QuPath Analysis for CD30+ Cutaneous T-Cell Lymphoma

Cieslak, Cassandra; Mitteldorf, Christina; Krömer-Olbrisch, Tanja; Kempf, Werner; Stadler, Rudolf

doi:10.1097/dad.0000000000002330

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…In the diagnostic field, all the mentioned software provide relevant advantages compared to manual image analysis, which is highly labor-intensive and liable to variability [71,85,101,143].…”

Section: Discussionmentioning

confidence: 99%

Digital Pathology: A Comprehensive Review of Open-Source Histological Segmentation Software

Pavone,

Giannone,

Cabibi

et al. 2024

BioMedInformatics

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In the era of digitalization, the biomedical sector has been affected by the spread of artificial intelligence. In recent years, the possibility of using deep and machine learning methods for clinical diagnostic and therapeutic interventions has been emerging as an essential resource for biomedical imaging. Digital pathology represents innovation in a clinical world that looks for faster and better-performing diagnostic methods, without losing the accuracy of current human-guided analyses. Indeed, artificial intelligence has played a key role in a wide variety of applications that require the analysis of a massive amount of data, including segmentation processes in medical imaging. In this context, artificial intelligence enables the improvement of image segmentation methods, moving towards the development of fully automated systems of analysis able to support pathologists in decision-making procedures. The aim of this review is to aid biologists and clinicians in discovering the most common segmentation open-source tools, including ImageJ (v. 1.54), CellProfiler (v. 4.2.5), Ilastik (v. 1.3.3) and QuPath (v. 0.4.3), along with their customized implementations. Additionally, the tools’ role in the histological imaging field is explored further, suggesting potential application workflows. In conclusion, this review encompasses an examination of the most commonly segmented tissues and their analysis through open-source deep and machine learning tools.

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Section: Discussionmentioning

confidence: 99%

Digital Pathology: A Comprehensive Review of Open-Source Histological Segmentation Software

Pavone,

Giannone,

Cabibi

et al. 2024

BioMedInformatics

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“…We conducted the inaugural quantitative IHC analysis of peri-implant hyperplastic tissue using QuPath ® , an open-source digital pathology image analysis program 47 . QuPath ® has been extensively used in diverse pathological investigations, elucidating complex immunopathological processes [48][49][50][51][52][53][54][55][56][57][58] , affirming its value in pathology research. The UPR involves three main signaling branches mediated by transmembrane proteins: inositol-requiring enzyme 1 (IRE1), PERK, and ATF6 59,60 .…”

Section: Discussionmentioning

confidence: 99%

Ultrastructural and immunohistochemical evaluation of hyperplastic soft tissues surrounding dental implants in fibular jaws

Mustakim,

Eo,

Seo

et al. 2024

Sci Rep

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In reconstructive surgery, complications post-fibula free flap (FFF) reconstruction, notably peri-implant hyperplasia, are significant yet understudied. This study analyzed peri-implant hyperplastic tissue surrounding FFF, alongside peri-implantitis and foreign body granulation (FBG) tissues from patients treated at the Department of Oral and Maxillofacial Surgery, Seoul National University Dental Hospital. Using light microscopy, pseudoepitheliomatous hyperplasia, anucleate and pyknotic prickle cells, and excessive collagen deposition were observed in FFF hyperplastic tissue. Ultrastructural analyses revealed abnormal structures, including hemidesmosome dilation, bacterial invasion, and endoplasmic reticulum (ER) swelling. In immunohistochemical analysis, unfolded protein-response markers ATF6, PERK, XBP1, inflammatory marker NFκB, necroptosis marker MLKL, apoptosis marker GADD153, autophagy marker LC3, epithelial–mesenchymal transition, and angiogenesis markers were expressed variably in hyperplastic tissue surrounding FFF implants, peri-implantitis, and FBG tissues. NFκB expression was higher in peri-implantitis and FBG tissues compared to hyperplastic tissue surrounding FFF implants. PERK expression exceeded XBP1 significantly in FFF hyperplastic tissue, while expression levels of PERK, XBP1, and ATF6 were not significantly different in peri-implantitis and FBG tissues. These findings provide valuable insights into the interconnected roles of ER stress, necroptosis, apoptosis, and angiogenesis in the pathogenesis of oral pathologies, offering a foundation for innovative strategies in dental implant rehabilitation management and prevention.

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“…Considering these observations and the accompanying increase in quality through segmentation, the performance of our models is comparable to that of human pathologists and thus suitable for real-world applications. Studies on interobserver variability among pathologists for different histological stains have reported varying levels of agreement 19 , 20 , indicating the need for standardized software tools in interpreting histological stains.…”

Section: Discussionmentioning

confidence: 99%

Semantic modeling of cell damage prediction: a machine learning approach at human-level performance in dermatology

Wagner

Springenberg

Kröger

et al. 2023

Sci Rep

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Machine learning is transforming the field of histopathology. Especially in classification related tasks, there have been many successful applications of deep learning already. Yet, in tasks that rely on regression and many niche applications, the domain lacks cohesive procedures that are adapted to the learning processes of neural networks. In this work, we investigate cell damage in whole slide images of the epidermis. A common way for pathologists to annotate a score, characterizing the degree of damage for these samples, is the ratio between healthy and unhealthy nuclei. The annotation procedure of these scores, however, is expensive and prone to be noisy among pathologists. We propose a new measure of damage, that is the total area of damage, relative to the total area of the epidermis. In this work, we present results of regression and segmentation models, predicting both scores on a curated and public dataset. We have acquired the dataset in collaborative efforts with medical professionals. Our study resulted in a comprehensive evaluation of the proposed damage metrics in the epidermis, with recommendations, emphasizing practical relevance for real world applications.

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QuPath Analysis for CD30+ Cutaneous T-Cell Lymphoma

Cited by 4 publications

References 24 publications

Digital Pathology: A Comprehensive Review of Open-Source Histological Segmentation Software

Digital Pathology: A Comprehensive Review of Open-Source Histological Segmentation Software

Ultrastructural and immunohistochemical evaluation of hyperplastic soft tissues surrounding dental implants in fibular jaws

Semantic modeling of cell damage prediction: a machine learning approach at human-level performance in dermatology

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