Content-based analysis of Ki-67 stained meningioma specimens for automatic hot-spot selection

Świderska-Chadaj, Żaneta; Markiewicz, Tomasz; Grala, Bartłomiej; Lorent, Małgorzata

doi:10.1186/s13000-016-0546-7

Cited by 14 publications

(21 citation statements)

References 33 publications

(48 reference statements)

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“…The integration of automated hot-spot selection in this study sets it apart from most other studies that have explored automated Ki-67 index quantification. Only a few studies are currently in the literature that explore automated hot-spot detection 13,[27][28][29] . Our pipeline generates Ki-67 indices that are comparable to the GS of exhaustive manual counting by a pathologist.…”

Section: Discussionmentioning

confidence: 99%

Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning

Govind

Jen

Matsukuma

et al. 2020

Sci Rep

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The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the number of Ki-67-positive and Ki-67negative tumor cells within a subjectively picked hot-spot. Intraobserver variability in this procedure as well as difficulty in distinguishing tumor from non-tumor cells can lead to inaccurate Ki-67 indices and possibly incorrect tumor grades. We introduce two computational tools that utilize Ki-67 and synaptophysin double-immunostained (DS) slides to improve the accuracy of Ki-67 index quantitation in GI-NETs: (1) Synaptophysin-KI-Estimator (SKIE), a pipeline automating Ki-67 index quantitation via whole-slide image (WSI) analysis and (2) deep-SKIE, a deep learner-based approach where a Ki-67 index heatmap is generated throughout the tumor. Ki-67 indices for 50 GI-NETs were quantitated using SKIE and compared with DS slide assessments by three pathologists using a microscope and a fourth pathologist via manually ticking off each cell, the latter of which was deemed the gold standard (GS). Compared to the GS, SKIE achieved a grading accuracy of 90% and substantial agreement (linearweighted Cohen's kappa 0.62). Using DS WSIs, deep-SKIE displayed a training, validation, and testing accuracy of 98.4%, 90.9%, and 91.0%, respectively, significantly higher than using SS WSIs. Since DS slides are not standard clinical practice, we also integrated a cycle generative adversarial network into our pipeline to transform SS into DS WSIs. The proposed methods can improve accuracy and potentially save a significant amount of time if implemented into clinical practice. The Ki-67 index is an important prognostic marker and the most widely used parameter for grading gastrointestinal neuroendocrine tumors (GI-NETs) 1-3. The current practice for obtaining the Ki-67 index involves microscopic examination of tumor tissue that is immunostained for only Ki-67 (henceforth referred to as singleimmunostained or SS). First, a hot-spot (tumor region with the highest density of Ki-67-positive tumor cells) is selected, which is then used to manually obtain the percentage of Ki-67-positive tumor cells by counting a total of 500 to 2000 tumor cells 2,3. Current GI-NET grading, as proposed by the World Health Organization (WHO) 2017 recommendations 4,5 is based entirely on the mitotic count and Ki-67 index, of which the latter has proven to more accurately reflect biological behavior 6,7. A Ki-67 index of < 3% is grade 1 (G1), between 3 and 20% is grade 2 (G2), and > 20% is grade 3 (G3) 4,5. Nevertheless, the Ki-67 index still suffers from intra-and inter-observer variability 8 , especially for differentiating G1 from G2 GI-NETs, given the subjective nature of hot-spot selection as well as the common practice of "eyeball" estimation among pathologists due to the cumbersome process of manually counting individual tumor cells 9. Thus, an automated method of quantifying...

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Section: Discussionmentioning

confidence: 99%

Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning

Govind

Jen

Matsukuma

et al. 2020

Sci Rep

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“…RGB [8,22,30,51,68,72,94,106,108,119,136,142,146,157,165,170] HSV [30,67,69,100,105,118,137,150,189] HSI [30,67,69,100,105,118,137,150,189] L * a * b * [22,30,66,81,94,139,185] L * u * v * [30,125,155,160,165,191,193] YUV [23,26,27,80,…”

Section: Color Space Referencesmentioning

confidence: 99%

“…The algorithms for color image segmentation have been developed because color features may provide relevant data about the objects within the image. These algorithms have been applied in different areas such as medicine [9,55,127,160,193] and food analysis [47,108,111] , among others [7,14,33,74,137,169,174] . Many of the techniques developed for image segmentation in gray scale have been extended for color images [34,95,114,147,158,172,197] ; however, such techniques cannot be always successfully applied, because they are designed to process mainly the intensity of the colors without considering the chromaticity.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of images by color features: A survey

et al. 2018

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Image segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown.

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“…[111317] Molecular markers of mitosis, such as phosphorylated histone H3 (pHH3) and MKI67 (Ki-67), improve prognostic utility of counting MF compared to routine H&E tissue stains. [2715171819] Active development of computer-assisted image analysis indicates promise for improving reproducibility and accuracy. [1113141517202122232425] The ability to specifically identify and segment the proliferating cells in digitized whole-slide images (WSIs) using computer-assisted diagnosis (CAD) can be complex, however.…”

Section: Introductionmentioning

confidence: 99%

“…Certain challenges in assessing proliferative activity using computer-assisted decision support have been described, and a limited number of automated approaches for detecting MF in tissue sections have been published. [141518192021] Some shortcomings in image processing performance persist, nevertheless. Chief among these includes insufficient locating clinically meaningful mitotic HS regions topographically, substantial minimization of common confounding tissue artifacts, generation of MF counts that are clinically relatable to the MF count values currently produced using conventional microscopy, and the ability for pathologists to quality assure algorithm performance on a patient by patient basis.…”

Section: Introductionmentioning

confidence: 99%

Automated Computational Detection, Quantitation, and Mapping of Mitosis in Whole-Slide Images for Clinically Actionable Surgical Pathology Decision Support

Puri

Hoover

Hewitt

et al. 2019

Journal of Pathology Informatics

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Background: Determining mitotic index by counting mitotic figures (MFs) microscopically from tumor areas with most abundant MF (hotspots [HS]) produces a prognostically useful tumor grading biomarker. However, interobserver concordance identifying MF and HS can be poorly reproducible. Immunolabeling MF, coupled with computer-automated counting by image analysis, can improve reproducibility. A computational system for obtaining MF values across digitized whole-slide images (WSIs) was sought that would minimize impact of artifacts, generate values clinically relatable to counting ten high-power microscopic fields of view typical in conventional microscopy, and that would reproducibly map HS topography. Materials and Methods: Relatively low-resolution WSI scans (0.50 μm/pixel) were imported in grid-tile format for feature-based MF segmentation, from naturally occurring canine melanomas providing a wide range of proliferative activity. MF feature extraction conformed to anti-phospho-histone H3-immunolabeled mitotic (M) phase cells. Computer vision image processing was established to subtract key artifacts, obtain MF counts, and employ rotationally invariant feature extraction to map MF topography. Results: The automated topometric HS (TMHS) algorithm identified mitotic HS and mapped select tissue tiles with greatest MF counts back onto WSI thumbnail images to plot HS topographically. Influence of dye, pigment, and extraneous structure artifacts was minimized. TMHS diagnostic decision support included image overlay graphics of HS topography, as well as a spreadsheet and plot of tile-based MF count values. TMHS performance was validated examining both mitotic HS counting and mapping functions. Significantly correlated TMHS MF mapping and metrics were demonstrated using repeat analysis with WSI in different orientation ( R 2 = 0.9916) and by agreement with a pathologist ( R 2 = 0.8605) as well as through assessment of counting function using an independently tuned object counting algorithm (OCA) ( R 2 = 0.9482). Limits of agreement analysis support method interchangeability. MF counts obtained led to accurate patient survival prediction in all ( n = 30) except one case. By contrast, more variable performance was documented when several pathologists examined similar cases using microscopy (pair-wise correlations, rho range = 0.7597–0.9286). Conclusions: Automated TMHS MF segmentation and feature engineering performance were interchangeable with both observer and OCA in digital mode. Moreover, enhanced HS location accuracy and superior method reproducibility were achieved using the automated TMHS algorithm compared to the current practice employing clinical microscopy.

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Content-based analysis of Ki-67 stained meningioma specimens for automatic hot-spot selection

Cited by 14 publications

References 33 publications

Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning

Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning

Segmentation of images by color features: A survey

Automated Computational Detection, Quantitation, and Mapping of Mitosis in Whole-Slide Images for Clinically Actionable Surgical Pathology Decision Support

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