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Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays.

ABSTRACT: Tissue microarrays (TMAs) have become a valuable resource for biomarker expression in translational research. Immunohistochemical (IHC) assessment of TMAs is the principal method for analysing large numbers of patient samples, but manual IHC assessment of TMAs remains a challenging and laborious task. With advances in image analysis, computer-generated analyses of TMAs have the potential to lessen the burden of expert pathologist review.In current commercial software computerised oestrogen receptor (ER) scoring relies on tumour localisation in the form of hand-drawn annotations. In this study, tumour localisation for ER scoring was evaluated comparing computer-generated segmentation masks with those of two specialist breast pathologists. Automatically and manually obtained segmentation masks were used to obtain IHC scores for thirty-two ER-stained invasive breast cancer TMA samples using FDA-approved IHC scoring software.Although pixel-level comparisons showed lower agreement between automated and manual segmentation masks (?=0.81) than between pathologists' masks (?=0.91), this had little impact on computed IHC scores (Allred; =0.91, Quickscore; =0.92).The proposed automated system provides consistent measurements thus ensuring standardisation, and shows promise for increasing IHC analysis of nuclear staining in TMAs from large clinical trials.

SUBMITTER: Akbar S

PROVIDER: S-EPMC4651129 | biostudies-other | 2015 Sep

REPOSITORIES: biostudies-other

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Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays.

Akbar Shazia S Jordan Lee B LB Purdie Colin A CA Thompson Alastair M AM McKenna Stephen J SJ

British journal of cancer 20150908 7

<h4>Background</h4>Tissue microarrays (TMAs) have become a valuable resource for biomarker expression in translational research. Immunohistochemical (IHC) assessment of TMAs is the principal method for analysing large numbers of patient samples, but manual IHC assessment of TMAs remains a challenging and laborious task. With advances in image analysis, computer-generated analyses of TMAs have the potential to lessen the burden of expert pathologist review.<h4>Methods</h4>In current commercial so ...[more]

PMID: 26348443

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Project description:Automated methods are needed to facilitate high-throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large-scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37-0.87) and study (kappa range = 0.39-0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p-value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000-4,500 cells: kappa = 0.78) than those with lower counts (50-500 cells: kappa = 0.41; p-value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre- and post-analytical quality control procedures are necessary in order to ensure satisfactory performance.

Project description:Breast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large-scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65-70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa?=?0.76), followed by human epidermal growth factor receptor 2 (Kappa?=?0.69) and progesterone receptor (Kappa?=?0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose-response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96-98%), but yielded many false positives (positive predictive value?=?30-32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large-scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker-specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results.

Project description:BackgroundDetection of HER2/neu receptor overexpression and/or amplification is a prerequisite for efficient anti-HER2 treatment of breast and gastric carcinomas. Immunohistochemistry (IHC) of the HER2 protein is the most common screening test, thus precise and reproducible IHC-scoring is of utmost importance. Interobserver variance still is a problem; in particular in gastric carcinomas the reliable differentiation of IHC scores 2+ and 1+ is challenging. Herein we describe the physical basis of what we called the 'magnification rule': Different microscope objectives are employed to reproducibly subdivide the continuous spectrum of IHC staining intensities into distinct categories (1+, 2+, 3+).MethodsHER2-IHC was performed on 120 breast cancer biopsy specimens (n = 40 per category). Width and color-intensity of membranous DAB chromogen precipitates were measured by whole-slide scanning and digital morphometry. Image-analysis data were related to semi-quantitative manual scoring according to the magnification rule and to the optical properties of the employed microscope objectives.ResultsThe semi-quantitative manual HER2-IHC scores are correlated to color-intensity measured by image-analysis and to the width of DAB-precipitates. The mean widths ±standard deviations of precipitates were: IHC-score 1+, 0.64 ± 0.1 μm; score 2+, 1.0 ± 0.23 μm; score 3+, 2.14 ± 0.4 μm. The width of precipitates per category matched the optical resolution of the employed microscope objective lenses: Approximately 0.4 μm (40×), 1.0 μm (10×) and 2.0 μm (5×).ConclusionsPerceived intensity, width of the DAB chromogen precipitate, and absolute color-intensity determined by image-analysis are linked. These interrelations form the physical basis of the 'magnification rule': 2+ precipitates are too narrow to be observed with 5× microscope objectives, 1+ precipitates are too narrow for 10× objectives. Thus, the rule uses the optical resolution windows of standard diagnostic microscope objectives to derive the width of the DAB-precipitates. The width is in turn correlated with color-intensity. Hereby, the more or less subjective estimation of IHC scores based only on the staining-intensity is replaced by a quasi-morphometric measurement. The principle seems universally applicable to immunohistochemical stainings of membrane-bound biomarkers that require an intensity-dependent scoring.

Dataset Information

Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays.

Publications

Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays.

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