Detection of tumor-infiltrating lymphocytes (TILs) using explainable AI. Above:...
Detection of tumor-infiltrating lymphocytes (TILs) using explainable AI. Above: the AI-technique is used to generate a heatmap showing TILs (red) and other tissues and cells (blue and green).
Source: Klauschen/Charité

AI system for the diagnosis of breast cancer

Researchers at Charité – Universitätsmedizin Berlin and TU Berlin as well as the University of Oslo have developed a new tissue-section analysis system for diagnosing breast cancer based on artificial intelligence (AI).

Two further developments make this system unique: For the first time, morphological, molecular and histological data are integrated in a single analysis. Secondly, the system provides a clarification of the AI decision process in the form of heatmaps. Pixel by pixel, these heatmaps show which visual information influenced the AI decision process and to what extent, thus enabling doctors to understand and assess the plausibility of the results of the AI analysis. This represents a decisive and essential step forward for the future regular use of AI systems in hospitals.

Cancer treatment is increasingly concerned with the molecular characterization of tumor tissue samples. Studies are conducted to determine whether and/or how the DNA has changed in the tumor tissue as well as the gene and protein expression in the tissue sample. At the same time, researchers are becoming increasingly aware that cancer progression is closely related to intercellular cross-talk and the interaction of neoplastic cells with the surrounding tissue - including the immune system.

Although microscopic techniques enable biological processes to be studied with high spatial detail, they only permit a limited measurement of molecular markers. These are rather determined using proteins or DNA taken from tissue. As a result, spatial detail is not possible and the relationship between these markers and the microscopic structures is typically unclear. “We know that in the case of breast cancer, the number of immigrated immune cells, known as lymphocytes, in tumor tissue has an influence on the patient’s prognosis. There are also discussions as to whether this number has a predictive value - in other words if it enables us to say how effective a particular therapy is,” says Prof. Dr. Frederick Klauschen of Charité’s Institute of Pathology.

“The problem we have is the following: We have good and reliable molecular data and we have good histological data with high spatial detail. What we don’t have as yet is the decisive link between imaging data and high-dimensional molecular data,” adds Prof. Dr. Klaus-Robert Müller, professor of machine learning at TU Berlin. Both researchers have been working together for a number of years now at the national AI center of excellence the Berlin Institute for the Foundations of Learning and Data (BIFOLD) located at TU Berlin.

Breast cancer tissue sample (hematoxylin and eosin staining).
Breast cancer tissue sample (hematoxylin and eosin staining).
Source: Klauschen/Charité

It is precisely this symbiosis which the newly published approach makes possible. “Our system facilitates the detection of pathological alterations in microscopic images. Parallel to this, we are able to provide precise heatmap visualizations showing which pixel in the microscopic image contributed to the diagnostic algorithm and to what extent,” explains Prof. Müller. The research team has also succeeded in significantly further developing this process: “Our analysis system has been trained using machine learning processes so that it can also predict various molecular characteristics, including the condition of the DNA, the gene expression as well as the protein expression in specific areas of the tissue, on the basis of the histological images.

Next on the agenda are certification and further clinical validations - including tests in tumor routine diagnostics. However, Prof. Klauschen is already convinced of the value of the research: “The methods we have developed will make it possible in the future to make histopathological tumor diagnostics more precise, more standardized and qualitatively better.”

The research have been published in Nature Machine Intelligence.

Subscribe to our newsletter

Related articles

A pen to pin down the fringes of cancer

A pen to pin down the fringes of cancer

The MasSpec Pen has shown to accurately differentiate healthy and cancerous tissue from banked pancreas samples during surgery.

MasSpec Pen shows promise in pancreatic cancer surgery

MasSpec Pen shows promise in pancreatic cancer surgery

The MasSpec Pen has shown to accurately identify tissues and surgical margins directly in patients and differentiate healthy and cancerous tissue from banked pancreas samples.

AI finds patterns of mutations in tumour images

AI finds patterns of mutations in tumour images

Researchers have developed an AI algorithm that uses computer vision to analyze tissue samples from cancer patients.

ConvPath software uses AI to identify cancer cells

ConvPath software uses AI to identify cancer cells

A software tool uses artificial intelligence to recognize cancer cells from digital pathology images — giving clinicians a powerful way of predicting patient outcomes.

Combination of AI and radiologists accurately identified breast cancer

Combination of AI and radiologists accurately identified breast cancer

An AI tool identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists.

Hydrogel improves method to diagnose cancer

Hydrogel improves method to diagnose cancer

Researchers tested the effectiveness of specialized hydrogels.

Pancreatic “organoids” mimic the real thing

Pancreatic “organoids” mimic the real thing

Studying these organoids could help researchers develop and test new treatments for pancreatic cancer.

Machine learning helps diagnose leukemia

Machine learning helps diagnose leukemia

Researchers at the University of Bonn show how artificial intelligence improves the evaluation of blood analysis data.

AI assesses metastatic potential in skin cancers

AI assesses metastatic potential in skin cancers

Using a deep learning algorithm, researchers have developed a way to accurately predict which skin cancers are highly metastatic.

Popular articles

Subscribe to Newsletter