Researchers from Karolinska Institutet studied how well different AI models can predict the prognosis of triple negative breast cancer by analyzing certain immune cells inside the tumor. The study, published in the magazine eMedicineClinicalis an important step toward using AI in cancer care to improve patient health.

Tumor-infiltrating lymphocytes are a type of immune cell that plays an important role in fighting cancer. When they are present in a tumor, it means that the immune system is trying to attack and destroy the cancer cells.

These immune cells may be important in predicting how a patient with so-called triple negative breast cancer will respond to treatment and how the disease will progress. But when pathologists evaluate immune cells, results can vary. Artificial intelligence (AI) can help standardize and automate this process, but it has been difficult to demonstrate that AI works well enough for use in healthcare.

The researchers tested 10 AI models and compared their ability to analyze tumor-infiltrating lymphocytes in triple-negative breast cancer tissue samples.

The results showed that the analytical performance of the AI ​​models varied. Despite these differences, eight of the ten models showed good prognostic ability, meaning they were able to predict patients’ future health in the same way.

“Even models trained on fewer samples showed good prognostic ability, suggesting that tumor-infiltrating lymphocytes are a robust biomarker,” explains Balazs Acs, researcher at the Department of Oncology-Pathology at Karolinska Institutet.

Independent studies are needed

The study shows that large data sets are needed to compare different AI tools and ensure they perform well before they can be used in healthcare. Although the results are promising, more validation is needed.

“OUR research highlights the importance of independent studies that mimic real clinical practice,” says Acs. “Only through such testing can we ensure that AI tools are reliable and effective for clinical use.”