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Article ; Online: SFRP1 Expression is Inversely Associated With Metastasis Formation in Canine Mammary Tumours.

Seitz, Judith / Bilsland, Alan / Puget, Chloé / Baasner, Ian / Klopfleisch, Robert / Stein, Torsten

Journal of mammary gland biology and neoplasia

2023 Volume 28, Issue 1, Page(s) 15

Abstract: Background: Canine mammary tumours (CMTs) are the most frequent tumours in intact female dogs and show strong similarities with human breast cancer. In contrast to the human disease there are no standardised diagnostic or prognostic biomarkers available ...

Abstract	Background: Canine mammary tumours (CMTs) are the most frequent tumours in intact female dogs and show strong similarities with human breast cancer. In contrast to the human disease there are no standardised diagnostic or prognostic biomarkers available to guide treatment. We recently identified a prognostic 18-gene RNA signature that could stratify human breast cancer patients into groups with significantly different risk of distant metastasis formation. Here, we assessed whether expression patterns of these RNAs were also associated with canine tumour progression. Method: A sequential forward feature selection process was performed on a previously published microarray dataset of 27 CMTs with and without lymph node (LN) metastases to identify RNAs with significantly differential expression to identify prognostic genes within the 18-gene signature. Using an independent set of 33 newly identified archival CMTs, we compared expression of the identified prognostic subset on RNA and protein basis using RT-qPCR and immunohistochemistry on FFPE-tissue sections. Results: While the 18-gene signature as a whole did not have any prognostic power, a subset of three RNAs: Col13a1, Spock2, and Sfrp1, together completely separated CMTs with and without LN metastasis in the microarray set. However, in the new independent set assessed by RT-qPCR, only the Wnt-antagonist Sfrp1 showed significantly increased mRNA abundance in CMTs without LN metastases on its own (p = 0.013) in logistic regression analysis. This correlated with stronger SFRP1 protein staining intensity of the myoepithelium and/or stroma (p < 0.001). SFRP1 staining, as well as β-catenin membrane staining, was significantly associated with negative LN status (p = 0.010 and 0.014 respectively). However, SFRP1 did not correlate with β-catenin membrane staining (p = 0.14). Conclusion: The study identified SFRP1 as a potential biomarker for metastasis formation in CMTs, but lack of SFRP1 was not associated with reduced membrane-localisation of β-catenin in CMTs.
MeSH term(s)	Humans ; Dogs ; Animals ; Female ; beta Catenin/metabolism ; Prognosis ; Lymphatic Metastasis ; Mammary Neoplasms, Animal/pathology ; RNA ; Breast Neoplasms/genetics
Chemical Substances	beta Catenin ; RNA (63231-63-0)
Language	English
Publishing date	2023-07-04
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1327345-0
ISSN	1573-7039 ; 1083-3021
ISSN (online)	1573-7039
ISSN	1083-3021
DOI	10.1007/s10911-023-09543-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry.

Wilm, Frauke / Ihling, Christian / Méhes, Gábor / Terracciano, Luigi / Puget, Chloé / Klopfleisch, Robert / Schüffler, Peter / Aubreville, Marc / Maier, Andreas / Mrowiec, Thomas / Breininger, Katharina

Journal of pathology informatics

2023 Volume 14, Page(s) 100301

Abstract: The success of immuno-oncology treatments promises long-term cancer remission for an increasing number of patients. The response to checkpoint inhibitor drugs has shown a correlation with the presence of immune cells in the tumor and tumor ... ...

Abstract	The success of immuno-oncology treatments promises long-term cancer remission for an increasing number of patients. The response to checkpoint inhibitor drugs has shown a correlation with the presence of immune cells in the tumor and tumor microenvironment. An in-depth understanding of the spatial localization of immune cells is therefore critical for understanding the tumor's immune landscape and predicting drug response. Computer-aided systems are well suited for efficiently quantifying immune cells in their spatial context. Conventional image analysis approaches are often based on color features and therefore require a high level of manual interaction. More robust image analysis methods based on deep learning are expected to decrease this reliance on human interaction and improve the reproducibility of immune cell scoring. However, these methods require sufficient training data and previous work has reported low robustness of these algorithms when they are tested on out-of-distribution data from different pathology labs or samples from different organs. In this work, we used a new image analysis pipeline to explicitly evaluate the robustness of marker-labeled lymphocyte quantification algorithms depending on the number of training samples before and after being transferred to a new tumor indication. For these experiments, we adapted the RetinaNet architecture for the task of T-lymphocyte detection and employed transfer learning to bridge the domain gap between tumor indications and reduce the annotation costs for unseen domains. On our test set, we achieved human-level performance for almost all tumor indications with an average precision of 0.74 in-domain and 0.72-0.74 cross-domain. From our results, we derive recommendations for model development regarding annotation extent, training sample selection, and label extraction for the development of robust algorithms for immune cell scoring. By extending the task of marker-labeled lymphocyte quantification to a multi-class detection task, the pre-requisite for subsequent analyses, e.g., distinguishing lymphocytes in the tumor stroma from tumor-infiltrating lymphocytes, is met.
Language	English
Publishing date	2023-02-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	2579241-6
ISSN	2153-3539 ; 2229-5089
ISSN (online)	2153-3539
ISSN	2229-5089
DOI	10.1016/j.jpi.2023.100301
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Article ; Online: Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images

Fragoso-Garcia, Marco / Wilm, Frauke / Bertram, Christof A. / Merz, Sophie / Schmidt, Anja / Donovan, Taryn / Fuchs-Baumgartinger, Andrea / Bartel, Alexander / Marzahl, Christian / Diehl, Laura / Puget, Chloe / Maier, Andreas / Aubreville, Marc / Breininger, Katharina / Klopfleisch, Robert

Veterinary Pathology. 2023 Nov., v. 60, no. 6 p.865-875

2023

Abstract: Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer ... ...

Abstract	Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation (n = 35 WSIs), and test sets (n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types.
Keywords	algorithms ; animal pathology ; automation ; computers ; data collection ; databases ; dogs ; image analysis ; mast cells ; melanoma ; nerve tissue ; neural networks ; plasmacytoma ; squamous cell carcinoma ; computer-aided diagnosis ; computational pathology ; digital pathology ; dog ; machine learning ; skin ; veterinary oncology
Language	English
Dates of publication	2023-11
Size	p. 865-875.
Publishing place	SAGE Publications
Document type	Article ; Online
ZDB-ID	188012-3
ISSN	1544-2217 ; 0300-9858
ISSN (online)	1544-2217
ISSN	0300-9858
DOI	10.1177/03009858231189205
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Deep Learning model predicts the c-Kit-11 mutational status of canine cutaneous mast cell tumors by HE stained histological slides

Puget, Chloé / Ganz, Jonathan / Ostermaier, Julian / Konrad, Thomas / Parlak, Eda / Bertram, Christof Albert / Kiupel, Matti / Breininger, Katharina / Aubreville, Marc / Klopfleisch, Robert

2024

Abstract: Numerous prognostic factors are currently assessed histopathologically in biopsies of canine mast cell tumors to evaluate clinical behavior. In addition, PCR analysis of the c-Kit exon 11 mutational status is often performed to evaluate the potential ... ...

Abstract	Numerous prognostic factors are currently assessed histopathologically in biopsies of canine mast cell tumors to evaluate clinical behavior. In addition, PCR analysis of the c-Kit exon 11 mutational status is often performed to evaluate the potential success of a tyrosine kinase inhibitor therapy. This project aimed at training deep learning models (DLMs) to identify the c-Kit-11 mutational status of MCTs solely based on morphology without additional molecular analysis. HE slides of 195 mutated and 173 non-mutated tumors were stained consecutively in two different laboratories and scanned with three different slide scanners. This resulted in six different datasets (stain-scanner variations) of whole slide images. DLMs were trained with single and mixed datasets and their performances was assessed under scanner and staining domain shifts. The DLMs correctly classified HE slides according to their c-Kit 11 mutation status in, on average, 87% of cases for the best-suited stain-scanner variant. A relevant performance drop could be observed when the stain-scanner combination of the training and test dataset differed. Multi-variant datasets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant. In summary, DLM-assisted morphological examination of MCTs can predict c-Kit-exon 11 mutational status of MCTs with high accuracy. However, the recognition performance is impeded by a change of scanner or staining protocol. Larger data sets with higher numbers of scans originating from different laboratories and scanners may lead to more robust DLMs to identify c-Kit mutations in HE slides. Comment: 17 pages, 3 figures, 4 tables
Keywords	Quantitative Biology - Biomolecules ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; J.3
Subject code	006
Publishing date	2024-01-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images.

Fragoso-Garcia, Marco / Wilm, Frauke / Bertram, Christof A / Merz, Sophie / Schmidt, Anja / Donovan, Taryn / Fuchs-Baumgartinger, Andrea / Bartel, Alexander / Marzahl, Christian / Diehl, Laura / Puget, Chloe / Maier, Andreas / Aubreville, Marc / Breininger, Katharina / Klopfleisch, Robert

Veterinary pathology

2023 Volume 60, Issue 6, Page(s) 865–875

Abstract	Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training,
MeSH term(s)	Animals ; Dogs ; Artificial Intelligence ; Deep Learning ; Eosine Yellowish-(YS) ; Hematoxylin ; Reproducibility of Results ; Skin Neoplasms/diagnosis ; Skin Neoplasms/veterinary ; Machine Learning ; Dog Diseases/diagnosis
Chemical Substances	Eosine Yellowish-(YS) (TDQ283MPCW) ; Hematoxylin (YKM8PY2Z55)
Language	English
Publishing date	2023-07-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	188012-3
ISSN	1544-2217 ; 0300-9858
ISSN (online)	1544-2217
ISSN	0300-9858
DOI	10.1177/03009858231189205
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset.

Wilm, Frauke / Fragoso, Marco / Marzahl, Christian / Qiu, Jingna / Puget, Chloé / Diehl, Laura / Bertram, Christof A / Klopfleisch, Robert / Maier, Andreas / Breininger, Katharina / Aubreville, Marc

Scientific data

2022 Volume 9, Issue 1, Page(s) 588

Abstract: Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. ...

Abstract	Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.
MeSH term(s)	Algorithms ; Animals ; Dog Diseases/pathology ; Dogs ; Neural Networks, Computer ; Skin Neoplasms/pathology ; Skin Neoplasms/veterinary
Language	English
Publishing date	2022-09-27
Publishing country	England
Document type	Dataset ; Journal Article
ZDB-ID	2775191-0
ISSN	2052-4463 ; 2052-4463
ISSN (online)	2052-4463
ISSN	2052-4463
DOI	10.1038/s41597-022-01692-w
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Deep Learning-Based Automatic Assessment of AgNOR-scores in Histopathology Images

Ganz, Jonathan / Lipnik, Karoline / Ammeling, Jonas / Richter, Barbara / Puget, Chloé / Parlak, Eda / Diehl, Laura / Klopfleisch, Robert / Donovan, Taryn A. / Kiupel, Matti / Bertram, Christof A. / Breininger, Katharina / Aubreville, Marc

2022

Abstract: Nucleolar organizer regions (NORs) are parts of the DNA that are involved in RNA transcription. Due to the silver affinity of associated proteins, argyrophilic NORs (AgNORs) can be visualized using silver-based staining. The average number of AgNORs per ... ...

Abstract	Nucleolar organizer regions (NORs) are parts of the DNA that are involved in RNA transcription. Due to the silver affinity of associated proteins, argyrophilic NORs (AgNORs) can be visualized using silver-based staining. The average number of AgNORs per nucleus has been shown to be a prognostic factor for predicting the outcome of many tumors. Since manual detection of AgNORs is laborious, automation is of high interest. We present a deep learning-based pipeline for automatically determining the AgNOR-score from histopathological sections. An additional annotation experiment was conducted with six pathologists to provide an independent performance evaluation of our approach. Across all raters and images, we found a mean squared error of 0.054 between the AgNOR- scores of the experts and those of the model, indicating that our approach offers performance comparable to humans. Comment: 6 pages, 2 figures, 1 table
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2022-12-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

Wilm, Frauke / Fragoso, Marco / Marzahl, Christian / Qiu, Jingna / Puget, Chloé / Diehl, Laura / Bertram, Christof A. / Klopfleisch, Robert / Maier, Andreas / Breininger, Katharina / Aubreville, Marc

2022

Abstract	Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application. Comment: Submitted to Scientific Data. 15 pages, 9 figures, 6 tables
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	004 ; 006
Publishing date	2022-01-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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