AI Improves Lung Cancer Diagnostics

An interdisciplinary research team led by Professor Frederick Klauschen, Director of the Institute of Pathology at LMU and research group leader at the Berlin Institute for the Foundations of Learning and Data (BIFOLD), together with Professor Klaus-Robert Müller (BIFOLD/TU Berlin), has now developed a novel AI-based method for making more accurate prognoses for lung cancer patients.

Until now, such predictions of the likelihood of lung cancer recurrence have been based mainly on factors such as tumor size or lymph node involvement, which are often insufficient, especially in early disease stages. This so-called “staging” does not capture the detailed interactions between the tumor and the various cell types of the immune and connective tissue in its surrounding environment.

In their work, the interdisciplinary research team from LMU, BIFOLD, TU Berlin, Universitätsklinikum Köln, Charité – Universitätsmedizin Berlin, and the AI company Aignostics combined histological data, multiplex immunofluorescence imaging, and multimodal machine learning to analyze the complex cellular structures and interactions in the tumor microenvironment of 1,168 patients from two major German cancer centers.

Significant improvement in risk stratification

The scientists were able to characterize 43 different cell types and their spatial relationships within the tumor microenvironment, identifying so-called “cellular niches”, small “neighborhoods” of cells (radius approx. 34 µm) in which the composition of cell types (tumor cells, T and B lymphocytes, macrophages, etc.) was analyzed. By using AI to combine these patterns with established clinical parameters, the researchers achieved a significant improvement in risk stratification.

“AI helps us to better understand the spatial organization of cells and the formation of specific cellular niches within the tumor and to translate this knowledge into clinically relevant decisions,” says Dr. Simon Schallenberg, pathologist at Charité and one of the study’s first authors.

Prognostically relevant information

“These niche patterns provide prognostically relevant information in addition to classical staging. We were able to show that the composition of cells in the tumor environment is closely linked to patient survival, particularly in early stages of lung cancer,” explains Prof. Dr. Frederick Klauschen, research group leader at BIFOLD and pathologist at LMU Munich. “Many of these patients can be cured by surgery alone - but not all. Our method now helps identify those at higher risk of recurrence who could benefit from additional therapy.”

“Our results show that multimodal, explainable AI can unlock clinically relevant insights from biomedical data that cannot be achieved with conventional methods. The approach combines AI-based image analysis with AI-driven predictive models, is automated, and will be further validated in prospective studies,” says Prof. Dr. Klaus-Robert Müller, BIFOLD Co-director.

This research illustrates how artificial intelligence and modern imaging technologies can jointly provide new insights into tumor biology, laying the groundwork for more precise diagnostics and personalized treatment decisions.

Schallenberg S., Dernbach G., et al. (2025): AI-powered spatial cell phenomics enhances risk stratification in non-small-cell lung cancer. Nature Communications 2025