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    <title language="eng">Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing</title>
    <abstract language="eng">AbstractHistopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users’ trust in computer-assisted image classification.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
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Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. 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      <first_name>Andreas</first_name>
      <last_name>Haghofer</last_name>
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      <first_name>Andrea</first_name>
      <last_name>Fuchs-Baumgartinger</last_name>
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      <first_name>Karoline</first_name>
      <last_name>Lipnik</last_name>
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      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
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      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
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      <first_name>Josef</first_name>
      <last_name>Scharinger</last_name>
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      <first_name>Herbert</first_name>
      <last_name>Weissenböck</last_name>
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      <first_name>Stephan M.</first_name>
      <last_name>Winkler</last_name>
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      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
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    <title language="eng">Nuclear pleomorphism in canine cutaneous mast cell tumors: Comparison of reproducibility and prognostic relevance between estimates, manual morphometry, and algorithmic morphometry</title>
    <abstract language="eng">Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics can improve reproducibility, but current manual methods are time-consuming. The aim of this study was to explore the limitations of estimates and develop alternative morphometric solutions for canine cutaneous mast cell tumors (ccMCTs). We assessed the following nuclear evaluation methods for accuracy, reproducibility, and prognostic utility: (1) anisokaryosis estimates by 11 pathologists; (2) gold standard manual morphometry of at least 100 nuclei; (3) practicable manual morphometry with stratified sampling of 12 nuclei by 9 pathologists; and (4) automated morphometry using deep learning–based segmentation. The study included 96 ccMCTs with available outcome information. Inter-rater reproducibility of anisokaryosis estimates was low (k = 0.226), whereas it was good (intraclass correlation = 0.654) for practicable morphometry of the standard deviation (SD) of nuclear size. As compared with gold standard manual morphometry (area under the ROC curve [AUC] = 0.839, 95% confidence interval [CI] = 0.701–0.977), the prognostic value (tumor-specific survival) of SDs of nuclear area for practicable manual morphometry and automated morphometry were high with an AUC of 0.868 (95% CI = 0.737–0.991) and 0.943 (95% CI = 0.889–0.996), respectively. This study supports the use of manual morphometry with stratified sampling of 12 nuclei and algorithmic morphometry to overcome the poor reproducibility of estimates. Further studies are needed to validate our findings, determine inter-algorithmic reproducibility and algorithmic robustness, and explore tumor heterogeneity of nuclear features in entire tumor sections.</abstract>
    <parentTitle language="eng">Veterinary Pathology</parentTitle>
    <identifier type="issn">1544-2217</identifier>
    <identifier type="issn">0300-9858</identifier>
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The aim of this study was to explore the limitations of estimates and develop alternative morphometric solutions for canine cutaneous mast cell tumors (ccMCTs). We assessed the following nuclear evaluation methods for accuracy, reproducibility, and prognostic utility: (1) anisokaryosis estimates by 11 pathologists; (2) gold standard manual morphometry of at least 100 nuclei; (3) practicable manual morphometry with stratified sampling of 12 nuclei by 9 pathologists; and (4) automated morphometry using deep learning\u2013based segmentation. The study included 96 ccMCTs with available outcome information. Inter-rater reproducibility of anisokaryosis estimates was low (k = 0.226), whereas it was good (intraclass correlation = 0.654) for practicable morphometry of the standard deviation (SD) of nuclear size. 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    <author>
      <first_name>Andreas</first_name>
      <last_name>Haghofer</last_name>
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      <first_name>Eda</first_name>
      <last_name>Parlak</last_name>
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      <first_name>Alexander</first_name>
      <last_name>Bartel</last_name>
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      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
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      <first_name>Charles-Antoine</first_name>
      <last_name>Assenmacher</last_name>
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      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
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      <first_name>Michael</first_name>
      <last_name>Dark</last_name>
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      <first_name>Andrea</first_name>
      <last_name>Fuchs-Baumgartinger</last_name>
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      <first_name>Andrea</first_name>
      <last_name>Klang</last_name>
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      <first_name>Kathrin</first_name>
      <last_name>Jäger</last_name>
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      <first_name>Robert</first_name>
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      <last_name>Janout</last_name>
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      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
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      <first_name>Josef</first_name>
      <last_name>Scharinger</last_name>
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      <first_name>Marc</first_name>
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      <first_name>Stephan M.</first_name>
      <last_name>Winkler</last_name>
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      <first_name>Matti</first_name>
      <last_name>Kiupel</last_name>
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      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
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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>
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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. Box 334, Saint Kitts and Nevis"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6547-1298","authenticated-orcid":false,"given":"Michael J.","family":"Dark","sequence":"additional","affiliation":[{"name":"College of Veterinary Medicine, University of Florida, Gainesville, FL 32611, USA"}]},{"given":"Andrea","family":"Klang","sequence":"additional","affiliation":[{"name":"Institute of Pathology, University of Veterinary Medicine Vienna, 1210 Vienna, Austria"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6308-0568","authenticated-orcid":false,"given":"Robert","family":"Klopfleisch","sequence":"additional","affiliation":[{"name":"Institute of Veterinary Pathology, Freie Universit\u00e4t Berlin, 14163 Berlin, Germany"}]},{"given":"Sophie","family":"Merz","sequence":"additional","affiliation":[{"name":"IDEXX Vet Med Labor GmbH, 70806 Kornwestheim, Germany"}]},{"given":"Barbara","family":"Richter","sequence":"additional","affiliation":[{"name":"Institute of Pathology, University of Veterinary Medicine Vienna, 1210 Vienna, Austria"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7454-8517","authenticated-orcid":false,"given":"F. Yvonne","family":"Schulman","sequence":"additional","affiliation":[{"name":"Antech Diagnostics, Mars Petcare Science and Diagnostics, Fountain Valley, CA 92708, USA"}]},{"given":"Jonathan","family":"Ganz","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Technische Hochschule Ingolstadt, 85049 Ingolstadt, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6502-7501","authenticated-orcid":false,"given":"Josef","family":"Scharinger","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Johannes Kepler University, 4040 Linz, Austria"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5294-5247","authenticated-orcid":false,"given":"Marc","family":"Aubreville","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Technische Hochschule Ingolstadt, 85049 Ingolstadt, Germany"}]},{"given":"Stephan M.","family":"Winkler","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\/0000-0002-2402-9997","authenticated-orcid":false,"given":"Christof A.","family":"Bertram","sequence":"additional","affiliation":[{"name":"Institute of Pathology, University of Veterinary Medicine Vienna, 1210 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,17]]},"reference":[{"key":"ref_1","first-page":"1061","article-title":"Surgical Treatment of Primary Pulmonary Neoplasia in 15 Dogs","volume":"20","author":"Mehlhaff","year":"1984","journal-title":"J. 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    <author>
      <first_name>Imaine</first_name>
      <last_name>Glahn</last_name>
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      <first_name>Andreas</first_name>
      <last_name>Haghofer</last_name>
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      <first_name>Taryn</first_name>
      <last_name>Donovan</last_name>
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    <author>
      <first_name>Brigitte</first_name>
      <last_name>Degasperi</last_name>
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    <author>
      <first_name>Alexander</first_name>
      <last_name>Bartel</last_name>
    </author>
    <author>
      <first_name>Theresa</first_name>
      <last_name>Kreilmeier-Berger</last_name>
    </author>
    <author>
      <first_name>Philip S.</first_name>
      <last_name>Hyndman</last_name>
    </author>
    <author>
      <first_name>Hannah</first_name>
      <last_name>Janout</last_name>
    </author>
    <author>
      <first_name>Charles-Antoine</first_name>
      <last_name>Assenmacher</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Bartenschlager</last_name>
    </author>
    <author>
      <first_name>Pompei</first_name>
      <last_name>Bolfa</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Dark</last_name>
    </author>
    <author>
      <first_name>Andrea</first_name>
      <last_name>Klang</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Sophie</first_name>
      <last_name>Merz</last_name>
    </author>
    <author>
      <first_name>Barbara</first_name>
      <last_name>Richter</last_name>
    </author>
    <author>
      <first_name>F. Yvonne</first_name>
      <last_name>Schulman</last_name>
    </author>
    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
    </author>
    <author>
      <first_name>Josef</first_name>
      <last_name>Scharinger</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Stephan M.</first_name>
      <last_name>Winkler</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/4861/vetsci-11-00278-v2.pdf</file>
  </doc>
</export-example>
