PT  - JOURNAL ARTICLE
AU  - Riku Turkki
AU  - Nina Linder
AU  - Tanja Holopainen
AU  - Yinhai Wang
AU  - Anne Grote
AU  - Mikael Lundin
AU  - Kari Alitalo
AU  - Johan Lundin
TI  - Assessment of tumour viability in human lung cancer xenografts with texture-based image analysis
AID  - 10.1136/jclinpath-2015-202888
DP  - 2015 Aug 01
TA  - Journal of Clinical Pathology
PG  - 614--621
VI  - 68
IP  - 8
4099  - http://jcp.bmj.com/content/68/8/614.short
4100  - http://jcp.bmj.com/content/68/8/614.full
SO  - J Clin Pathol2015 Aug 01; 68
AB  - Aims To build and evaluate an automated method for assessing tumour viability in histological tissue samples using texture features and supervised learning.Methods H&E-stained sections (n=56) of human non-small cell lung adenocarcinoma xenografts were digitised with a whole-slide scanner. A novel image analysis method based on local binary patterns and a support vector machine classifier was trained with a set of sample regions (n=177) extracted from the whole-slide images and tested with another set of images (n=494). The extracted regions, or single-tissue entity images, were chosen to represent as pure as possible examples of three morphological tissue entities: viable tumour tissue, non-viable tumour tissue and mouse host tissue.Results An agreement of 94.5% (area under the curve=0.995, kappa=0.90) was achieved to classify the single-tissue entity images in the test set (n=494) into the viable tumour and non-viable tumour tissue categories. The algorithm assigned 250 of the 252 non-viable and 219 of the 242 of viable sample regions to the correct categories, respectively. This corresponds to a sensitivity of 90.5% and specificity of 99.2%.Conclusions The proposed image analysis-based tumour viability assessment resulted in a high agreement with expert annotations. By providing extraction of detailed information of the tumour microenvironment, the automated method can be used in preclinical research settings. The method could also have implications in cancer diagnostics, cancer outcome prognostics and prediction.