Dataset Information

Development and Validation of a Histological Method to Measure Microvessel Density in Whole-Slide Images of Cancer Tissue.

ABSTRACT: Despite all efforts made to develop predictive biomarkers for antiangiogenic therapies, no unambiguous markers have been identified so far. This is due to among others the lack of standardized tests. This study presents an improved microvessel density quantification method in tumor tissue based on stereological principles and using whole-slide images. Vessels in tissue sections of different cancer types were stained for CD31 by an automated and validated immunohistochemical staining method. The stained slides were digitized with a digital slide scanner. Systematic, uniform, random sampling of the regions of interest on the whole-slide images was performed semi-automatically with the previously published applications AutoTag and AutoSnap. Subsequently, an unbiased counting grid was combined with the images generated with these scripts. Up to six independent observers counted microvessels in up to four cancer types: colorectal carcinoma, glioblastoma multiforme, ovarian carcinoma and renal cell carcinoma. At first, inter-observer variability was found to be unacceptable. However, after a series of consensus training sessions and interim statistical analysis, counting rules were modified and inter-observer concordance improved considerably. Every CD31-positive object was counted, with exclusion of suspected CD31-positive monocytes, macrophages and tumor cells. Furthermore, if interconnected, stained objects were considered a single vessel. Ten regions of interest were sufficient for accurate microvessel density measurements. Intra-observer and inter-observer variability were low (intraclass correlation coefficient > 0.7) if the observers were adequately trained.

SUBMITTER: Marien KM

PROVIDER: S-EPMC5008750 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Development and Validation of a Histological Method to Measure Microvessel Density in Whole-Slide Images of Cancer Tissue.

Marien Koen M KM Croons Valerie V Waumans Yannick Y Sluydts Ellen E De Schepper Stefanie S Andries Luc L Waelput Wim W Fransen Erik E Vermeulen Peter B PB Kockx Mark M MM De Meyer Guido R Y GR

PloS one 20160901 9

Despite all efforts made to develop predictive biomarkers for antiangiogenic therapies, no unambiguous markers have been identified so far. This is due to among others the lack of standardized tests. This study presents an improved microvessel density quantification method in tumor tissue based on stereological principles and using whole-slide images. Vessels in tissue sections of different cancer types were stained for CD31 by an automated and validated immunohistochemical staining method. The ...[more]

PMID: 27583442

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Project description:BackgroundDeep learning has revolutionized digital pathology, allowing automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs are broken into smaller images called tiles, and a neural network encodes each tile. Many recent works use supervised attention-based models to aggregate tile-level features into a slide-level representation, which is then used for downstream analysis. Training supervised attention-based models is computationally intensive, architecture optimization of the attention module is non-trivial, and labeled data are not always available. Therefore, we developed an unsupervised and fast approach called SAMPLER to generate slide-level representations.MethodsSlide-level representations of SAMPLER are generated by encoding the cumulative distribution functions of multiscale tile-level features. To assess effectiveness of SAMPLER, slide-level representations of breast carcinoma (BRCA), non-small cell lung carcinoma (NSCLC), and renal cell carcinoma (RCC) WSIs of The Cancer Genome Atlas (TCGA) were used to train separate classifiers distinguishing tumor subtypes in FFPE and frozen WSIs. In addition, BRCA and NSCLC classifiers were externally validated on frozen WSIs. Moreover, SAMPLER's attention maps identify regions of interest, which were evaluated by a pathologist. To determine time efficiency of SAMPLER, we compared runtime of SAMPLER with two attention-based models. SAMPLER concepts were used to improve the design of a context-aware multi-head attention model (context-MHA).FindingsSAMPLER-based classifiers were comparable to state-of-the-art attention deep learning models to distinguish subtypes of BRCA (AUC = 0.911 ± 0.029), NSCLC (AUC = 0.940 ± 0.018), and RCC (AUC = 0.987 ± 0.006) on FFPE WSIs (internal test sets). However, training SAMLER-based classifiers was >100 times faster. SAMPLER models successfully distinguished tumor subtypes on both internal and external test sets of frozen WSIs. Histopathological review confirmed that SAMPLER-identified high attention tiles contained subtype-specific morphological features. The improved context-MHA distinguished subtypes of BRCA and RCC (BRCA-AUC = 0.921 ± 0.027, RCC-AUC = 0.988 ± 0.010) with increased accuracy on internal test FFPE WSIs.InterpretationOur unsupervised statistical approach is fast and effective for analyzing WSIs, with greatly improved scalability over attention-based deep learning methods. The high accuracy of SAMPLER-based classifiers and interpretable attention maps suggest that SAMPLER successfully encodes the distinct morphologies within WSIs and will be applicable to general histology image analysis problems.FundingThis study was supported by the National Cancer Institute (Grant No. R01CA230031 and P30CA034196).

Project description:ObjectiveDigital pathology is today a widely used technology, and the digitalization of microscopic slides into whole slide images (WSIs) allows the use of machine learning algorithms as a tool in the diagnostic process. In recent years, "deep learning" algorithms for image analysis have been applied to digital pathology with great success. The training of these algorithms requires a large volume of high-quality images and image annotations. These large image collections are a potent source of information, and to use and share the information, standardization of the content through a consistent terminology is essential. The aim of this project was to develop a pilot dataset of exhaustive annotated WSI of normal and abnormal human tissue and link the annotations to appropriate ontological information.Materials and methodsSeveral biomedical ontologies and controlled vocabularies were investigated with the aim of selecting the most suitable ontology for this project. The selection criteria required an ontology that covered anatomical locations, histological subcompartments, histopathologic diagnoses, histopathologic terms, and generic terms such as normal, abnormal, and artifact. WSIs of normal and abnormal tissue from 50 colon resections and 69 skin excisions, diagnosed 2015-2016 at the Department of Clinical Pathology in Linköping, were randomly collected. These images were manually and exhaustively annotated at the level of major subcompartments, including normal or abnormal findings and artifacts.ResultsSystemized nomenclature of medicine clinical terms (SNOMED CT) was chosen, and the annotations were linked to its codes and terms. Two hundred WSI were collected and annotated, resulting in 17,497 annotations, covering a total area of 302.19 cm2, equivalent to 107,7 gigapixels. Ninety-five unique SNOMED CT codes were used. The time taken to annotate a WSI varied from 45 s to over 360 min, a total time of approximately 360 h.ConclusionThis work resulted in a dataset of 200 exhaustive annotated WSIs of normal and abnormal tissue from the colon and skin, and it has informed plans to build a comprehensive library of annotated WSIs. SNOMED CT was found to be the best ontology for annotation labeling. This project also demonstrates the need for future development of annotation tools in order to make the annotation process more efficient.

Dataset Information

Development and Validation of a Histological Method to Measure Microvessel Density in Whole-Slide Images of Cancer Tissue.

Publications

Development and Validation of a Histological Method to Measure Microvessel Density in Whole-Slide Images of Cancer Tissue.

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