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SlideToolkit: an assistive toolset for the histological quantification of whole slide images.

ABSTRACT: The demand for accurate and reproducible phenotyping of a disease trait increases with the rising number of biobanks and genome wide association studies. Detailed analysis of histology is a powerful way of phenotyping human tissues. Nonetheless, purely visual assessment of histological slides is time-consuming and liable to sampling variation and optical illusions and thereby observer variation, and external validation may be cumbersome. Therefore, within our own biobank, computerized quantification of digitized histological slides is often preferred as a more precise and reproducible, and sometimes more sensitive approach. Relatively few free toolkits are, however, available for fully digitized microscopic slides, usually known as whole slides images. In order to comply with this need, we developed the slideToolkit as a fast method to handle large quantities of low contrast whole slides images using advanced cell detecting algorithms. The slideToolkit has been developed for modern personal computers and high-performance clusters (HPCs) and is available as an open-source project on github.com. We here illustrate the power of slideToolkit by a repeated measurement of 303 digital slides containing CD3 stained (DAB) abdominal aortic aneurysm tissue from a tissue biobank. Our workflow consists of four consecutive steps. In the first step (acquisition), whole slide images are collected and converted to TIFF files. In the second step (preparation), files are organized. The third step (tiles), creates multiple manageable tiles to count. In the fourth step (analysis), tissue is analyzed and results are stored in a data set. Using this method, two consecutive measurements of 303 slides showed an intraclass correlation of 0.99. In conclusion, slideToolkit provides a free, powerful and versatile collection of tools for automated feature analysis of whole slide images to create reproducible and meaningful phenotypic data sets.

SUBMITTER: Nelissen BG

PROVIDER: S-EPMC4220929 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Validating digital pathology as substitute for conventional microscopy in diagnosis remains a priority to assure effectiveness. Intermodality concordance studies typically focus on achieving the same diagnosis by digital display of whole slide images and conventional microscopy. Assessment of discrete histological features in whole slide images, such as mitotic figures, has not been thoroughly evaluated in diagnostic practice. To further gauge the interchangeability of conventional microscopy with digital display for primary diagnosis, 12 pathologists examined 113 canine naturally occurring mucosal melanomas exhibiting a wide range of mitotic activity. Design reflected diverse diagnostic settings and investigated independent location, interpretation, and enumeration of mitotic figures. Intermodality agreement was assessed employing conventional microscopy (CM40×), and whole slide image specimens scanned at 20× (WSI20×) and at 40× (WSI40×) objective magnifications. An aggregate 1647 mitotic figure count observations were available from conventional microscopy and whole slide images for comparison. The intraobserver concordance rate of paired observations was 0.785 to 0.801; interobserver rate was 0.784 to 0.794. Correlation coefficients between the 2 digital modes, and as compared to conventional microscopy, were similar and suggest noninferiority among modalities, including whole slide image acquired at lower 20× resolution. As mitotic figure counts serve for prognostic grading of several tumor types, including melanoma, 6 of 8 pathologists retrospectively predicted survival prognosis using whole slide images, compared to 9 of 10 by conventional microscopy, a first evaluation of whole slide image for mitotic figure prognostic grading. This study demonstrated agreement of replicate reads obtained across conventional microscopy and whole slide images. Hence, quantifying mitotic figures served as surrogate histological feature with which to further credential the interchangeability of whole slide images for primary diagnosis.

Dataset Information

SlideToolkit: an assistive toolset for the histological quantification of whole slide images.

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