AI-enabled D-dimeromics may identify ultra-high-risk breast cancer phenotypes for individualized treatmentAI and D-dimer may spot high-risk breast cancer

Frontiers in Medicine Published September 4, 2026 DOI ↗ Editorial oversight: Dr. Julia Lee, PhD · Oncology, Genomics & Drug Development

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Key Takeaway

Consider D-dimeromics as a conceptual tool for risk stratification, but await clinical validation.

This narrative review discusses the emerging concept of AI-enabled D-dimeromics in breast cancer. The authors propose integrating D-dimer levels with haemostatic, inflammatory, molecular, radiological, and clinical data to create a multidimensional biomarker profile. They argue that D-dimer itself signals tumour aggressiveness, disease extent, and unfavorable clinical results, making it a valuable biomarker in oncology.

The review suggests that AI-driven analysis of these combined data can identify predictive patterns that exceed the capabilities of conventional statistical methods. This approach may lead to the identification of ultra-high-risk breast cancer phenotypes, improve risk assessment, and enhance metastatic prediction. The authors also highlight potential applications in treatment response evaluation and the development of individualized treatment strategies.

As a narrative review, this article does not present original clinical trial data. The limitations are not reported, and the evidence is based on the authors' synthesis of existing literature and conceptual reasoning. The potential of D-dimeromics is promising, but clinical validation through prospective studies is needed before it can be integrated into routine practice.

For clinicians, this review offers a forward-looking perspective on how AI and biomarker combinations might refine breast cancer management. However, the lack of empirical data means that these concepts should be interpreted cautiously and not yet applied in clinical decision-making.

How this fits prior evidence

This narrative review introduces a novel conceptual framework that complements prior coverage on breast cancer risk and treatment. While previous findings focused on specific interventions (e.g., CDK4/6 inhibitors and hepatotoxicity, trolamine and skin toxicity) and risk factors (ionizing radiation, surgical delays, PFAS), this review addresses a gap by proposing a dynamic, AI-integrated biomarker approach for risk stratification. It extends the discussion from single biomarkers to a multidimensional D-dimeromics model, potentially offering a more personalized strategy. However, it does not provide empirical data to compare with the quantitative findings from prior studies.

A new review explores a concept called AI-enabled D-dimeromics for breast cancer. This approach combines a blood marker called D-dimer with other health data, such as clotting, inflammation, and imaging details. The goal is to use artificial intelligence to find patterns that might identify women with ultra-high-risk breast cancer.

The review did not involve new patients or a clinical trial. Instead, it looked at existing research and described a potential framework. The authors suggest that D-dimer levels may signal how aggressive a tumor is and how far the disease has spread. They also believe AI could help predict metastasis and guide treatment choices.

Because this is a narrative review, it is not proof that this approach works. No safety issues were reported, but that is because no patients were treated. The main limitation is that this is an early idea, not a ready-to-use test.

For now, this is a promising direction for future research. Patients should not expect this to change their care immediately. Anyone with questions about breast cancer risk or treatment should talk to their doctor.

What this means for you:

AI and D-dimer might help identify aggressive breast cancer, but it's early research, not ready for patients.

Common questions

What is D-dimer and how is it related to breast cancer?

D-dimer is a blood marker that is normally used to check for blood clots. In this review, researchers suggest that D-dimer levels might also signal how aggressive breast cancer is and how far it has spread. However, this is an early idea, not a proven test.

Is this AI approach ready for patients?

No. This is a narrative review, meaning it describes a concept, not a clinical trial. The authors discuss the potential of AI to find patterns, but there is no proof yet that it works in real patients. More research is needed before it could be used in clinics.

What are the potential benefits of AI-enabled D-dimeromics?

The review suggests it could help identify ultra-high-risk breast cancer, provide dynamic risk assessment, predict metastasis, and guide individualized treatment. But these are possibilities, not confirmed outcomes. Patients should rely on current standard care and discuss any questions with their doctor.

Study Details

Study typeSystematic review

EvidenceLevel 1

PublishedSep 2026

View Original Abstract ↓

Breast cancer is a highly diverse ailment marked by various molecular subtypes, differing clinical paths, and unique treatment reactions. Notwithstanding considerable progress in precision oncology, the prompt detection of patients with ultra-high-risk breast cancer phenotypes continues to be a significant clinical hurdle. Growing evidence suggests that hypercoagulability linked to cancer and thromboinflammation are vital in tumour advancement, metastatic spread, evasion of immunity, and resistance to treatment. D-dimer has become a notable biomarker for coagulation, signalling tumour aggressiveness, disease extent, and unfavorable clinical results. Nonetheless, traditional D-dimer evaluation depends on singular assessments that do not reflect the intricate biological interactions influencing breast cancer advancement. To overcome this limitation, the idea of D-dimeromics has arisen as a comprehensive framework that combines D-dimer with additional haemostatic, inflammatory, molecular, radiological, and clinical data. Simultaneously, progress in artificial intelligence (AI), particularly in machine learning and deep learning, has facilitated the examination of high-dimensional biomedical data and the identification of predictive patterns that exceed the capabilities of conventional statistical methods. This narrative review examines the biological justifications, technological bases, and clinical uses of AI-driven D-dimeromics as a groundbreaking precision oncology approach for detecting ultra-high-risk breast cancer phenotypes. The article examined the connections between coagulation activation and tumour development, the prognostic value of D-dimer in various breast cancer subtypes, and the function of AI in combining multidimensional data for dynamic risk assessment, metastatic prediction, treatment response evaluation, and individualized treatment strategies.

AI-enabled D-dimeromics may identify ultra-high-risk breast cancer phenotypes for individualized treatmentAI and D-dimer may spot high-risk breast cancer

How this fits prior evidence

Common questions

Study Details

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AI-enabled D-dimeromics may identify ultra-high-risk breast cancer phenotypes for individualized treatmentAI and D-dimer may spot high-risk breast cancer

How this fits prior evidence

Common questions

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