AI Model Enhances Accuracy of Breast Cancer Prognosis Predictions

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Artificial intelligence (AI) is increasingly being used for medical and clinical analysis to accelerate patient diagnoses for important diseases. A recent study looked at how AI could improve the accuracy of breast cancer prognosis predictions. This study, led by researchers Fan Zhang, Sheng Chang, Binjie Wang, and Xinhong Zhang from Henan University, was published in the Journal of Chemometrics, and it presented a new AI model that can be used to improve patient outcomes for breast cancer (1).

Breast cancer is one of the most common and deadly cancers among women worldwide. According to the American Cancer Society, it is the second-most frequent cancer that women contract in the United States (2). Breast cancer in women accounts for approximately 30% of all new female cancers each year (2). Estimates are that one out of every eight women will get breast cancer at some point in their life (2).

As a result, accurately predicting patient outcomes is crucial for guiding treatment decisions and improving survival rates. However, traditional prognosis assessments often rely heavily on physicians’ experience and multidisciplinary judgment, which can introduce subjectivity and inconsistency due to the absence of standardized evaluation criteria (1).

What did the researchers do in their study?

In their study, the research team introduced a novel cross-modal contrastive learning model called Predictive Graph Attention Network (PreGAT). The PreGAT model utilizes graph neural networks (GNNs) combined with an attention mechanism to integrate and interpret diverse types of patient data (1). By incorporating both clinical features and graph-structured representations of data, PreGAT enables a more holistic analysis of patient profiles, capturing complex relationships among medical variables that traditional models often overlook (1).

PreGAT has a couple key features that differentiate it from other AI models. As an example, it contains a contrastive learning loss function, which strengthens the model’s ability to distinguish subtle variations between patient cases (1). This design allows the system to more effectively learn patterns associated with different prognostic outcomes (1).

The researchers tested PreGAT on the available METABRIC data set. The METABRIC data set contains breast cancer molecular and clinical data (1). The model achieved an average accuracy of 92.9% and an area under the curve (AUC) value of 0.969, outperforming existing benchmark models used for cancer prognosis prediction (1).

The authors highlight that these results underscore the potential of advanced AI models like PreGAT to enhance clinical decision-making by providing objective, data-driven insights into patient prognosis (1). Such tools could play a vital role in developing personalized treatment strategies, optimizing therapy choices, and ultimately improving patient quality of life.

“This research provides a promising technique for breast cancer prognosis prediction in clinical practice, which can provide more accurate and reliable decision support for the development of precise treatment programs,” the authors wrote in their study (1).

References

1. Zhang, F.; Chang, S.; Wang, B.; Zhang, X. A Breast Cancer Prognosis Prediction Model Based on Cross-Modal Contrastive Learning. J. Chemom. 2025, 39 (11), e70082. DOI: 10.1002/cem.70082
2. American Cancer Society, Key Statistics for Breast Cancer. ACS.org. Available at: https://www.cancer.org/cancer/types/breast-cancer/about/how-common-is-breast-cancer.html (accessed 2025-11-4).