Dataset Information

Standardization of whole slide image morphologic assessment with definition of a new application: Digital slide dynamic morphometry.

ABSTRACT: BACKGROUND:In histopathology, the quantitative assessment of various morphologic features is based on methods originally conceived on specific areas observed through the microscope used. Failure to reproduce the same reference field of view using a different microscope will change the score assessed. Visualization of a digital slide on a screen through a dedicated viewer allows selection of the magnification. However, the field of view is rectangular, unlike the circular field of optical microscopy. In addition, the size of the selected area is not evident, and must be calculated. MATERIALS AND METHODS:A digital slide morphometric system was conceived to reproduce the various methods published for assessing tumor budding in colorectal cancer. Eighteen international experts in colorectal cancer were invited to participate in a web-based study by assessing tumor budding with five different methods in 100 digital slides. RESULTS:The specific areas to be tested by each method were marked by colored circles. The areas were grouped in a target-like pattern and then saved as an .xml file. When a digital slide was opened, the .xml file was imported in order to perform the measurements. Since the morphometric tool is composed of layers that can be freely moved on top of the digital slide, the technique was named digital slide dynamic morphometry. Twelve investigators completed the task, the majority of them performing the multiple evaluations of each of the cases in less than 12 minutes. CONCLUSIONS:Digital slide dynamic morphometry has various potential applications and might be a useful tool for the assessment of histologic parameters originally conceived for optical microscopy that need to be quantified.

SUBMITTER: Puppa G

PROVIDER: S-EPMC3237062 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Standardization of whole slide image morphologic assessment with definition of a new application: Digital slide dynamic morphometry.

Puppa Giacomo G Risio Mauro M Sheahan Kieran K Vieth Michael M Zlobec Inti I Lugli Alessandro A Pecori Sara S Wang Lai Mun LM Langner Cord C Mitomi Hiroyuki H Nakamura Takatoshi T Watanabe Masahiko M Ueno Hideki H Chasle Jacques J Senore Carlo C Conley Stephen A SA Herlin Paulette P Lauwers Gregory Y GY

Journal of pathology informatics 20111029

<h4>Background</h4>In histopathology, the quantitative assessment of various morphologic features is based on methods originally conceived on specific areas observed through the microscope used. Failure to reproduce the same reference field of view using a different microscope will change the score assessed. Visualization of a digital slide on a screen through a dedicated viewer allows selection of the magnification. However, the field of view is rectangular, unlike the circular field of optical ...[more]

PMID: 22200031

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Project description:For a method to be widely adopted in medical research or clinical practice, it needs to be reproducible so that clinicians and regulators can have confidence in its use. Machine learning and deep learning have a particular set of challenges around reproducibility. Small differences in the settings or the data used for training a model can lead to large differences in the outcomes of experiments. In this work, three top-performing algorithms from the Camelyon grand challenges are reproduced using only information presented in the associated papers and the results are then compared to those reported. Seemingly minor details were found to be critical to performance and yet their importance is difficult to appreciate until the actual reproduction is attempted. We observed that authors generally describe the key technical aspects of their models well but fail to maintain the same reporting standards when it comes to data preprocessing which is essential to reproducibility. As an important contribution of the present study and its findings, we introduce a reproducibility checklist that tabulates information that needs to be reported in histopathology ML-based work in order to make it reproducible. Author summary For a method to be used a lot in medical research or clinical practice, it needs to be able to be reproduced so that people can trust it. Machine learning and deep learning have some challenges around this. For example, small changes in how a model is trained can lead to significant changes in the results of experiments. This makes it essential that researchers report how they do things in enough detail for the results of their experiments to be reproducible. In this work, we looked at three different algorithms used for digital pathology image analysis. We tried to reproduce them using only the information reported in their papers. We confirmed that even minor details could be essential. Authors often do not report all the details needed to reproduce their work. We also created a checklist of things that need to be reported to help other researchers make their work reproducible.

Project description:Renal tubulointerstitial injury often leads to interstitial fibrosis and tubular atrophy (IF/TA). IF/TA is typically assessed in the renal cortex and can be objectively quantitated with computerized image analysis (IA). However, the human medulla accounts for a substantial proportion of the nephron; therefore, medullary scarring will have important cortical consequences and may parallel overall chronic renal injury. Trichrome, periodic acid-Schiff (PAS), and collagen III immunohistochemistry (IHC) were visually examined and quantitated on scanned whole slide images (WSIs) (N = 67 cases). When tuned to measure fibrosis, IA of trichrome and Trichrome-PAS (T-P) WSIs correlated for all anatomic compartments (among cortex, medulla, and entire tissue, r = 0.84 to 0.89, P all <0.0001); and collagen III deposition correlated between compartments (r = 0.69 to 0.89, P <0.0001 to 0.0002); however, trichrome and T-P measures did not correlate with collagen deposition, suggesting heterogeneous contributions to extracellular matrix deposition. Epithelial cell mass (EPCM) correlated between cortex and medulla when measured with cytokeratin IHC and with the trichrome red portion (r = 0.85 and 0.66, respectively, all P < 0.0001). Visual assessment also correlated between compartments for fibrosis and EPCM. Correlations were found between increasing medullary inner stripe (IS) width and fibrosis in all of the tissue and the medulla by trichrome morphometry (r = 0.56, P < 0.0001, and r = 0.48, P = 0.00008, respectively). Weak correlations were found between increasing IS width and decreasing visual assessment of all tissue EPCM. Microvessel density (MVD) and microvessel area (MVA) measured using a MVD algorithm applied to CD34 IHC correlated significantly between all compartments (r = 0.76 to 0.87 for MVD and 0.71 to 0.87 for MVA, P all < 0.0001). Overall, these findings demonstrate the interrelatedness of the cortex and medulla and the importance of considering the renal parenchyma as a whole.

Dataset Information

Standardization of whole slide image morphologic assessment with definition of a new application: Digital slide dynamic morphometry.

Publications

Standardization of whole slide image morphologic assessment with definition of a new application: Digital slide dynamic morphometry.

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