A histopathological tool for quantification of biomarkers with sub-cellular resolution

Kårsnäs, Andreas; Strand, Robin; Doré, Johan; Ebstrup, Thomas; Lippert, Michael Friis; Bjerrum, Kim

doi:10.1080/21681163.2014.885120

Cited by 7 publications

(9 citation statements)

References 42 publications

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“…Inter-platform variability between different DIA platforms may be expected as tissue morphology, cellular features, and staining patterns are handled differently depending on the platform’s algorithm [ 15 , 20 ]. In clinical practice, this could lead to inconsistency of Ki67 scores when different platforms are used to perform DIA.…”

Section: Discussionmentioning

confidence: 99%

“…Studies comparing manual scoring and DIA have used different platforms by various vendors, which have unique image analysis algorithms to determine Ki67 proliferation index [ 12 – 17 ]. As these algorithms have different approaches to classify tissue and cellular components, inter-platform variability may be expected [ 15 , 20 ]. To the best of our knowledge, all studies up to this date have implemented only one DIA platform per study and therefore have not examined inter-platform agreement.…”

Section: Introductionmentioning

confidence: 99%

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Digital image analysis of Ki67 proliferation index in breast cancer using virtual dual staining on whole tissue sections: clinical validation and inter-platform agreement

Koopman

Buikema

Hollema

et al. 2018

Breast Cancer Res Treat

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PurposeThe Ki67 proliferation index is a prognostic and predictive marker in breast cancer. Manual scoring is prone to inter- and intra-observer variability. The aims of this study were to clinically validate digital image analysis (DIA) of Ki67 using virtual dual staining (VDS) on whole tissue sections and to assess inter-platform agreement between two independent DIA platforms.MethodsSerial whole tissue sections of 154 consecutive invasive breast carcinomas were stained for Ki67 and cytokeratin 8/18 with immunohistochemistry in a clinical setting. Ki67 proliferation index was determined using two independent DIA platforms, implementing VDS to identify tumor tissue. Manual Ki67 score was determined using a standardized manual counting protocol. Inter-observer agreement between manual and DIA scores and inter-platform agreement between both DIA platforms were determined and calculated using Spearman’s correlation coefficients. Correlations and agreement were assessed with scatterplots and Bland–Altman plots.ResultsSpearman’s correlation coefficients were 0.94 (p < 0.001) for inter-observer agreement between manual counting and platform A, 0.93 (p < 0.001) between manual counting and platform B, and 0.96 (p < 0.001) for inter-platform agreement. Scatterplots and Bland–Altman plots revealed no skewness within specific data ranges. In the few cases with ≥ 10% difference between manual counting and DIA, results by both platforms were similar.ConclusionsDIA using VDS is an accurate method to determine the Ki67 proliferation index in breast cancer, as an alternative to manual scoring of whole sections in clinical practice. Inter-platform agreement between two different DIA platforms was excellent, suggesting vendor-independent clinical implementability.Electronic supplementary materialThe online version of this article (10.1007/s10549-018-4669-2) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital image analysis of Ki67 proliferation index in breast cancer using virtual dual staining on whole tissue sections: clinical validation and inter-platform agreement

Koopman

Buikema

Hollema

et al. 2018

Breast Cancer Res Treat

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“…These have previously been validated on data from multiple sites comprising thousands of tumor samples to ensure that the variance that DIA contributes is kept at a minimum 40 (see specific statistics on reproducibility with Visiopharm integrator system for each tested biomarker in Supplementary Data).…”

Section: Discussionmentioning

confidence: 99%

“…Thus, only cells that express cytokeratin are eligible for detection of positivity or negativity for the respective biomarker. Individual applications for each biomarker then run the scoring of positive and negative cells itself, with subcellular resolution 40 ( Figure 1). Excellent reproducibility with this and similar systems has been shown previously [41][42][43][44][45] (see specific statistics on reproducibility with the Visiopharm integrator system for each tested biomarker in Supplementary Data).…”

Section: Digital Image Analysismentioning

confidence: 99%

Digital image analysis outperforms manual biomarker assessment in breast cancer

Stålhammar

Martínez

Lippert³

et al. 2016

Modern Pathology

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149

128

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In the spectrum of breast cancers, categorization according to the four gene expression-based subtypes 'Luminal A,' 'Luminal B,' 'HER2-enriched,' and 'Basal-like' is the method of choice for prognostic and predictive value. As gene expression assays are not yet universally available, routine immunohistochemical stains act as surrogate markers for these subtypes. Thus, congruence of surrogate markers and gene expression tests is of utmost importance. In this study, 3 cohorts of primary breast cancer specimens (total n = 436) with up to 28 years of survival data were scored for Ki67, ER, PR, and HER2 status manually and by digital image analysis (DIA). The results were then compared for sensitivity and specificity for the Luminal B subtype, concordance to PAM50 assays in subtype classification and prognostic power. The DIA system used was the Visiopharm Integrator System. DIA outperformed manual scoring in terms of sensitivity and specificity for the Luminal B subtype, widely considered the most challenging distinction in surrogate subclassification, and produced slightly better concordance and Cohen's κ agreement with PAM50 gene expression assays. Manual biomarker scores and DIA essentially matched each other for Cox regression hazard ratios for all-cause mortality. When the Nottingham combined histologic grade (Elston-Ellis) was used as a prognostic surrogate, stronger Spearman's rank-order correlations were produced by DIA. Prognostic value of Ki67 scores in terms of likelihood ratio χ 2 (LR χ 2 ) was higher for DIA that also added significantly more prognostic information to the manual scores (LR− Δχ 2 ). In conclusion, the system for DIA evaluated here was in most aspects a superior alternative to manual biomarker scoring. It also has the potential to reduce time consumption for pathologists, as many of the steps in the workflow are either automatic or feasible to manage without pathological expertise.

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“…For a more general system dealing with various histologically stained image data, Kårsnäs et al. [KSD*15] provided a comprehensive software tool for extracting and quantifying sub‐cellular data. They especially account for biological and staining‐induced variations.…”

Section: Related Workmentioning

confidence: 99%

Virtual Inflation of the Cerebral Artery Wall for the Integrated Exploration of OCT and Histology Data

Glaßer

Hoffmann

Boese

et al. 2016

Computer Graphics Forum

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Intravascular imaging provides new insights into the condition of vessel walls. This is crucial for cerebrovascular diseases including stroke and cerebral aneurysms, where it may present an important factor for indication of therapy. In this work, we provide new information of cerebral artery walls by combining ex vivo optical coherence tomography (OCT) imaging with histology data sets. To overcome the obstacles of deflated and collapsed vessels due to the missing blood pressure, the lack of co‐alignment as well as the geometrical shape deformations due to catheter probing, we developed the new image processing method virtual inflation. We locally sample the vessel wall thickness based on the (deflated) vessel lumen border instead of the vessel's centerline. Our method is embedded in a multi‐view framework where correspondences between OCT and histology can be highlighted via brushing and linking yielding OCT signal characteristics of the cerebral artery wall and its pathologies. Finally, we enrich the data views with a hierarchical clustering representation which is linked via virtual inflation and further supports the deduction of vessel wall pathologies.

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A histopathological tool for quantification of biomarkers with sub-cellular resolution

Cited by 7 publications

References 42 publications

Digital image analysis of Ki67 proliferation index in breast cancer using virtual dual staining on whole tissue sections: clinical validation and inter-platform agreement

Digital image analysis of Ki67 proliferation index in breast cancer using virtual dual staining on whole tissue sections: clinical validation and inter-platform agreement

Digital image analysis outperforms manual biomarker assessment in breast cancer

Virtual Inflation of the Cerebral Artery Wall for the Integrated Exploration of OCT and Histology Data

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