Data-driven tool to help oncologists identify high-risk periods for metastatic breast cancer patients

by University of North Carolina at Chapel Hill School of Medicine

edited by Swati Mestri, reviewed by Andrew Zinin

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Oncologists know that intensive treatment rarely helps patients in their final days, but recognizing when someone is entering that stage is difficult, especially in metastatic breast cancer, where an increasing number of effective treatments has helped prolong life and given patients and oncologists more treatment options. Physicians often overestimate survival, and uncertainty can delay important conversations and supportive care.

New findings published in JCO Oncology Practice describe a regression-based model that uses routinely collected clinical data to estimate the risk of near-term death for patients with metastatic breast cancer (MBC).

The model aims to help oncologists recognize when patients are entering a high-risk period and prompt timely discussions about care needs and preferences.

Emily Ray, MD, MPH, a medical oncologist at UNC Lineberger Comprehensive Cancer Center and associate professor in the UNC School of Medicine's Division of Oncology, led the effort to develop the tool. It draws on information readily available in the electronic health record, such as laboratory results, vital signs, breast cancer subtype and medications, to estimate the probability of death within 30 or 90 days.

"In our prognostic model, the variables most closely correlated with increased risk align with signals clinicians already recognize, like worsening liver function, increased heart rate, rising needs for pain medication and declining ability to care for oneself at home," Ray said.

"Any clinician could tell you those are signs that a patient is getting sicker, but sometimes we still miss them and don't do enough to prepare patients and families. Our model reinforces that clinical intuition and could help oncologists better recognize these patterns and act on them to arrange more support for patients and caregivers."

Unlike existing tools that combine multiple cancer types and often underrepresent breast cancer, this model is specific to metastatic breast cancer and is derived from a national oncology database with real-world data. Researchers say it can help identify high-risk periods even for patients who have lived a long time with MBC and could be implemented in routine practice to support better end-of-life care.

"We hope that this tool will nudge oncologists to have conversations with their patients that are sometimes hard to have due to time constraints, emotional weight and sometimes patient reluctance to talk about these tough topics," Ray said.

"We hope it sparks conversations and actions to ensure that we are caring for our patients in the best possible way, one that honors their preferences and supports them as a whole person."

Ray added that even for patients who prefer not to discuss prognosis, the model may encourage oncologists to review treatment options, involve palliative care specialists when appropriate and ensure that care plans align with patients' values and goals. The team plans future studies to understand how best to integrate the model into practice and how it affects decision-making for oncologists, patients and caregivers.

More information

Emily M. Ray et al, Development and Validation of a Metastatic Breast Cancer–Specific Prognostic Model Using CancerLinQ Discovery, JCO Oncology Practice (2026). DOI: 10.1200/op-25-00610

Key medical concepts

metastatic breast cancerPalliative Care

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Oncology Provided by University of North Carolina at Chapel Hill School of Medicine Who's behind this story?

Swati Mestri

Swati Mestri holds a bachelor's degree in Electronics Engineering and has worked as a content editor since 2019. She has experience editing research documents across technology, health care, and materials science, and has a particular interest in technology and space. Full profile →

Andrew Zinin

Master's in physics with research experience. Long-time science news enthusiast. Plays key role in Science X's editorial success. Full profile →

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