New publications
AI can predict prognosis in triple negative breast cancer
Last reviewed: 02.07.2025
All iLive content is medically reviewed or fact checked to ensure as much factual accuracy as possible.
We have strict sourcing guidelines and only link to reputable media sites, academic research institutions and, whenever possible, medically peer reviewed studies. Note that the numbers in parentheses ([1], [2], etc.) are clickable links to these studies.
If you feel that any of our content is inaccurate, out-of-date, or otherwise questionable, please select it and press Ctrl + Enter.
Researchers at the Karolinska Institute in Sweden have studied how well different artificial intelligence models can predict the prognosis of triple-negative breast cancer by analyzing certain immune cells inside the tumor. The study, published in the journal eClinicalMedicine, is 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 the immune system is trying to attack and destroy cancer cells.
These immune cells may be important for predicting how a patient with so-called triple-negative breast cancer will respond to treatment and how the disease will progress. However, the results of assessing immune cells can vary when pathologists do it. Artificial intelligence (AI) may help standardize and automate this process, but it has been difficult to prove that AI works well enough for use in healthcare.
Ten AI models compared
The researchers tested ten different AI models and compared their ability to analyze tumor-infiltrating lymphocytes in triple-negative breast cancer tissue samples.
The results showed that the AI models varied in their analytical performance. Despite these differences, eight out of ten models showed good predictive ability, meaning they were able to predict the patients’ future health status in a similar way.
Even models trained on smaller numbers of samples showed good predictive ability, indicating that tumor-infiltrating lymphocytes are a reliable biomarker," said Balázs Aç, a researcher at the Department of Oncology and Pathology at Karolinska Institutet.
Independent research is needed
The study shows that large data sets are needed to compare different AI tools and ensure their quality before implementation in healthcare. While the results are promising, more validation is needed.
"Our study highlights the importance of independent studies that mimic real-world clinical practice," says Balazs Aç. "Only through such trials can we be confident that AI tools are reliable and effective for clinical use."