Three-dimensional histopathological reconstruction of bladder tumours

Jansen, Ilaria; Lucas, Marit; Savcı-Heijink, C. Dilara; Meijer, Sybren L.; Liem, Esmée I. M. L.; Boer, Onno J. de; Leeuwen, Ton G. van; Marquering, Henk A.; Bruin, Daniël M. de

doi:10.1186/s13000-019-0803-7

Cited by 22 publications

(24 citation statements)

References 29 publications

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“…Furthermore, as each stain might provide unique information, the algorithms will need to be generated and tested on all stains before current practice can be changed to limit AI applications to a single stain. Finally, as the spatial distribution of different pathological structures is uniquely important to renal diagnostics, future applications in 3D WSI reconstruction 115,116 will facilitate the accurate assessment of histological primitives (for example, glomerular number in a biopsy sample) and the spatial interplay between pathological features (for example, interstitial fibrosis and inflammation) and associated molecular signatures.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Despite the excitement about these new approaches, pathologists and data scientists are advocating for cautious scepticism in the interpretation of quantitative results derived from the application of image analysis techniques [115][116][117][118][119] . Similar caution is warranted when considering the potential clinical implementation of such approaches, recognizing that vigorous validation should always be an integral step of the deployment process [126][127][128][129][130] .…”

Section: Image Microarraymentioning

confidence: 99%

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Digital pathology and computational image analysis in nephropathology

et al. 2020

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The emergence of digital pathology-an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis-is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Image Microarraymentioning

confidence: 99%

Digital pathology and computational image analysis in nephropathology

et al. 2020

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“…Holmium and thulium laser ERBT eliminate the risk of obturator nerve reflex during the procedure [22,29,33,44]. There is however a risk of residual disease and understaging when we use HybridKnife (hydrodissection) for ERBT due to its nature of submucosal elevation [35,53].…”

Section: Different Modalities Of Erbtmentioning

confidence: 99%

An International Collaborative Consensus Statement on En Bloc Resection of Bladder Tumour Incorporating Two Systematic Reviews, a Two-round Delphi Survey, and a Consensus Meeting

et al. 2020

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BackgroundThere has been increasing interest in en bloc resection of bladder tumour (ERBT) as an oncologically non-inferior alternative to transurethral resection of bladder tumour (TURBT) with fewer complications and better histology specimens. However, there is a lack of robust randomised controlled trial (RCT) data for making recommendations. ObjectiveWe aimed to develop a consensus statement to standardise various aspects of ERBT for clinical practice and to guide future research. Design, Setting and ParticipantsWe developed the consensus statement on ERBT using a modified Delphi method.First, two systematic reviews were performed to investigate the clinical effectiveness of ERBT versus TURBT (effectiveness review), and to identify areas of uncertainty in ERBT (uncertainties review). Next, 200 health care professionals (urologists, oncologists and pathologists) with experience in ERBT were invited to complete a two-round Delphi survey. Finally, a 16-member consensus panel meeting was held to review, discuss and re-vote on the statements as appropriate. Outcome Measurements and Statistical AnalysisMeta-analyses were performed for RCT data in the effectiveness review. Consensus statements were developed from the uncertainties review. Consensus was defined as:(1) ≥70% scoring a statement 7-9 AND ≤15% scoring the statement 1-3 (consensus agree); OR (2) ≥70% scoring a statement 1-3 AND ≤15% scoring the statement 7-9 (consensus disagree). Results and LimitationsA total of 10 RCTs were identified upon systematic review. ERBT had a shorter irrigation time (mean difference -7.24 hours, 95% CI -9.29 --5.20, I 2 =85%, p<0.001) and lower rate of bladder perforation (Risk ratio [RR] 0.30, 95% CI 0.11-0.83, I 2 =1%, p=0.02) than TURBT, both with moderate certainty of evidence. There were no significant differences in recurrences at 0-12 months, 13-24 months or 25-36 months (all very low certainty of evidence). A total of 103 statements were developed and 99

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“…However, limitations persist. One of the major problems encountered in 3D image reconstruction of histology slides is tissue deformation due to the preparation steps, such as sectioning, slice thickness, staining, and mounting, because the quality of the digital images depends on the quality of the original slide [59][60][61][62][63]66,68]. Additionally, it is inevitably difficult to correctly align them with adjacent sections to reconstruct a 3D image.…”

Section: D Models Of Multiple Histological Sectionsmentioning

confidence: 99%

X-ray dark-field phase-contrast imaging: Origins of the concept to practical implementation and applications

et al. 2020

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The basic idea of X-ray dark-field imaging (XDFI), first presented in 2000, was based on the concepts used in an X-ray interferometer. In this article, we review 20 years of developments in our theoretical understanding, scientific instrumentation, and experimental demonstration of XDFI and its applications to medical imaging. We first describe the concepts underlying XDFI that are responsible for imparting phase contrast information in projection X-ray images. We then review the algorithms that can convert these projection phase images into three-dimensional tomographic slices. Various implementations of computed tomography reconstructions algorithms for XDFI data are discussed. The next four sections describe and illustrate potential applications of XDFI in pathology, musculoskeletal imaging, oncologic imaging, and neuroimaging. The sample applications that are presented illustrate potential use scenarios for XDFI in histopathology and other clinical applications. Finally, the last section presents future perspectives and potential technical developments that can make XDFI an even more powerful tool.

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Three-dimensional histopathological reconstruction of bladder tumours

Cited by 22 publications

References 29 publications

Digital pathology and computational image analysis in nephropathology

Digital pathology and computational image analysis in nephropathology

An International Collaborative Consensus Statement on En Bloc Resection of Bladder Tumour Incorporating Two Systematic Reviews, a Two-round Delphi Survey, and a Consensus Meeting

X-ray dark-field phase-contrast imaging: Origins of the concept to practical implementation and applications

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