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Weis, CA; Kather, JN; Melchers, S; Al-Ahmdi, H; Pollheimer, MJ; Langner, C; Gaiser, T.
Automatic evaluation of tumor budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome.
Diagn Pathol. 2018; 13(1):64-64 [OPEN ACCESS]
Web of Science PubMed PUBMED Central FullText FullText_MUG

 

Autor/innen der Med Uni Graz:
Langner Cord
Pollheimer Marion
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Number of Figures: 6
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Abstract:
Tumor budding, meaning a detachment of tumor cells at the invasion front of colorectal carcinoma (CRC) into single cells or clusters (<=5 tumor cells), has been shown to correlate to an inferior clinical outcome by several independent studies. Therefore, it has been discussed as a complementary prognostic factor to the TNM staging system, and it is already included in national guidelines as an additional prognostic parameter. However, its application by manual evaluation in routine pathology is hampered due to the use of several slightly different assessment systems, a time-consuming manual counting process and a high inter-observer variability. Hence, we established and validated an automatic image processing approach to reliably quantify tumor budding in immunohistochemically (IHC) stained sections of CRC samples. This approach combines classical segmentation methods (like morphological operations) and machine learning techniques (k-means and hierarchical clustering, convolutional neural networks) to reliably detect tumor buds in colorectal carcinoma samples immunohistochemically stained for pan-cytokeratin. As a possible application, we tested it on whole-slide images as well as on tissue microarrays (TMA) from a clinically well-annotated CRC cohort. Our automatic tumor budding evaluation tool detected the absolute number of tumor buds per image with a very good correlation to the manually segmented ground truth (R2 value of 0.86). Furthermore the automatic evaluation of whole-slide images from 20 CRC-patients, we found that neither the detected number of tumor buds at the invasion front nor the number in hotspots was associated with the nodal status. However, the number of spatial clusters of tumor buds (budding hotspots) significantly correlated to the nodal status (p-value = 0.003 for N0 vs. N1/N2). TMAs were not feasible for tumor budding evaluation, as the spatial relationship of tumor buds (especially hotspots) was not preserved. Automatic image processing is a feasible and valid assessment tool for tumor budding in CRC on whole-slide images. Interestingly, only the spatial clustering of the tumor buds in hotspots (and especially the number of hotspots) and not the absolute number of tumor buds showed a clinically relevant correlation with patient outcome in our data.
Find related publications in this database (using NLM MeSH Indexing)
Aged -
Automation, Laboratory -
Biomarkers, Tumor - analysis
Cell Adhesion -
Cell Movement -
Cluster Analysis -
Colorectal Neoplasms - chemistry
Colorectal Neoplasms - pathology
Female -
Humans -
Image Interpretation, Computer-Assisted -
Immunohistochemistry -
Keratins - analysis
Machine Learning -
Male -
Neoplasm Invasiveness -
Neural Networks (Computer) -
Observer Variation -
Predictive Value of Tests -
Prognosis -
Reproducibility of Results -
Tissue Array Analysis -

Find related publications in this database (Keywords)
Colorectal carcinoma
Digital pathology
Tumor budding
Image processing
Convolutional neural network
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