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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.
Aiforia MIT webinar histopathology image

Watch our webinar with MIT postdoc Peter Westcott:

Using AI to study non-small-cell lung carcinoma

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  • Cancer biomarkers PD-L1, ER, Ki67 and HER2
  • Tumor-infiltrating lymphocyte (TIL) percentage
  • Of spatial and morphological metrics
  • Cell-to-tumor distance, for example: between stromal CD8+ cells and lung cancer tumor borders
  • Cell-to-cell distances, including unique cell pairs (only the closest cells), for example: PD1 and PD-L1 pairing in lung stroma
  • Multi-class segmentation
  • Precise and quantitative grading of whole slide images
  • Tumor grading and tumor burden
  • 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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