| 1. |
- Arora, Anmol, et al.
(author)
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The value of standards for health datasets in artificial intelligence-based applications
- 2023
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In: Nature Medicine. - : NATURE PORTFOLIO. - 1078-8956 .- 1546-170X. ; 29, s. 2929-2938
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Journal article (peer-reviewed)abstract
- Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. However, a growing body of evidence has highlighted the risk of algorithmic bias, which may perpetuate existing health inequity. This problem arises in part because of systemic inequalities in dataset curation, unequal opportunity to participate in research and inequalities of access. This study aims to explore existing standards, frameworks and best practices for ensuring adequate data diversity in health datasets. Exploring the body of existing literature and expert views is an important step towards the development of consensus-based guidelines. The study comprises two parts: a systematic review of existing standards, frameworks and best practices for healthcare datasets; and a survey and thematic analysis of stakeholder views of bias, health equity and best practices for artificial intelligence as a medical device. We found that the need for dataset diversity was well described in literature, and experts generally favored the development of a robust set of guidelines, but there were mixed views about how these could be implemented practically. The outputs of this study will be used to inform the development of standards for transparency of data diversity in health datasets (the STANDING Together initiative). A systematic review, combined with a stakeholder survey, presents an overview of current practices and recommendations for dataset curation in health, with specific focuses on data diversity and artificial intelligence-based applications.
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| 2. |
- Asa, Sylvia, et al.
(author)
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2020 vision of digital pathology in action
- 2019
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In: Journal of Pathology Informatics. - : Medknow Publications. - 2229-5089 .- 2153-3539. ; 10:27
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Journal article (other academic/artistic)
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| 3. |
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| 4. |
- Bivik Stadler, Caroline, 1986-, et al.
(author)
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Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training
- 2021
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In: Journal of digital imaging. - : Springer-Verlag New York. - 0897-1889 .- 1618-727X. ; 34, s. 105-115
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Journal article (peer-reviewed)abstract
- Artificial intelligence (AI) holds much promise for enabling highly desired imaging diagnostics improvements. One of the most limiting bottlenecks for the development of useful clinical-grade AI models is the lack of training data. One aspect is the large amount of cases needed and another is the necessity of high-quality ground truth annotation. The aim of the project was to establish and describe the construction of a database with substantial amounts of detail-annotated oncology imaging data from pathology and radiology. A specific objective was to be proactive, that is, to support undefined subsequent AI training across a wide range of tasks, such as detection, quantification, segmentation, and classification, which puts particular focus on the quality and generality of the annotations. The main outcome of this project was the database as such, with a collection of labeled image data from breast, ovary, skin, colon, skeleton, and liver. In addition, this effort also served as an exploration of best practices for further scalability of high-quality image collections, and a main contribution of the study was generic lessons learned regarding how to successfully organize efforts to construct medical imaging databases for AI training, summarized as eight guiding principles covering team, process, and execution aspects.
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| 5. |
- Bodén, Anna, et al.
(author)
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The human-in-the-loop : an evaluation of pathologists interaction with artificial intelligence in clinical practice
- 2021
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In: Histopathology. - : Wiley-Blackwell. - 0309-0167 .- 1365-2559. ; 79:2, s. 210-218
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Journal article (peer-reviewed)abstract
- Aims: One of the major drivers of the adoption of digital pathology in clinical practice is the possibility of introducing digital image analysis (DIA) to assist with diagnostic tasks. This offers potential increases in accuracy, reproducibility, and efficiency. Whereas stand-alone DIA has great potential benefit for research, little is known about the effect of DIA assistance in clinical use. The aim of this study was to investigate the clinical use characteristics of a DIA application for Ki67 proliferation assessment. Specifically, the human-in-the-loop interplay between DIA and pathologists was studied. Methods and results: We retrospectively investigated breast cancer Ki67 areas assessed with human-in-the-loop DIA and compared them with visual and automatic approaches. The results, expressed as standard deviation of the error in the Ki67 index, showed that visual estimation (eyeballing) (14.9 percentage points) performed significantly worse (P < 0.05) than DIA alone (7.2 percentage points) and DIA with human-in-the-loop corrections (6.9 percentage points). At the overall level, no improvement resulting from the addition of human-in-the-loop corrections to the automatic DIA results could be seen. For individual cases, however, human-in-the-loop corrections could address major DIA errors in terms of poor thresholding of faint staining and incorrect tumour-stroma separation. Conclusion: The findings indicate that the primary value of human-in-the-loop corrections is to address major weaknesses of a DIA application, rather than fine-tuning the DIA quantifications.
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| 6. |
- Broad, A., et al.
(author)
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Attention-guided sampling for colorectal cancer analysis with digital pathology
- 2022
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In: Journal of Pathology Informatics. - : Elsevier B.V.. - 2229-5089 .- 2153-3539. ; 13
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Journal article (peer-reviewed)abstract
- Improvements to patient care through the development of automated image analysis in pathology are restricted by the small image patch size that can be processed by convolutional neural networks (CNNs), when compared to the whole-slide image (WSI). Tile-by-tile processing across the entire WSI is slow and inefficient. While this may improve with future computing power, the technique remains vulnerable to noise from uninformative image areas. We propose a novel attention-inspired algorithm that selects image patches from informative parts of the WSI, first using a sparse randomised grid pattern, then iteratively re-sampling at higher density in regions where a CNN classifies patches as tumour. Subsequent uniform sampling across the enclosing region of interest (ROI) is used to mitigate sampling bias. Benchmarking tests informed the adoption of VGG19 as the main CNN architecture, with 79% classification accuracy. A further CNN was trained to separate false-positive normal epithelium from tumour epithelium, in a novel adaptation of a two-stage model used in brain imaging. These subsystems were combined in a processing pipeline to generate spatial distributions of classified patches from unseen WSIs. The ROI was predicted with a mean F1 (Dice) score of 86.6% over 100 evaluation WSIs. Several algorithms for evaluating tumour–stroma ratio (TSR) within the ROI were compared, giving a lowest root mean square (RMS) error of 11.3% relative to pathologists’ annotations, against 13.5% for an equivalent tile-by-tile pipeline. Our pipeline processed WSIs between 3.3x and 6.3x faster than tile-by-tile processing. We propose our attention-based sampling pipeline as a useful tool for pathology researchers, with the further potential for incorporating additional diagnostic calculations. © 2022 The Authors
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| 7. |
- Capitanio, Arrigo, et al.
(author)
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Digital cytology: A short review of technical and methodological approaches and applications
- 2018
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In: Cytopathology. - : WILEY. - 0956-5507 .- 1365-2303. ; 29:4, s. 317-325
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Research review (peer-reviewed)abstract
- The recent years have been characterised by a rapid development of whole slide imaging (WSI) especially in its applications to histology. The application of WSI technology to cytology is less common because of technological problems related to the three-dimensional nature of cytology preparations (which requires capturing of z-stack information, with an increase in file size and usability issues in viewing cytological preparations). The aim of this study is to provide a review of the literature on the use of digital cytology and provide an overview of cytological applications of WSI in current practice as well as identifying areas for future development.
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| 8. |
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| 9. |
- Clarke, Emily L., et al.
(author)
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Development and Evaluation of a Novel Point-of-Use Quality Assurance Tool for Digital Pathology
- 2019
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In: Archives of Pathology & Laboratory Medicine. - : COLL AMER PATHOLOGISTS. - 0003-9985 .- 1543-2165. ; 143:10, s. 1246-1255
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Journal article (peer-reviewed)abstract
- Context.-Flexible working at diverse or remote sites is a major advantage when reporting using digital pathology, but currently there is no method to validate the clinical diagnostic setting within digital microscopy. Objective.-To develop a preliminary Point-of-Use Quality Assurance (POUQA) tool designed specifically to validate the diagnostic setting for digital microscopy. Design.-We based the POUQA tool on the red, green, and blue (RGB) values of hematoxylin-eosin. The tool used 144 hematoxylin-eosin-colored, 5x5-cm patches with a superimposed random letter with subtly lighter RGB values from the background color, with differing levels of difficulty. We performed an initial evaluation across 3 phases within 2 pathology departments: 1 in the United Kingdom and 1 in Sweden. Results.-In total, 53 experiments were conducted across all phases resulting in 7632 test images viewed in all. Results indicated that the display, the users visual system, and the environment each independently impacted performance. Performance was improved with reduction in natural light and through use of medical-grade displays. Conclusions.-The use of a POUQA tool for digital microscopy is essential to afford flexible working while ensuring patient safety. The color-contrast test provides a standardized method of comparing diagnostic settings for digital microscopy. With further planned development, the color-contrast test may be used to create a "Verified Login" for diagnostic setting validation.
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| 10. |
- Clarke, Emily L., et al.
(author)
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Development of a novel tissue-mimicking color calibration slide for digital microscopy
- 2018
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In: Color Research and Application. - : WILEY. - 0361-2317 .- 1520-6378. ; 43:2, s. 184-197
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Journal article (peer-reviewed)abstract
- Digital microscopy produces high resolution digital images of pathology slides. Because no acceptable and effective control of color reproduction exists in this domain, there is significant variability in color reproduction of whole slide images. Guidance from international bodies and regulators highlights the need for color standardization. To address this issue, we systematically measured and analyzed the spectra of histopathological stains. This information was used to design a unique color calibration slide utilizing real stains and a tissue-like substrate, which can be stained to produce the same spectral response as tissue. By closely mimicking the colors in stained tissue, our target can provide more accurate color representation than film-based targets, whilst avoiding the known limitations of using actual tissue. The application of the color calibration slide in the clinical setting was assessed by conducting a pilot user-evaluation experiment with promising results. With the imminent integration of digital pathology into the routine work of the diagnostic pathologist, it is hoped that this color calibration slide will help provide a universal color standard for digital microscopy thereby ensuring better and safer healthcare delivery.
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| 11. |
- Clarke, Emily L., et al.
(author)
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Display evaluation for primary diagnosis using digital pathology
- 2020
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In: Journal of Medical Imaging. - : SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS. - 2329-4302 .- 2329-4310. ; 7:2
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Journal article (peer-reviewed)abstract
- Purpose: As pathology departments around the world contemplate digital microscopy for primary diagnosis, making an informed choice regarding display procurement is very challenging in the absence of defined minimum standards. In order to help inform the decision, we aimed to conduct an evaluation of displays with a range of technical specifications and sizes. Approach: We invited histopathologists within our institution to take part in a survey evaluation of eight short-listed displays. Pathologists reviewed a single haematoxylin and eosin whole slide image of a benign nevus on each display and gave a single score to indicate their preference in terms of image quality and size of the display. Results: Thirty-four pathologists took part in the display evaluation experiment. The preferred display was the largest and had the highest technical specifications (11.8-MP resolution, 2100 cd/m(2) maximum luminance). The least preferred display had the lowest technical specifications (2.3-MP resolution, 300 cd/m(2) maximum luminance). A trend was observed toward an increased preference for displays with increased luminance and resolution. Conclusions: This experiment demonstrates a preference for large medical-grade displays with the high luminance and high resolution. As cost becomes implicated in procurement, significantly less expensive medical-grade displays with slightly lower technical specifications may be the most cost-effective option. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)
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| 12. |
- Clarke, Emily L., et al.
(author)
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Image analysis of cutaneous melanoma histology: a systematic review and meta-analysis
- 2023
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In: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 13:1
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Journal article (peer-reviewed)abstract
- The current subjective histopathological assessment of cutaneous melanoma is challenging. The application of image analysis algorithms to histological images may facilitate improvements in workflow and prognostication. To date, several individual algorithms applied to melanoma histological images have been reported with variations in approach and reported accuracies. Histological digital images can be created using a camera mounted on a light microscope, or through whole slide image (WSI) generation using a whole slide scanner. Before any such tool could be integrated into clinical workflow, the accuracy of the technology should be carefully evaluated and summarised. Therefore, the objective of this review was to evaluate the accuracy of existing image analysis algorithms applied to digital histological images of cutaneous melanoma.Database searching of PubMed and Embase from inception to 11th March 2022 was conducted alongside citation checking and examining reports from organisations. All studies reporting accuracy of any image analysis applied to histological images of cutaneous melanoma, were included. The reference standard was any histological assessment of haematoxylin and eosin-stained slides and/or immunohistochemical staining. Citations were independently deduplicated and screened by two review authors and disagreements were resolved through discussion. The data was extracted concerning study demographics; type of image analysis; type of reference standard; conditions included and test statistics to construct 2 x 2 tables. Data was extracted in accordance with our protocol and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Diagnostic Test Accuracy (PRISMA-DTA) Statement. A bivariate random-effects meta-analysis was used to estimate summary sensitivities and specificities with 95% confidence intervals (CI). Assessment of methodological quality was conducted using a tailored version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The primary outcome was the pooled sensitivity and specificity of image analysis applied to cutaneous melanoma histological images. Sixteen studies were included in the systematic review, representing 4,888 specimens. Six studies were included in the meta-analysis. The mean sensitivity and specificity of automated image analysis algorithms applied to melanoma histological images was 90% (CI 82%, 95%) and 92% (CI 79%, 97%), respectively. Based on limited and heterogeneous data, image analysis appears to offer high accuracy when applied to histological images of cutaneous melanoma. However, given the early exploratory nature of these studies, further development work is necessary to improve their performance.
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| 13. |
- Dessauvagie, Benjamin F., et al.
(author)
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Interobserver variation in the diagnosis of fibroepithelial lesions of the breast: a multicentre audit by digital pathology
- 2018
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In: Journal of Clinical Pathology. - : BMJ PUBLISHING GROUP. - 0021-9746 .- 1472-4146. ; 71:8, s. 672-679
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Journal article (peer-reviewed)abstract
- Aim Fibroepithelial lesions (FELs) of the breast span a morphological continuum including lesions where distinction between cellular fibroadenoma (FA) and benign phyllodes tumour (PT) is difficult. The distinction is clinically important with FAs managed conservatively while equivocal lesions and PTs are managed with surgery. We sought to audit core biopsy diagnoses of equivocal FELs by digital pathology and to investigate whether digital point counting is useful in clarifying FEL diagnoses. Method Scanned slide images from cores and subsequent excisions of 69 equivocal FELs were examined in a multicentre audit by eight pathologists to determine the agreement and accuracy of core needle biopsy (CNB) diagnoses and by digital point counting of stromal cellularity and expansion to determine if classification could be improved. Results Interobserver variation was high on CNB with a unanimous diagnosis from all pathologists in only eight cases of FA, diagnoses of both FA and PT on the same CNB in 15 and a weak mean kappa agreement between pathologists (k=0.36). Moderate agreement was observed on CNBs among breast specialists (k=0.44) and on excision samples (k=0.49). Up to 23% of lesions confidently diagnosed as FA on CNB were PT on excision and up to 30% of lesions confidently diagnosed as PT on CNB were FA on excision. Digital point counting did not aid in the classification of FELs. Conclusion Accurate and reproducible diagnosis of equivocal FELs is difficult, particularly on CNB, resulting in poor interobserver agreement and suboptimal accuracy. Given the diagnostic difficulty, and surgical implications, equivocal FELs should be reported in consultation with experienced breast pathologists as a small number of benign FAs can be selected out from equivocal lesions.
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| 14. |
- Dunn, Catriona, et al.
(author)
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Quantitative assessment of H&E staining for pathology: development and clinical evaluation of a novel system
- 2024
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In: Diagnostic Pathology. - : BMC. - 1746-1596. ; 19:1
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Journal article (peer-reviewed)abstract
- BackgroundStaining tissue samples to visualise cellular detail and tissue structure is at the core of pathology diagnosis, but variations in staining can result in significantly different appearances of the tissue sample. While the human visual system is adept at compensating for stain variation, with the growth of digital imaging in pathology, the impact of this variation can be more profound. Despite the ubiquity of haematoxylin and eosin staining in clinical practice worldwide, objective quantification is not yet available. We propose a method for quantitative haematoxylin and eosin stain assessment to facilitate quality assurance of histopathology staining, enabling truly quantitative quality control and improved standardisation.MethodsThe stain quantification method comprises conventional microscope slides with a stain-responsive biopolymer film affixed to one side, called stain assessment slides. The stain assessment slides were characterised with haematoxylin and eosin, and implemented in one clinical laboratory to quantify variation levels.ResultsStain assessment slide stain uptake increased linearly with duration of haematoxylin and eosin staining (r = 0.99), and demonstrated linearly comparable staining to samples of human liver tissue (r values 0.98-0.99). Laboratory implementation of this technique quantified intra- and inter-instrument variation of staining instruments at one point in time and across a five-day period.ConclusionThe proposed method has been shown to reliably quantify stain uptake, providing an effective laboratory quality control method for stain variation. This is especially important for whole slide imaging and the future development of artificial intelligence in digital pathology.
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| 15. |
- Elsharif, Mohamed, et al.
(author)
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Hepatectomy risk assessment with functional magnetic resonance imaging (HEPARIM)
- 2021
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In: BMC Cancer. - : BMC. - 1471-2407. ; 21:1
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Journal article (peer-reviewed)abstract
- Background: Post hepatectomy liver failure (PHLF) remains a significant risk in patients undergoing curative liver resection for cancer, however currently available PHLF risk prediction investigations are not sufficiently accurate. The Hepatectomy risk assessment with functional magnetic resonance imaging trial (HEPARIM) aims to establish if quantitative MRI biomarkers of liver function & perfusion can be used to more accurately predict PHLF risk and FLR function, measured against indocyanine green (ICG) liver function test. Methods: HEPARIM is an observational cohort study recruiting patients undergoing liver resection of 2 segments or more, prior to surgery patients will have both Dynamic Gadoxetate-enhanced (DGE) liver MRI and ICG testing. Day one post op ICG testing is repeated and R15 compared to the Gadoxetate Clearance (GC) of the future liver remnant (FLR-GC) as measure by preoperative DGE- MRI which is the primary outcome, and preoperative ICG R15 compared to GC of whole liver (WL-GC) as a secondary outcome. Data will be collected from medical records, biochemistry, pathology and radiology reports and used in a multi-variate analysis to the value of functional MRI and derive multivariant prediction models for future validation. Discussion: If successful, this test will potentially provide an efficient means to quantitatively assess FLR function and PHLF risk enabling surgeons to push boundaries of liver surgery further while maintaining safe practice and thereby offering chance of cure to patients who would previously been deemed inoperable. MRI has the added benefit of already being part of the routine diagnostic pathway and as such would have limited additional burden on patients time or cost to health care systems. (Hepatectomy Risk Assessment With Functional Magnetic Resonance Imaging - Full Text View -, n.d.)
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| 16. |
- Falk, Martin, Dr.rer.nat. 1981-, et al.
(author)
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Interactive Visualization of 3D Histopathology in Native Resolution
- 2019
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In: IEEE Transactions on Visualization and Computer Graphics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1077-2626 .- 1941-0506 .- 2160-9306. ; 25:1, s. 1008-1017
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Journal article (peer-reviewed)abstract
- We present a visualization application that enables effective interactive visual analysis of large-scale 3D histopathology, that is, high-resolution 3D microscopy data of human tissue. Clinical work flows and research based on pathology have, until now, largely been dominated by 2D imaging. As we will show in the paper, studying volumetric histology data will open up novel and useful opportunities for both research and clinical practice. Our starting point is the current lack of appropriate visualization tools in histopathology, which has been a limiting factor in the uptake of digital pathology. Visualization of 3D histology data does pose difficult challenges in several aspects. The full-color datasets are dense and large in scale, on the order of 100,000 x 100,000 x 100 voxels. This entails serious demands on both rendering performance and user experience design. Despite this, our developed application supports interactive study of 3D histology datasets at native resolution. Our application is based on tailoring and tuning of existing methods, system integration work, as well as a careful study of domain specific demands emanating from a close participatory design process with domain experts as team members. Results from a user evaluation employing the tool demonstrate a strong agreement among the 14 participating pathologists that 3D histopathology will be a valuable and enabling tool for their work.
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| 17. |
- Falk, Martin, et al.
(author)
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Transfer Function Design Toolbox for Full-Color Volume Datasets
- 2017
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In: 2017 IEEE PACIFIC VISUALIZATION SYMPOSIUM (PACIFICVIS), IEEE. - : IEEE. - 9781509057382 ; , s. 171-179
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Conference paper (peer-reviewed)abstract
- In this paper, we tackle the challenge of effective Transfer Function (TF) design for Direct Volume Rendering (DVR) of full-color datasets. We propose a novel TF design toolbox based on color similarity which is used to adjust opacity as well as replacing colors. We show that both CIE L*u*v* chromaticity and the chroma component of YCbCr are equally suited as underlying color space for the TF widgets. In order to maximize the area utilized in the TF editor, we renormalize the color space based on the histogram of the dataset. Thereby, colors representing a higher share of the dataset are depicted more prominently, thus providing a higher sensitivity for fine-tuning TF widgets. The applicability of our TF design toolbox is demonstrated by volume ray casting challenging full-color volume data including the visible male cryosection dataset and examples from 3D histology.
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| 18. |
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| 19. |
- Godson, Lucy, et al.
(author)
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Immune subtyping of melanoma whole slide images using multiple instance learning
- 2024
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In: Medical Image Analysis. - : ELSEVIER. - 1361-8415 .- 1361-8423. ; 93
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Journal article (peer-reviewed)abstract
- Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies have used tumour transcriptome data to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential predictive biomarkers. However, acquiring transcriptome data is a time-consuming and costly process. Moreover, it is not routinely used in the current clinical workflow. Here, we attempt to overcome this by developing deep learning models to classify gigapixel haematoxylin and eosin (H&E) stained pathology slides, which are well established in clinical workflows, into these immune subgroups. We systematically assess six different multiple instance learning (MIL) frameworks, using five different image resolutions and three different feature extraction methods. We show that pathology-specific self-supervised models using 10x resolution patches generate superior representations for the classification of immune subtypes. In addition, in a primary melanoma dataset, we achieve a mean area under the receiver operating characteristic curve (AUC) of 0.80 for classifying histopathology images into 'high' or 'low immune' subgroups and a mean AUC of 0.82 in an independent TCGA melanoma dataset. Furthermore, we show that these models are able to stratify patients into 'high' and 'low immune' subgroups with significantly different melanoma specific survival outcomes (log rank test, P < 0.005). We anticipate that MIL methods will allow us to find new biomarkers of high importance, act as a tool for clinicians to infer the immune landscape of tumours and stratify patients, without needing to carry out additional expensive genetic tests.
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| 20. |
- Haddad, Tariq Sami, et al.
(author)
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Improving tumor budding reporting in colorectal cancer: a Delphi consensus study
- 2021
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In: Virchows Archiv. - : SPRINGER. - 0945-6317 .- 1432-2307. ; 479:3, s. 459-469
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Journal article (peer-reviewed)abstract
- Tumor budding is a long-established independent adverse prognostic marker in colorectal cancer, yet methods for its assessment have varied widely. In an effort to standardize its reporting, a group of experts met in Bern, Switzerland, in 2016 to reach consensus on a single, international, evidence-based method for tumor budding assessment and reporting (International Tumor Budding Consensus Conference [ITBCC]). Tumor budding assessment using the ITBCC criteria has been validated in large cohorts of cancer patients and incorporated into several international colorectal cancer pathology and clinical guidelines. With the wider reporting of tumor budding, new issues have emerged that require further clarification. To better inform researchers and health-care professionals on these issues, an international group of experts in gastrointestinal pathology participated in a modified Delphi process to generate consensus and highlight areas requiring further research. This effort serves to re-affirm the importance of tumor budding in colorectal cancer and support its continued use in routine clinical practice.
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| 21. |
- Haddad, Tariq Sami, et al.
(author)
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Tutorial: methods for three-dimensional visualization of archival tissue material
- 2021
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In: Nature Protocols. - : NATURE PORTFOLIO. - 1754-2189 .- 1750-2799. ; 16:11, s. 4945-4962
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Research review (peer-reviewed)abstract
- The authors describe three-dimensional imaging pipelines available to analyze archival patient specimens. The pipelines facilitate the visualization of both large and small volumes of tissue with subcellular resolution. Analysis of three-dimensional patient specimens is gaining increasing relevance for understanding the principles of tissue structure as well as the biology and mechanisms underlying disease. New technologies are improving our ability to visualize large volume of tissues with subcellular resolution. One resource often overlooked is archival tissue maintained for decades in hospitals and research archives around the world. Accessing the wealth of information stored within these samples requires the use of appropriate methods. This tutorial introduces the range of sample preparation and microscopy approaches available for three-dimensional visualization of archival tissue. We summarize key aspects of the relevant techniques and common issues encountered when using archival tissue, including registration and antibody penetration. We also discuss analysis pipelines required to process, visualize and analyze the data and criteria to guide decision-making. The methods outlined in this tutorial provide an important and sustainable avenue for validating three-dimensional tissue organization and mechanisms of disease.
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| 22. |
- Homeyer, Andre, et al.
(author)
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Automated quantification of steatosis: agreement with stereological point counting
- 2017
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In: Diagnostic Pathology. - : BIOMED CENTRAL LTD. - 1746-1596. ; 12
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Journal article (peer-reviewed)abstract
- Background: Steatosis is routinely assessed histologically in clinical practice and research. Automated image analysis can reduce the effort of quantifying steatosis. Since reproducibility is essential for practical use, we have evaluated different analysis methods in terms of their agreement with stereological point counting (SPC) performed by a hepatologist. Methods: The evaluation was based on a large and representative data set of 970 histological images from human patients with different liver diseases. Three of the evaluated methods were built on previously published approaches. One method incorporated a new approach to improve the robustness to image variability. Results: The new method showed the strongest agreement with the expert. At 20x resolution, it reproduced steatosis area fractions with a mean absolute error of 0.011 for absent or mild steatosis and 0.036 for moderate or severe steatosis. At 10x resolution, it was more accurate than and twice as fast as all other methods at 20x resolution. When compared with SPC performed by two additional human observers, its error was substantially lower than one and only slightly above the other observer. Conclusions: The results suggest that the new method can be a suitable automated replacement for SPC. Before further improvements can be verified, it is necessary to thoroughly assess the variability of SPC between human observers.
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| 23. |
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| 24. |
- Jarkman, Sofia, et al.
(author)
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Generalization of Deep Learning in Digital Pathology : Experience in Breast Cancer Metastasis Detection
- 2022
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In: Cancers. - : MDPI. - 2072-6694. ; 14:21
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Journal article (peer-reviewed)abstract
- Simple Summary Pathology is a cornerstone in cancer diagnostics, and digital pathology and artificial intelligence-driven image analysis could potentially save time and enhance diagnostic accuracy. For clinical implementation of artificial intelligence, a major question is whether the computer models maintain high performance when applied to new settings. We tested the generalizability of a highly accurate deep learning model for breast cancer metastasis detection in sentinel lymph nodes from, firstly, unseen sentinel node data and, secondly, data with a small change in surgical indication, in this case lymph nodes from axillary dissections. Model performance dropped in both settings, particularly on axillary dissection nodes. Retraining of the model was needed to mitigate the performance drop. The study highlights the generalization challenge of clinical implementation of AI models, and the possibility that retraining might be necessary. Poor generalizability is a major barrier to clinical implementation of artificial intelligence in digital pathology. The aim of this study was to test the generalizability of a pretrained deep learning model to a new diagnostic setting and to a small change in surgical indication. A deep learning model for breast cancer metastases detection in sentinel lymph nodes, trained on CAMELYON multicenter data, was used as a base model, and achieved an AUC of 0.969 (95% CI 0.926-0.998) and FROC of 0.838 (95% CI 0.757-0.913) on CAMELYON16 test data. On local sentinel node data, the base model performance dropped to AUC 0.929 (95% CI 0.800-0.998) and FROC 0.744 (95% CI 0.566-0.912). On data with a change in surgical indication (axillary dissections) the base model performance indicated an even larger drop with a FROC of 0.503 (95%CI 0.201-0.911). The model was retrained with addition of local data, resulting in about a 4% increase for both AUC and FROC for sentinel nodes, and an increase of 11% in AUC and 49% in FROC for axillary nodes. Pathologist qualitative evaluation of the retrained model s output showed no missed positive slides. False positives, false negatives and one previously undetected micro-metastasis were observed. The study highlights the generalization challenge even when using a multicenter trained model, and that a small change in indication can considerably impact the model s performance.
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| 25. |
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