AI Tool Predicts Cancer Spread with 80% Accuracy Across Multiple Cancer Types

Researchers at the University of Geneva have developed a groundbreaking artificial intelligence tool called MangroveGS — Mangrove Gene Signatures — that can predict whether a cancer is likely to metastasize with nearly 80 percent accuracy. The tool, published in Cell Reports, represents a significant advance in oncology's ability to identify high-risk patients and tailor treatment accordingly.

MangroveGS works by analyzing gene expression patterns in tumor samples to identify molecular signatures that signal an elevated risk of cancer spreading to other parts of the body. Metastasis — the process by which cancer cells break away from the primary tumor and establish new growths elsewhere — is responsible for the vast majority of cancer deaths, making early prediction of metastatic risk one of the most important challenges in cancer treatment.

“The tool, published in Cell Reports, represents a significant advance in oncology's ability to identify high-risk patients and tailor treatment accordingly.”

What makes MangroveGS particularly noteworthy is its versatility across cancer types. Although the AI model was originally trained on colon cancer data, it has demonstrated the ability to predict metastasis in stomach, lung, and breast cancers as well. This cross-cancer applicability suggests that the gene expression patterns associated with metastatic potential share common features across different tumor types.

The practical application of the tool is designed to be straightforward. Tumor samples collected in hospitals undergo RNA sequencing, and the resulting data is fed into MangroveGS, which generates a risk score. This score is then shared with the treating physician via an encrypted platform, providing actionable information that can influence treatment decisions.

The nearly 80 percent accuracy rate outperforms existing methods for predicting metastasis, which often rely on staging systems that consider tumor size and lymph node involvement but cannot capture the molecular complexity that drives cancer spread. By analyzing gene expression at a granular level, MangroveGS provides a more nuanced and accurate assessment of risk.

The research team envisions MangroveGS becoming a standard tool in oncology practice, used alongside traditional diagnostic methods to provide a more complete picture of a patient's cancer and its likely trajectory. The publication in Cell Reports has generated significant interest from oncologists worldwide, who see the tool as a potential game-changer in personalized cancer treatment.