For research use only. Not for use in diagnostic procedures!


A new method of predicting response to anti-PD-L1 checkpoint inhibitor immunotherapy in Non-Small Cell Lung Cancer stages III and IV patients.

By using Artificial Intelligence and Computer Vision technologies in combination with modern Bioinformatics approach, we developed a predictive biomarker capable of predicting responseto anti-PD-L1 checkpoint inhibitor therapy, with response understood in sense of Response Evaluation Criteria in Solid Tumors (RECIST 1.1). The biomarker is computed based on the data from several distinct sources:

Gene expression data

Tumor genome data

Scanned image of tumor histologic specimen

Patient information

Information from these sources is automatically combined within the deep-learning algorithm to produce a single-value biomarker score. The resulting value is then compared with the pre-set thresholds and thus the most probable response type is selected.

Input data



RNA-seq raw gene counts (.txt file)

Slide Image (.svs file)