Balancing survival and quality of life: The evolving role of AI-enabled pathology in personalized cancer care

Traditional oncology metrics have long measured success in survival curves and hazard ratios, not in days spent comfortably with family or with autonomy intact. Yet, a recent study from JAMA Oncology¹ offers insight into what truly matters to patients. The study found that less than 1 in 10 older adults with advanced cancer prioritized extending survival over maintaining quality of life (QoL). 

Achieving outcomes that matter to patients depends on understanding the relationship between treatment preferences and results. While healthcare has traditionally lacked visibility into how different treatment preferences impact outcomes, there is a wealth of data embedded within routinely collected histopathology slides, data that artificial intelligence (AI) is now beginning to unlock. Pathology laboratories have traditionally focused on diagnostics, but as treatment options have become more advanced and complex, clinicians and patients often face uncertainty in determining the optimal plan. With the rise of computational pathology and AI, laboratory professionals are now uniquely positioned to analyze the wealth of data embedded in routine histopathology slides and generate actionable insights that help guide personalized, patient-centered care.

In fact, labs are the primary drivers of AI integration in pathology, representing the largest segment of the AI pathology market with ~62%³ of projected market share. This leadership positions laboratories not only as innovators in analytics but also as contributors to the longitudinal data needed to evaluate how treatment decisions impact patient priorities and QoL.

From lab to life

As laboratories increasingly adopt AI, they can help clinicians and patients interpret complex data to guide personalized treatment decisions, with the potential to improve patient outcomes and preserve QoL. This is especially valuable in oncology, where disease heterogeneity, variable patient risk profiles, and complex treatment options make it difficult to balance effective therapy with minimizing side effects and maintaining patients’ daily lifestyle.

For example, in prostate cancer, decisions are influenced by a broad range of factors, including disease risk, patient comorbidities, age, and personal preferences. The ArteraAI Prostate Test² illustrates how labs can contribute to a personalized approach. Artera’s test enables laboratories to analyze digitized biopsy images alongside clinical data to produce prognostic and predictive insights. Clinicians can then use these insights to personalize treatment decisions. This can include considering active surveillance for low-risk patients or evaluating the potential benefits and risks of therapies such as androgen deprivation therapy (ADT) for intermediate-risk patients.

ADT highlights why QoL is central in treatment discussions. While effective for the treatment of prostate cancer in some patients, ADT is associated with physical side effects,⁴ including fatigue, sexual dysfunction, hot flashes, metabolic changes, loss of bone density, and increased cardiovascular risk, all of which can meaningfully disrupt daily functioning and well‑being. Clinical evidence suggests that for many men with low- or favorable intermediate-risk prostate cancer, the benefit from ADT is minimal and radiotherapy alone can provide effective disease control, allowing patients to safely forgo ADT.⁵ By identifying those who may not need hormone therapy, clinicians can help preserve both health and QoL, aligning care decisions with patient priorities.

From data to decisions

We are fortunate to be in an era where oncology increasingly moves beyond survival alone to focus on life after treatment. With nearly 18.6 million⁶ Americans living with a history of cancer (and more patients living longer than ever before), treatment decisions are no longer solely about survival. Advanced data-driven insights now empower clinicians and patients to make more informed choices, weighing both clinical outcomes and the potential impact on daily living, physical function, and long-term well-being. For the growing population of cancer survivors, these insights help ensure that living longer also means living well.

References

1. Richardson DR, Wang Y, Flannery M, et al. Outcomes of older adults with advanced cancer who prefer quality of life vs prolonging survival: A secondary analysis of the GAP70+ cluster randomized clinical trial. JAMA Oncol. 2026:e260072. doi:10.1001/jamaoncol.2026.0072.
2. Our clinically available, laboratory-developed test. Artera. Accessed April 10, 2026. https://artera.ai/arteraai-prostate-cancer-test.
3. AI in pathology market size, share & industry analysis, by component (hardware, software, and services), by technology (machine learning, natural language processing, and others), by application (disease diagnostics, drug discovery & development, clinical workflow management, and others), by indication (oncology, infectious diseases, inflammatory & autoimmune disorders, gastrointestinal diseases, and others), by end user (diagnostic laboratories, pharmaceutical & biotechnology companies, and others), and regional forecast, 2026-2034. Fortune Business Insights. March 23, 2026. Accessed April 10, 2026. https://www.fortunebusinessinsights.com/ai-in-pathology-market-111753#.
4. Hormone therapy for prostate cancer. National Cancer Institute. Accessed April 10, 2026. https://www.cancer.gov/types/prostate/prostate-hormone-therapy-fact-sheet.
5. Spratt DE, Sun Y, Van der Wal D, et al. An AI-derived digital pathology-based biomarker to predict the benefit of androgen deprivation therapy in localized prostate cancer with validation in NRG/RTOG 9408. J Clin Oncol. 2022;40(6_suppl):223-223. doi:10.1200/jco.2022.40.6_suppl.223.
6. Cancer treatment and survivorship facts & figures. American Cancer Society. Accessed April 10, 2026. https://www.cancer.org/research/cancer-facts-statistics/survivor-facts-figures.html?.