Recently, a joint research team from the The First Affiliated Hospital, Zhejiang University School of Medicine and the Hong Kong University of Science and Technology (HKUST), together with partner hospitals in China, announced a major breakthrough in AI-assisted triple negative breast cancer (TNBC) identification and prognosis prediction. Their work introduces the TRiple-negative breast cancer Identification and Prognosis prediction (TRIP) System designed for simultaneously identifying triple-negative breast cancer and predicting its prognosis based on H&E slides. Published in EClinicalMedicine (IF = 10.0, CAS Medicine Q1), this study demonstrates substantial accuracy gains and time savings for pathologists in clinical scenarios.

Introduction

In this study, we developed and rigorously validated the TRIP system, leveraging 4,898 breast cancer patient samples (including over 1,000 TNBC cases) from five hospitals in China and the public TCGA dataset. The system accurately identifies TNBC subtype and predicts patient’s disease-free survival and overall survival using H&E slides, providing an innovative paradigm for integrated AI-assisted pathology image analysis of TNBC.

The development of TRIP not only provides a new diagnostic and prognostic tool for TNBC, the most aggressive and heterogeneous breast cancer subtype, but also injects new momentum into intelligent pathology and precision oncology.

Clinical Challenges

Triple-negative breast cancer, the most aggressive subtype of breast cancer, accounts for approximately 15% of all breast cancer cases. Its cancer cells do not express estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2), lacking clear therapeutic targets. The five-year survival rate for patients is only approximately 75%, significantly lower than that of other breast cancer subtypes (over 90%). Current clinical diagnosis relies primarily on immunohistochemistry (IHC), which is costly, time-consuming, and requires stringent tissue specimens. Furthermore, due to tumor heterogeneity, even patients with the same TNM stage have varying prognoses. Existing prognostic stratification methods based on clinicopathological features are limited in effectiveness, necessitating an urgent need for more efficient and accurate diagnostic and prognostic tools. Previous AI researches on triple-negative breast cancer have been plagued by small sample sizes (less than 600 TNBC patients), limited validation cohorts, and single clinical tasks, making it difficult to meet clinical needs. The TRIP system is developed by integrating multi-center large-sample data with innovative algorithms, and achieves the “identification + prognosis” multi-task integration for the first time, filling the technological gap in this field.

Method

​​The exceptional performance of the TRIP system stems from multiple innovations in its underlying technical architecture, establishing a fully intelligent solution from pathology image analysis to clinical prediction:​​

• ​​Unified Multiple-Instance Learning Framework:​​ Unlike traditional single-task AI models, the TRIP system employs the same AI network structure simultaneously supporting two key modules: “Triple-Negative Breast Cancer (TNBC) Identification” and “Disease-Free Survival (DFS)/Overall Survival (OS) Prediction,” enabling efficient analysis of WSIs without manual intervention.
• Effective and Efficient Long-sequence Modeling:​​ The system utilizes a state-of-the-art pathology foundation model GPFM to extract patch-level features, which can automatically adapts to staining variations across different hospitals, eliminating the need for additional staining normalization. Then, a bidirectional Mamba encoder effectively captures long-range dependencies between patches across the entire slide, addressing the limitation of traditional Mamba models that early information in long sequences is easy to be forgot, thereby enhancing the accuracy of pathology image analysis.
• Dynamic Adaptation for Enhanced Generalizability:​​ To tackle challenges like staining and scanner differences across hospitals, the system integrates a Test-Time Adaptation (TTA) strategy. By fine-tuning only the parameters of normalization layer, it significantly improves prediction robustness on data from external sources, laying a solid foundation for real-world clinical deployment.

Validation and Results

To validate the effectiveness of the TRIP system, we constructed a large-scale dataset covering five tertiary hospitals in China and the public TCGA dataset. A total of 4,898 breast cancer patients (including more than 1,000 TNBC patients) were included, making this the world’s largest AI system validation study for triple-negative breast cancer.

TNBC identification performance:

• Internal cohort. AUC: 0.980 (95% CI: 0.958-0.996), Sensitivity: 0.963, Specificity: 0.857, Accuracy: 0.934.
• External cohort. AUCs: 0.916 (95% CI: 0.848-0.959), 0.936 (95% CI: 0.907-0.962), 0.860 (95% CI: 0.779-0.930), and 0.890 (95% CI: 0.841-0.929) in SDPH, SRRS, WHCH, and TCGA, respectively, which were significantly better than conventional AI models (MaxMIL, AttMIL).

Survival analysis performance:

• Disease-free survival. Internal C-index: 0.747±0.070 (95% CI: 0.617-0.852), external C-index: 0.731±0.047 and 0.732±0.043
• Overall survival. Internal C-index: 0.744±0.075 (95% CI: 0.602-0.865), external C-index: 0.720±0.034 and 0.721±0.030
• Kaplan-Meier analysis. The TRIP system can accurately divide patients into high-risk and low-risk groups. The difference between the two groups is statistically significant (P-values ​​< 0.0033), providing a clear risk stratification for clinical decision-making.

Interpretability

To address the “black box” problem of AI models, the TRIP system incorporates pathology heatmap visualization technology to visually mark tissue regions that are critical for diagnosis and prognosis. Pathologists found that the areas the system focuses on closely align with typical pathological features of triple-negative breast cancer. For example, in highly malignant cases, the heatmap highlights areas of nuclear atypia, tumor necrosis, and an immunosuppressive microenvironment; areas with abundant lymphoplasmacytic infiltration correlate with a better prognosis, which is generally consistent with clinical pathology.

To further validate the system’s reliability, the research team conducted the multi-omics analysis. Transcriptome data from 211 patients revealed 116 differentially expressed genes between high- and low-risk groups stratified by the TRIP system, and identified three molecular subtypes (C1-C3) with distinct immune and tumor-promoting signaling profiles. Among them, the C2 subtype showed significantly better prognosis than the C1 and C3 subtypes (P-values ​​< 0.05), due to its high expression of immune-related pathways (such as the interferon-γ response pathway). This result is highly consistent with the conclusions of internationally recognized TNBC molecular classification studies, confirming the scientific validity of the TRIP system’s prognostic assessment at the molecular level.

Clinical Benefits and Limitations

Clinical Benefits:
The successful development of the TRIP system brings multiple clinical benefits to the diagnosis and treatment of triple-negative breast cancer:

• Simplified diagnostic workflow: TNBC can be rapidly identified using only H&E slides, eliminating the need for IHC testing. This significantly reduces testing costs, shortens reporting times, and reduces the workload of pathologists, making it particularly suitable for resource-limited settings.
• Optimized treatment decisions: Accurate prognostic stratification helps clinicians identify high-risk patients and chosse tailored treatment plans. It also enables treatment de-escalation for low-risk patients, reducing over-medication.
• Improved equity in diagnosis and treatment: The system can be deployed on a workstation equipped with a single GPU with 12GB GPU memory, offering a low hardware threshold and facilitating its adoption in hospitals at all levels, helping to narrow the gap in diagnosis and treatment across different regions.

Limitations:
The current research data mainly comes from postoperative tissue samples, while clinical variables (such as age and TNM stage) are not yet included, limiting its direct application in preoperative scenarios. Moreover, further prospective studies can be conducted to verify the effectiveness of the system in core needle biopsy (CNB) samples; at the same time, clinical, imaging and genomic data can be integrated to build a multimodal AI model to continuously improve system performance.

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Resources

For more details, see the paper: Development and validation of an artificial intelligence system for triple-negative breast cancer identification and prognosis prediction: a multicentre retrospective study via EClinicalMedicine.

Citation:
X.M. Zhang, H.J. Zhou, Q. Chen, et al. Development and validation of an artificial intelligence system for triple-negative breast cancer identification and prognosis prediction: a multicentre retrospective study. EClinicalMedicine (2025). https://doi.org/10.1016/j.eclinm.2025.103557