Assessment of tumour viability in human lung cancer xenografts with texture-based image analysis
By Riku Turkki, N. Linder, Tanja Holopainen, Yinhai Wang, A. Grote, M. Lundin, K. Alitalo, J. Lundin. 2015
17 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Biophysics, Radiology, Nuclear Medicine and Imaging
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In Artificial Intelligence
- Jakob Nikolas Kather
- A. Marx
- Cleo-Aron Weis
- T. Gaiser
- Susanne Melchers
- Francesco Bianconi
- Lothar Rudi Schad
- Frank G. Zöllner
In Biophysics
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Multi-class texture analysis in colorectal cancer histology (2016, methodology): “Other LBP variants have been used for histological texture analysis in other studies 12,13 .”