Artificial intelligence in pathological anatomy: digitization of the calculation of the proliferation index (Ki-67) in breast carcinoma

Abstract

Ki-67 is a non-histone nuclear protein located in the nuclear cortex and is one of the essential biomarkers used to provide the proliferative status of cancer cells. Because of the variability in color, morphology and intensity of the cell nuclei, Ki-67 is sensitive to chemotherapy and radiation therapy. The proliferation index is usually calculated visually by professional pathologists who assess the total percentage of positive (labeled) cells. This semi-quantitative counting can be the source of some inter- and intra-observer variability and is time-consuming. These factors open up a new field of scientific and technological research and development. Artificial intelligence is attracting attention to solve these problems. Our solution is based on deep learning to calculate the percentage of cells labeled by the ki-67 protein. The tumor area with \(\times\)40 magnification is given by the pathologist to segment different types of positive, negative or TIL (tumor infiltrating lymphocytes) cells. The calculation of the percentage comes after cells counting using classical image processing techniques. To give the model our satisfaction, we made a comparison with other datasets of the test and we compared it with the diagnosis of pathologists. Despite the error of our model, KiNet outperforms the best performing models to date in terms of average error measurement.