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Cancer

Transform discovery

 

Cancer research and therapeutics is increasingly being approached from an immunological perspective and the number of  samples in research and diagnostics is growing.

This coupled with the issues of bias and subjectivity in image analysis and the demand for more quantitative information are calling for a need to transform how we look at cancer — a need to automate, standardize, and optimize.

 

How does Aiforia enable cancer research?

Aiforia’s deep learning AI platform has successfully enabled the automation of a versatile range of analyses in a number of areas and sample types such as in immuno-oncology, breast, prostate, lung, and testis.
With Aiforia you can automate the detection of relevant regions of interest based on morphology, use external ground-truths (e.g. treated vs non-treated groups) to find these regions in precise locations, and visualize the feature of interest.

Solutions

Quantification
  • Cancer biomarkers PD-L1, ER, Ki67 and HER2
  • Tumor-infiltrating lymphocyte (TIL) percentage
Measurement
  • Of spatial and morphological metrics
  • Cell-to-tumor distance, for example: between stromal CD8+ cells and lung cancer tumor borders (see Figure 1)
  • Cell-to-cell distances, including unique cell pairs (only the closest cells), for example: PD1 and PD-L1 pairing in lung stroma (see Figure 2)
Segmentation
  • Multi-class segmentation
  • Precise and quantitative grading of whole slide images
  • Tumor grading and tumor burden
Regression
  • Regression models predicting continuous values
  • Cell and nucleus size, patient survival time and other end-points
Aiforia yields reproducible results that are easy to validate visually. The platforms supports H&E and immunofluorescence staining, including multiplex IF, IHC, whole slide images as well as batch analysis of multiple samples.

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