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Institute  Discovers Deep-Learning Technology For Cancer Treatments



Israeli researchers have on Monday developed a new Deep Learning (DL) technology that is expected to significantly improve personalised cancer treatments, the northern Israel Institute of Technology (Technion) reported.

This is a method for mapping critical receptors on cancer cells, based on biopsy images of breast cancer patients.

The new method, published in the journal JAMA, extracts molecular information from hematoxylin and eosin stain (H&E) biopsy images, a common staining used to test tissues taken in a biopsy test.

This staining allows the pathologist to identify in the tissue, under the microscope, the type of cancer and its severity.

However, staining alone does not allow to identify critical characteristics that were crucial in determining the appropriate treatment, such as the tumor’s molecular profile, its biological pathways, the genetic code of the cancerous cells.

As well as the common receptors on the cell membrane.

Mapping of these receptors is particularly relevant to personalised medicine, allowing a treatment which would block the receptors and inhibit the development of the cancerous tumor.

The Technion researchers’ conceptual innovation is in extracting molecular information from the cell shape and environment (the morphology of the tissue) as reflected in H&E scans.

According to the researchers, pathologists cannot deduce the tumor properties from its shape because of the huge number of variables.

But on the other hand, Artificial Intelligence (AI), especially DL, are able to do so and characterise the cancer with a complex analysis of its morphology.

However, with help of image processing, AI tools, researchers showed, for the first time, the possibility of predicting the molecular profile of cells from tumor morphology, only from looking at the tissue as it appears in H&E scans.

DL systems require a tremendous amount of information, so the researchers have written software code to scan network sources and automatically download thousands of biopsy samples and the relevant medical information approved for research.

Meanwhile, the study had so far focused on breast cancer, the researchers said it was a proof of feasibility relevant to all cancer types.

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