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    <id>4861</id>
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    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>21</pageNumber>
    <edition/>
    <issue>6</issue>
    <volume>11</volume>
    <articleNumber>278</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
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    <title language="eng">Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Veterinary Sciences</parentTitle>
    <identifier type="issn">2306-7381</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-48612</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
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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\u2019 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\u2019 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.&lt;\/jats:p&gt;","DOI":"10.3390\/vetsci11060278","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T15:14:16Z","timestamp":1718637256000},"page":"278","source":"Crossref","is-referenced-by-count":0,"title":["Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"http:\/\/orcid.org\/0009-0003-9247-9302","authenticated-orcid":false,"given":"Imaine","family":"Glahn","sequence":"first","affiliation":[{"name":"Institute of Pathology, University of Veterinary Medicine Vienna, 1210 Vienna, Austria"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6649-5374","authenticated-orcid":false,"given":"Andreas","family":"Haghofer","sequence":"additional","affiliation":[{"name":"Bioinformatics Research Group, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria"},{"name":"Department of Computer Science, Johannes Kepler University, 4040 Linz, Austria"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5740-9550","authenticated-orcid":false,"given":"Taryn A.","family":"Donovan","sequence":"additional","affiliation":[{"name":"Department of Anatomic Pathology, The Schwarzman Animal Medical Center, New York, NY 10065, USA"}]},{"given":"Brigitte","family":"Degasperi","sequence":"additional","affiliation":[{"name":"University Clinic for Small Animals, University of Veterinary Medicine Vienna, 1210 Vienna, Austria"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1280-6138","authenticated-orcid":false,"given":"Alexander","family":"Bartel","sequence":"additional","affiliation":[{"name":"Institute for Veterinary Epidemiology and Biostatistics, Freie Universit\u00e4t Berlin, 14163 Berlin, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5346-2821","authenticated-orcid":false,"given":"Theresa","family":"Kreilmeier-Berger","sequence":"additional","affiliation":[{"name":"University Clinic for Small Animals, University of Veterinary Medicine Vienna, 1210 Vienna, Austria"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4367-4032","authenticated-orcid":false,"given":"Philip S.","family":"Hyndman","sequence":"additional","affiliation":[{"name":"Department of Anatomic Pathology, The Schwarzman Animal Medical Center, New York, NY 10065, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0294-3585","authenticated-orcid":false,"given":"Hannah","family":"Janout","sequence":"additional","affiliation":[{"name":"Bioinformatics Research Group, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria"},{"name":"Department of Computer Science, Johannes Kepler University, 4040 Linz, Austria"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0073-2730","authenticated-orcid":false,"given":"Charles-Antoine","family":"Assenmacher","sequence":"additional","affiliation":[{"name":"Comparative Pathology Core, Department of Pathobiology, University of Pennsylvania, Philadelphia, PA 19104, USA"}]},{"given":"Florian","family":"Bartenschlager","sequence":"additional","affiliation":[{"name":"Institute of Veterinary Pathology, Freie Universit\u00e4t Berlin, 14163 Berlin, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2903-1535","authenticated-orcid":false,"given":"Pompei","family":"Bolfa","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Ross University School of Veterinary Medicine, Basseterre P.O. 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