, Charles-Antoine AssenmacherORCiD, Florian BartenschlagerORCiD, Pompei BolfaORCiD, Michael J. DarkORCiD, Andrea KlangORCiD, Robert KlopfleischORCiD, Sophie Merz, Barbara Richter, F. Yvonne SchulmanORCiD, Jonathan GanzORCiD, Josef ScharingerORCiD, Marc AubrevilleORCiD, Stephan M. WinklerORCiD, Christof A. BertramORCiD
Language:English
Document Type:Article
Year of first Publication:2024
published in (English):Veterinary Sciences
Publisher:MDPI
Place of publication:Basel
ISSN:2306-7381
Volume:11
Issue:6
Pages:21
Article Number:278
Review:peer-review
Open Access:ja
Version:published
URN:urn:nbn:de:bvb:573-48612
Related Identifier:https://doi.org/10.3390/vetsci11060278
Faculties / Institutes / Organizations:Fakultät Informatik
AImotion Bavaria
Licence (German):License Logo Creative Commons BY 4.0
Release Date:2024/07/17