Histopathology: ditch the slides, because digital and 3D are on show.
Ontology highlight
ABSTRACT:
SUBMITTER: Jansen I
PROVIDER: S-EPMC5871638 | biostudies-literature | 2018 Apr
REPOSITORIES: biostudies-literature
Publications
Histopathology: ditch the slides, because digital and 3D are on show.
Jansen Ilaria I Lucas Marit M Savci-Heijink C Dilara CD Meijer Sybren L SL Marquering Henk A HA de Bruin Daniel M DM Zondervan Patricia J PJ
World journal of urology 20180202 4
Due to the growing field of digital pathology, more and more digital histology slides are becoming available. This improves the accessibility, allows teleconsultations from specialized pathologists, improves education, and might give urologist the possibility to review the slides in patient management systems. Moreover, by stacking multiple two-dimensional (2D) digital slides, three-dimensional volumes can be created, allowing improved insight in the growth pattern of a tumor. With the addition ...[more]
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Project description:PURPOSE:Digital pathology (DP), referring to the digitization of tissue slides, is beginning to change the landscape of clinical diagnostic workflows and has engendered active research within the area of computational pathology. One of the challenges in DP is the presence of artefacts and batch effects, unintentionally introduced during both routine slide preparation (eg, staining, tissue folding) and digitization (eg, blurriness, variations in contrast and hue). Manual review of glass and digital slides is laborious, qualitative, and subject to intra- and inter-reader variability. Therefore, there is a critical need for a reproducible automated approach of precisely localizing artefacts to identify slides that need to be reproduced or regions that should be avoided during computational analysis. METHODS:Here we present HistoQC, a tool for rapidly performing quality control to not only identify and delineate artefacts but also discover cohort-level outliers (eg, slides stained darker or lighter than others in the cohort). This open-source tool employs a combination of image metrics (eg, color histograms, brightness, contrast), features (eg, edge detectors), and supervised classifiers (eg, pen detection) to identify artefact-free regions on digitized slides. These regions and metrics are presented to the user via an interactive graphical user interface, facilitating artefact detection through real-time visualization and filtering. These same metrics afford users the opportunity to explicitly define acceptable tolerances for their workflows. RESULTS:The output of HistoQC on 450 slides from The Cancer Genome Atlas was reviewed by two pathologists and found to be suitable for computational analysis more than 95% of the time. CONCLUSION:These results suggest that HistoQC could provide an automated, quantifiable, quality control process for identifying artefacts and measuring slide quality, in turn helping to improve both the repeatability and robustness of DP workflows.
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Project description:The residual cancer burden index is an important quantitative measure used for assessing treatment response following neoadjuvant therapy for breast cancer. It has shown to be predictive of overall survival and is composed of two key metrics: qualitative assessment of lymph nodes and the percentage of invasive or in situ tumour cellularity (TC) in the tumour bed (TB). Currently, TC is assessed through eye-balling of routine histopathology slides estimating the proportion of tumour cells within the TB. With the advances in production of digitized slides and increasing availability of slide scanners in pathology laboratories, there is potential to measure TC using automated algorithms with greater precision and accuracy. We describe two methods for automated TC scoring: 1) a traditional approach to image analysis development whereby we mimic the pathologists' workflow, and 2) a recent development in artificial intelligence in which features are learned automatically in deep neural networks using image data alone. We show strong agreements between automated and manual analysis of digital slides. Agreements between our trained deep neural networks and experts in this study (0.82) approach the inter-rater agreements between pathologists (0.89). We also reveal properties that are captured when we apply deep neural network to whole slide images, and discuss the potential of using such visualisations to improve upon TC assessment in the future.
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Project description:AIMS:In brain research, the histopathological examination of coronar whole-brain slides provides important insights into spatial disease characteristics. Regarding brain tumor research, this enables visualization of tumor heterogeneity, infiltration patterns and the relationship with the surrounding brain parenchyma. The precise correlation between radiological imaging and post-mortem brains is of special interest. NEW METHOD:We developed a wide-field slide scanner, comprising a microscope, a high-precision remotely controllable x-y-stage, a camera and a computer workstation, for automatically scanning uncommonly large formats. We analyzed whole brain slides of three patients and constructed cellularity heatmaps and fiber tract maps using a custom-made image processing pipeline. RESULTS:The obtained cellularity heatmaps allow for distinguishing compact tumor (5714?±?1786 cells/mm², mean?±?standard deviation) from white matter (3581?±?828 cells/mm²) and grey matter (2473?±?716 cells/mm²). Compared to magnetic resonance imaging, the proposed histopathological work-up (i) reveals a larger zone of tumor infiltration around the compact tumor areas and (ii) shows how pre-existing tracts are displaced by the tumor bulk. Moreover, we highlight differences in the histological tumor growth pattern of two different radiological progression subtypes. COMPARISON WITH EXISTING METHOD(S):Compared to existing (commercial) solutions, our slide scanning solution is adaptable and cost-efficient. Moreover, we showcase potential clinical applications by mapping whole brain histology to magnetic resonance imaging. CONCLUSIONS:We herein provide instructions on how to (i) construct a custom-built slide scanner capable of scanning arbitrary slide formats, (ii) automatically evaluate the cell density and (iii) perform fiber tracking on whole brain slides.
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Project description:Purpose To correlate multiparametric magnetic resonance (MR) imaging and quantitative digital histopathologic analysis (DHA) of the prostate. Materials and Methods This retrospective study was approved by the local institutional review board and was HIPAA compliant. Forty patients (median age, 60 years; age range, 44-71 years) who underwent prostate MR imaging consisting of T2-weighted and diffusion-weighted (DW) MR imaging along with subsequent robot-assisted radical prostatectomy gave informed consent to be included. Whole-mount tissue specimens were obtained with a patient-specific mold, and DHA was performed to assess the lumen, epithelium, stroma, and epithelial nucleus. These DHA images were registered with MR images and were correlated on a per-voxel basis. The relationship between MR imaging and DHA was assessed by using a linear mixed-effects model and the Pearson correlation coefficient. Results T2-weighted MR imaging, apparent diffusion coefficient (ADC) of DW imaging, and high-b-value DW imaging were significantly related to specific DHA parameters (P < .01). For instance, lumen density (ie, the percentage area of tissue components) was associated with T2-weighted MR imaging (slope = 0.36 ± 0.05 [standard error], ? = 0.35), ADC (slope = 0.47 ± 0.05, ? = 0.50), and high-b-value DW imaging (slope = -0.44 ± 0.05, ? = -0.44). Differences between regions harboring benign tissue and those harboring malignant tissue were observed at MR imaging and DHA (P < .01). Gleason score was significantly associated with MR imaging and DHA parameters (P < .05). For example, it was positively related to high-b-value DW imaging (slope = 0.21 ± 0.16, ? = 0.18) and negatively related to lumen density (slope = -0.19 ± 0.18, ? = -0.35). Conclusion Overall, significant associations were observed between MR imaging and DHA, regardless of prostate anatomy. © RSNA, 2017 Online supplemental material is available for this article.
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