World Breast Cancer Research Day | August 18, 2026
World Breast Cancer Research Day 2026: Build a Research-to-Care Operating System
An executive evidence brief for making research participation reachable, evidence usable, learning visible, and every handoff from question to care accountable.
World Breast Cancer Research Day is observed on August 18. Organizers explain that the 18th day of the eighth month reflects the “1 in 8” breast cancer statistic and that the observance, founded in 2021, recognizes research across the past, present, and future. The official site does not identify a separate 2026 theme, so this brief does not invent one.
Treat research as a care-delivery capability, not a separate destination.
World Breast Cancer Research Day can become more than an annual message about scientific progress. It can be used as a leadership test: does the organization have a dependable route from a meaningful question to representative participation, trustworthy data, interpretable findings, adoption in practice, and visible learning for patients and communities? If any connection is weak, promising evidence can remain distant from the people it is meant to serve.
The familiar pipeline metaphor is incomplete. Research and care do not move in one direction. Questions arise in clinics and communities. Trials generate evidence under defined conditions. Routine care produces additional information about reach, experience, outcomes, and variation. Patients report what matters to them. Implementation work identifies why a proven practice does or does not fit a setting. Those observations should shape the next question. A research-to-care operating system therefore needs a loop, clear ownership, and a safe way for information to return.
Access is part of scientific quality. In a prospective cohort of 343 patients with breast cancer screened for therapeutic trials, Reh and colleagues examined eligibility and enrollment alongside race, rurality, and neighborhood disadvantage. The study helps separate two questions that are often collapsed: who was eligible, and who enrolled after becoming eligible.8 A review focused on Black women describes system, provider, and patient barriers to clinical trial participation.11 A mixed-methods study found substantial awareness of trials among Black respondents while also identifying concerns about harm, placebo, financial expense, and who initiated the discussion.9 The leadership implication is operational: measure the route before attributing nonparticipation to a person or community.
Representation is also part of generalizability. An analysis of trials supporting U.S. Food and Drug Administration approvals for breast cancer between 2010 and 2020 found that reporting of race and ethnicity remained incomplete and that Black patients were underrepresented relative to U.S. cancer incidence in the trials that reported those data.14 One analysis cannot describe every trial portfolio, and incidence is not the only possible denominator. It does show why leaders need an explicit representation plan, transparent denominator choices, and prospective review rather than a retrospective diversity statement.
The same discipline applies after a study closes. Implementation science addresses the long lag between evidence and routine practice. A practical guide for surgical oncologists explains how implementation questions, outcomes, and strategies differ from efficacy questions and uses a breast cancer short-stay program to illustrate the method.15 Real-world evidence can complement randomized trials, but a multidisciplinary breast cancer think tank emphasized the need for clinically relevant, patient-informed endpoints, consensus, rigorous frameworks, and examples that demonstrate value.6 Leaders should not ask only, “What did the study find?” They should ask, “For which decision, population, setting, and workflow is this evidence ready to use?”
A practical executive promise is demanding but clear: eligible people are offered a fair chance to learn about research; consent is informed and unpressured; participation barriers are found and addressed; data are complete enough to support the intended claim; findings are interpreted within their limits; and learning returns to practice and community partners through a named owner. The observance becomes credible when that promise is measured.
Executive starting point
Select one active breast cancer research pathway and trace it from question selection through eligibility review, offer, consent, participation, data completion, result interpretation, practice decision, and feedback to patients and community partners.
Use evidence by design, decision, population, and setting.
The 15-source evidence set for this brief spans 2022 through 2026 and includes prospective and retrospective cohorts, qualitative interviews, mixed-methods research, reviews, implementation studies, quality improvement, real-world evidence, a learning health system analysis, and a futures study of decentralized biobanking. Every source was available in full text and peer reviewed. The sources are listed newest first in the reference section.
The set cannot be pooled into a single effect estimate. It addresses different populations, interventions, outcomes, and systems. Some studies examine trial eligibility or enrollment. Others examine implementation of evidence-based care, use of patient-reported outcomes, real-world data, clinical analytics, biospecimen governance, or patient preferences. Figure 2 maps those studies to executive decisions without implying that position or color ranks evidence strength.
Trial participation studies provide distinct signals. Qualitative interviews with Black breast cancer survivors who had joined a trial found that participants described altruism, legacy, social connections, active information seeking, and shared decision-making as important influences.4 A separate mixed-methods study found awareness and positive beliefs alongside practical and safety concerns.9 A community-based breast cancer awareness project that shifted to remote methods during COVID-19 reported recruitment difficulty related to connectivity and digital health literacy.10 These findings do not support one standard recruitment message. They support multiple trusted routes, clear discussion of burdens and protections, and alternatives when digital access is unreliable.
Patient preferences should shape both research and implementation. A 2026 scoping review found substantial diversity within and across breast cancer preference studies and recommended linking preference work to clear objectives and implementation strategies while involving patients throughout the process.2 Preference evidence is not a shortcut for assuming what an individual patient wants. Its value is in showing which choices, tradeoffs, outcomes, and communication needs should be examined directly.
Implementation studies show why an intervention cannot be separated from its operating environment. A provider-focused process evaluation of alert-based patient-reported outcome monitoring in stage IV breast cancer identified the central role of study nurses, favorable views of the intervention, digital infrastructure limitations, workflow integration needs, and training requirements.1 An evaluation of Dutch exercise oncology trials using the RE-AIM framework examined reach, effectiveness, adoption, implementation, and maintenance rather than assuming that efficacy alone would produce routine access.13 A quality-improvement pilot on breast cancer specimen handling used a train-the-trainer strategy and examined retained knowledge and implementation outcomes after six months.5 Together, these studies support an implementation plan that specifies the people, technology, training, resource, feedback, and maintenance conditions needed for use.
| Evidence signal | Design and setting | Executive use | Transfer boundary |
|---|---|---|---|
| Eligibility and enrollment can differ across demographic and geographic conditions.8 | Prospective cohort at an NCI-designated cancer center | Separate screening, eligibility, offer, and enrollment measures. | One center and its trial portfolio do not define access everywhere. |
| Black women described practical, safety, communication, and trust-related influences on participation.49 | Qualitative interviews and mixed methods | Co-design recruitment, consent support, navigation, and burden reduction. | Qualitative and survey findings should not be assigned to every individual. |
| Alert-based patient-reported outcome monitoring depended on nursing roles, digital infrastructure, workflow fit, and training.1 | Provider process evaluation across German breast centers | Define alert ownership, response time, escalation, and system integration before launch. | Provider perspectives do not establish patient benefit or local feasibility. |
| Real-world evidence needs patient-informed endpoints, rigorous methods, and visible utility.6 | Multidisciplinary think tank and call to action | Create a claim review and endpoint governance process. | Consensus recommendations are not a regulatory approval or causal result. |
| A regional analytics platform characterized practice and showed different uptake of guideline changes.12 | Learning health system analysis of 5,768 patients | Use routine data to examine adoption, variation, and unanswered questions. | Data completeness, coding, policy, and context affect interpretation. |
| Patient preference research is diverse and needs a clear implementation purpose.2 | Scoping review | Define the decision, include patients early, and plan how findings will be used. | Group preference evidence cannot replace individual choice. |
Safe evidence use requires the claim, design, denominator, population, setting, time period, and limitation to travel together. A retrospective association can identify a pattern for investigation but not prove a cause. A qualitative study can explain possible mechanisms but not how common they are. A review can organize a field but inherits heterogeneity from included studies. A local improvement project can test feasibility without establishing broad effectiveness. Executives should require a claim-to-source ledger for dashboards, board materials, public statements, and implementation decisions.
Build one visible route from question to care and back.
The route starts before a protocol is written. Research questions should be connected to a defined care or community problem and shaped by the people affected. Leaders should know who participated in setting priorities, which outcomes matter to patients, what decision the proposed evidence could change, and what burden participation may create. A technically interesting endpoint without a clear decision use can consume time while leaving an operational gap untouched.
Feasibility review should include access. Map the eligible population, usual care locations, language needs, travel, scheduling, digital access, caregiving, cost exposure, and referral sources. Review whether inclusion and exclusion criteria are scientifically necessary. Forecast representation using more than a broad demographic target. Identify the denominator for every step: potentially eligible, screened, eligible, offered, consented, enrolled, retained, and complete for the outcome of interest.
Consent is a process, not a document event. People need understandable information about purpose, alternatives, burdens, possible benefits, uncertainty, privacy, voluntary participation, and withdrawal. Navigation should help with logistics without becoming pressure. A refusal should not reduce the quality of care. When a person is ineligible, the care team should explain the next clinical step and, when appropriate, whether another study-search route exists.
Data collection should be proportional to purpose. Define provenance, timing, permissible use, correction routes, missing-data review, quality controls, and who can see identifiable information. Patient-reported outcomes need a response plan when an answer indicates a concern. Biospecimen programs need governance that addresses consent, custodianship, access, future use, return of information, and community expectations. A futures study of decentralized biobanking used a breast cancer biobank to explore implementation, incentives, governance, ethical oversight, and the connection among patients, specimens, scientists, and physicians.3 The technology described in that study is not a universal solution. The transferable lesson is that specimen pathways are also relationship and governance pathways.
Analysis should answer the pre-specified question while preserving uncertainty and subgroup context. Interpretation should include clinicians, methodologists, operational leaders, patients, and community partners when their knowledge is relevant. The implementation decision should name who will act, where, by when, under which safeguards, and how uptake and unintended effects will be monitored. The final step is return: explain what was learned, what remains uncertain, and what changed to participants, care teams, and communities.
Put patient and community authority beside scientific authority.
Patient engagement is not a testimonial added after a protocol is complete. It is participation in question selection, outcome choice, burden review, recruitment language, consent design, data interpretation, dissemination, and implementation. The scope of authority should be explicit. Advisors need to know which decisions they can change, how disagreement will be documented, how conflicts are handled, and how leaders will report back.
Representation within governance matters. A single advisor cannot represent every age, stage, race, ethnicity, language, geography, disability, insurance status, gender identity, family structure, or experience of breast cancer. Build a portfolio of relationships and compensate contributors for expertise and time. Offer accessible scheduling, remote options, language support, and clear preparation. Avoid requiring disclosure of personal clinical history as the price of participation.
The scoping review of patient preference studies reinforces the need to connect engagement to a defined objective and implementation route.2 The qualitative study of Black trial participants found that social relationships, altruism, legacy, and active information seeking shaped participation experiences.4 Those findings should not be converted into persuasive scripts. They should prompt leaders to ask communities what trustworthy information, messengers, protections, and feedback they need.
Govern the communication lifecycle. Recruitment materials should state purpose, eligibility, alternatives, practical demands, contact routes, and protections in usable language. Result summaries should distinguish study findings from personal medical guidance. Public reports should disclose whose data are represented, who is missing, and which conclusions are not supported. When a study exposes a disparity or implementation gap, name the system response rather than using community characteristics as the explanation.
Close the governance loop with a decision log. Record the question raised, contributor group, recommendation, organizational decision, rationale, owner, due date, and feedback method. This protects against symbolic consultation and helps leaders see where patient input changed a study or care process. It also makes disagreement visible. Trust is not created by promising agreement. It is strengthened when organizations show how input was considered and what happened next.
Find where patients, sites, specimens, or findings disappear.
A final enrollment count can hide several kinds of loss. A potentially eligible person may never be screened because the trial list is hard to search. A clinician may not discuss a study because time is short or eligibility is uncertain. A coordinator may be unavailable when the patient is present. A person may decline because travel, work, caregiving, cost, digital access, or prior harm was not addressed. An enrolled participant may be lost when follow-up crosses care systems. Each transition needs a denominator and a named response owner.
Data and specimens can disappear functionally even when they remain stored. Missing subgroup fields can prevent representation analysis. Inconsistent staging or outcome definitions can make sites incomparable. Patient-reported outcome alerts can be collected without a reliable clinical response. Biospecimens can become separated from current consent or clinical context. Analytic code, versioned definitions, and data provenance can be missing when a result must be reproduced.
Findings can also disappear after publication. Teams may not know a relevant result exists. The intervention may require resources that were not described. Local data may not support the same population or workflow. No one may own the decision to adopt, adapt, test, or decline the practice. A guideline can be posted without changing order sets, staffing, training, referral capacity, or feedback. The learning health system study showed that uptake of different guideline or policy changes varied, which is precisely why adoption should be measured rather than assumed.12
Use several methods to investigate. Walk the pathway with deidentified cases. Observe clinic and coordinator workflows. Interview people who enrolled, declined, were ineligible, or never completed screening. Review referral timestamps and unresolved queues. Examine missingness by site and subgroup. Trace one analytic claim to its source data and code. Follow one evidence-based recommendation into policy, ordering, staffing, and actual use. Then validate the interpretation with the people who experience the process.
Do not begin with blame or a league table. A site with low enrollment may serve a population for whom the protocol creates more burden. A coordinator with slow follow-up may be covering an unsafe workload. A subgroup field may be missing because the interface does not permit self-identification. A patient-reported outcome alert may remain unanswered because no clinical role was authorized to respond. The purpose of measurement is to locate system work, assign authority, and test a safer route.
Make research navigation a dependable service.
Research access should not depend on whether a patient happens to see a clinician who remembers a specific study. Maintain an up-to-date, searchable inventory with plain-language purpose, key eligibility, sites, contact response standards, language availability, remote options, and expected participant demands. Connect the inventory to tumor boards, specialty clinics, navigation, community oncology, and referral partners. Define how a person without portal access can receive the same information.
Separate the offer from persuasion. A navigator can explain how research differs from routine care, help the person formulate questions, identify travel or scheduling needs, coordinate interpreter access, and clarify the next step. The navigator should not promise benefit, minimize uncertainty, or imply that participation is owed. People need time and, when desired, a trusted support person. A decline must lead back to a clear care plan.
Measure the offer. Trial enrollment alone cannot show whether access was fair. Review the proportion screened, found eligible, offered a study, contacted within the standard, consented, enrolled, and retained. Record why a transition did not occur using categories that patients and staff recognize, with an option for more than one cause. Do not use “patient refusal” as a complete explanation. Ask whether information, trust, burden, timing, cost, digital access, or care priorities shaped the decision.
The research on Black women’s participation illustrates why this matters. Participants in one study were more likely than health care providers to initiate trial discussions, and respondents identified both positive beliefs and substantial concerns.9 Another qualitative study emphasized social connections, legacy, and active shared decision-making among women who had participated.4 These findings do not prescribe one message. They challenge leaders to ensure that information is available before a patient must discover and raise a study alone.
Build the service beyond the flagship campus. Community oncology practices may identify eligible people but lack research staff. Create consultation, remote screening, referral, and shared-care routes that clarify responsibility. Review contracting, data exchange, specimen transport, adverse-event communication, and emergency plans. Provide feedback to the referring team instead of allowing the patient to become the messenger between systems.
Digital options can reduce some burdens and create others. Remote consent, telehealth, electronic outcomes, and home-based activities may help with travel or scheduling, but they can also exclude people with limited connectivity, devices, privacy, accessibility, or digital confidence. The community recruitment study conducted during COVID-19 described barriers to internet-based education in the target population.10 Offer supported and non-digital alternatives. Test every participant-facing tool with users before deployment.
Run a learning health system with explicit decision rights.
A learning health system connects routine care, research, analytics, improvement, and patient partnership. It does not mean that every data point is automatically research or that every association should change care. It means the organization can identify a question, assemble fit-for-purpose evidence, review uncertainty, make a governed decision, implement safely, evaluate the result, and generate the next question.
The breast cancer learning health system study used a regional data and analytics platform to characterize 5,768 patients, their treatments, outcomes, socioeconomic information, and uptake of selected clinical practice or policy changes.12 Its value for executives is not the transfer of local rates to another system. It is the demonstration that routine data can make care patterns and adoption visible when clinical experts, data infrastructure, and defined questions are connected.
Real-world evidence needs a governed use case. Ask whether the decision concerns effectiveness, safety, utilization, access, implementation, or hypothesis generation. Pre-specify the population, exposure, comparator, outcome, timing, confounders, missing-data approach, and sensitivity analyses. Document data provenance and transformations. Review whether the source data can measure the question reliably. Label exploratory findings and avoid turning association into treatment guidance.
Patient-reported outcomes add information that clinical records can miss, but collection creates responsibility. The 2026 provider study of alert-based monitoring identified study nurses as central to managing the system, engaging patients, and supporting continuity while also identifying infrastructure and workflow challenges.1 Before deployment, leaders should define which responses create alerts, who receives them, expected response time, backup coverage, documentation, escalation, patient instructions, and what happens after hours.
Decision rights prevent the loop from stalling. A research council can oversee scientific priority and portfolio. An ethics and privacy structure can govern consent, participant protection, secondary use, and disclosure. A data council can own definitions, provenance, quality, access, and analytic reproducibility. Clinical leaders can decide whether and how evidence enters practice. Operational leaders can resource workflow change. Patient and community partners can shape purpose, burden, interpretation, and feedback. One executive sponsor should resolve cross-functional barriers.
Maintain a decision register. For each finding, record the question, evidence set, population, setting, limitations, reviewers, decision, owner, safeguards, implementation route, review date, and status. “No change” can be a valid decision when evidence is insufficient or the local context differs, but the rationale should be visible. This discipline prevents selective adoption based on novelty or pressure.
Return learning in forms people can use.
Participants and communities often contribute time, information, specimens, and trust without seeing what happened next. A return-of-learning standard should define which studies provide lay summaries, when they are released, who receives them, how language and accessibility needs are met, and whom people can contact with questions. The summary should distinguish aggregate research findings from individual medical information and state what remains uncertain.
Care teams also need usable return. A publication alert is not implementation. Translate the finding into the decision it may affect, the population studied, required resources, safety considerations, workflow implications, and reasons the result may not transfer. Identify whether the next step is adoption, local testing, adaptation, further evidence review, or no change. Make the decision available to frontline teams and referral partners.
Community partners should receive more than a final slide deck. Report which priorities became questions, which recommendations changed protocol or workflow, who participated, where representation or data completeness fell short, and what leaders will do next. Invite interpretation before public claims are finalized. If a finding shows an inequity, pair it with the accountable system response and a review date.
Real-world evidence and routine analytics can help test what happened after implementation. The multidisciplinary RWE paper called for clinically relevant, patient-informed endpoints and stronger examples of utility.6 The learning health system study illustrates how care data can characterize practice and uptake.12 The implementation studies show that reach, adoption, feasibility, workflow, and maintenance deserve measurement alongside clinical outcomes.51315
Close the loop with the next question. If uptake differs by site, investigate workflow and resource conditions. If patient-reported outcome completion falls, examine burden and usability. If one group is underrepresented, trace screening, offer, consent, logistics, and retention. If a real-world result conflicts with a trial, review data quality, population, exposure, comparator, and confounding before choosing a response. A learning system is defined by the quality of its next question as much as by its last answer.
Use measures that trigger a decision.
A useful measure has a defined question, numerator, denominator, time window, exclusions, data source, owner, review cadence, subgroup plan, and response rule. Start with a small set that covers access, experience, evidence quality, implementation, and feedback. Do not publish a rate because it is easy to extract. Choose it because a team has authority to respond.
Pair counts with transitions. The number enrolled matters, but so do the proportions screened, eligible, offered, contacted, consented, retained, and complete. Pair speed with quality. Time to coordinator contact matters, but not if the discussion is inaccessible or incomplete. Pair adoption with outcome and burden. A new workflow may be used reliably while creating inequitable workload or excluding people who cannot use the digital route.
| Transition | Candidate measure | Decision owner | Response trigger |
|---|---|---|---|
| Question to protocol | Priority questions with documented patient or community input and a stated care decision | Research portfolio lead | Return proposals without a defined decision use or burden review. |
| Potentially eligible to screened | Screened within the defined window, with subgroup and site review | Clinical research operations | Investigate missed clinics, stale study lists, and unclear referral rules. |
| Eligible to offered | Eligible people with a documented, accessible offer | Site principal investigator | Review variation by clinician, site, language, and visit type. |
| Offer to informed decision | Decision completed after required information, interpreter, and navigator support | Consent and navigation lead | Audit delays, burden, pressure, and communication gaps. |
| Enrollment to complete data | Outcome completion and retention, with reasons for missingness | Study operations and data lead | Redesign burden, follow-up, technology, or shared-care handoffs. |
| Finding to decision | Actionable findings with a documented adopt, test, adapt, defer, or decline decision | Clinical evidence council | Escalate findings without an owner or review date. |
| Decision to sustained use | Reach, adoption, fidelity, adaptation, safety, burden, and maintenance | Clinical operations and quality | Reassess fit, resources, training, workflow, and unintended effects. |
| Study to feedback | Participants and partners receiving an accessible results update within the standard | Research communications lead | Resolve delayed, inaccessible, or one-way communication. |
Stratify with care. Representation review may include age, race, ethnicity, language, geography, disability, insurance, and other decision-relevant factors, but collection and reporting need consent, privacy, data-quality, and small-cell rules. Publish denominator definitions and missingness. Do not interpret a small difference as meaningful without uncertainty. Do not assign a cause to a group when the measure describes a system transition.
| Domain | Board-level question | Leading signal | Balancing signal |
|---|---|---|---|
| Access | Can eligible people reach an informed choice? | Screening, offer, response time, and navigation completion | Burden, complaints, declines after delay, and unresolved referrals |
| Representation | Does participation reflect the intended scientific population? | Transition rates by defined subgroup and site | Missing demographic data, small-cell risk, and unstable estimates |
| Data integrity | Can a claim be reproduced and bounded? | Required fields, provenance, versioned definitions, and code review | Manual work, correction volume, and unresolved discrepancies |
| Implementation | Did evidence change the intended workflow safely? | Reach, adoption, fidelity, and staff readiness | Workload, access shift, safety events, and unintended exclusions |
| Patient experience | Are participation and monitoring understandable and responsive? | Consent understanding, reported outcome response, and feedback delivery | Alert fatigue, survey burden, digital exclusion, and unanswered questions |
| Learning | Did the organization create and act on the next question? | Completed decisions, review cycles, and new improvement tests | Open findings without owners and repeated unresolved gaps |
Review measures with people who generate and experience them. Coordinators can explain why a timestamp is misleading. Clinicians can identify an eligibility field that arrives too late. Patients can distinguish a meaningful contact from an automated message. Data stewards can show where definitions change across systems. Community partners can identify language that implies deficit instead of system responsibility. The scorecard should become more accurate as it becomes more shared.
Use 90 days to prove the loop can close.
The goal of a 90-day cycle is not to redesign the entire research enterprise. It is to prove that one high-value route can become visible, governed, and responsive. Select a pathway with active leadership, a measurable access or implementation problem, enough volume to learn safely, and patient or community partners willing to shape the work.
During days 1 through 15, charter the work. Name the executive sponsor, operational lead, clinical lead, research lead, data steward, privacy and ethics contacts, patient or community partners, and decision authority. Define the population, care setting, study or evidence decision, success measures, exclusions, and safeguards. Preserve the current state before changing it.
During days 16 through 30, walk the route. Trace deidentified cases from discovery through feedback. Observe work across sites and shifts. Interview people who enrolled, declined, were ineligible, or never completed the next step. Map systems, handoffs, documents, alerts, and queues. Validate causes with the people doing the work.
During days 31 through 45, build the minimum reliable data view. Define denominators and timestamps. Repair one or two critical fields. Create a claim-to-source ledger and a small decision register. Establish protected subgroup review and missing-data rules. Do not build a broad dashboard before the questions and response owners are clear.
During days 46 through 70, test a small change. It may be a structured eligibility review, a navigator handoff, an interpreter-ready consent route, an alert-response protocol, a community oncology referral, a specimen chain-of-custody check, or an evidence-to-workflow decision review. Test with a limited group, monitor burden and safety, and make rapid adaptations.
During days 71 through 90, evaluate and return learning. Compare transition measures with the baseline, review subgroup patterns and missingness, document adaptations, and decide whether to continue, expand, revise, or stop. Provide an accessible update to patients, staff, sites, and community partners. Publish the next question and assign its owner.
The executive deliverable at day 90 should be concise: one current-state map, one tested future-state route, one defined scorecard, one decision register, one evidence or claim ledger, one patient and community feedback summary, and one documented next decision. If the loop did not close, the result is still useful when leaders can state exactly where it stopped and what authority or resource is required.
World Breast Cancer Research Day commitment
Choose one research-to-care pathway, make every transition visible, remove one avoidable access barrier, return one finding in a usable form, and publish the next accountable question.
Continue exploring related executive observances in the Health Observance Calendar, including World Lung Cancer Day 2026 and National Surgical Oncologist Day 2026.
References
The peer-reviewed, full-text sources below were reviewed for this executive brief and are ordered newest first. Database access links are omitted from the public article; DOI or PubMed links are provided when available.
- Breidenbach C, Hage AM, Schellack SK, et al. Providers' perspectives on implementing alert-based patient-reported outcome monitoring for stage IV breast cancer. Journal of Patient-Reported Outcomes. 2026;10(1):1-8. doi:10.1186/s41687-026-01106-0.
- Verbeke C, Schuermans F, Vanopré F, et al. Patient Preferences in Breast Cancer: A Scoping Review. Cancers. 2026;18(1):134. doi:10.3390/cancers18010134.
- Gross M, Dewan A, Sabharwal K, et al. Decentralized Biobanking Pathway to Precision Medicine: Futures Study. Journal of Medical Internet Research. 2025;27:e73965. doi:10.2196/73965.
- Ridley-Merriweather KE, Head KJ, Brann M, et al. “We Don't Get Drugs Targeted for Us:” Applying the Integrated Behavioral Model to Understand Why Black Women Chose to Participate in a Breast Cancer Clinical Trial. Health Communication. 2025;40(11):2280-2289. doi:10.1080/10410236.2024.2448701.
- Kornfeld H, Alem AT, Schmolze D, et al. Knowledge to action: a pilot quality improvement intervention on breast cancer specimen handling in operating rooms in Southern Ethiopia. Oncologist. 2025;30(7):1-7. doi:10.1093/oncolo/oyaf198.
- Khozin S, Dreyer NA, Galante D, et al. Real-World Evidence Acceptability and Use in Breast Cancer Treatment Decision-Making in the United States: Call-to-Action from a Multidisciplinary Think Tank. Advances in Therapy. 2025;42(7):2973-2987. doi:10.1007/s12325-025-03201-y.
- Brown C, Kang HA, Johnsrud M, et al. Assessing equity of care across metastatic breast cancer treatment junctures: a multi-site retrospective cohort study. BMC Cancer. 2025;25(1):861. doi:10.1186/s12885-025-14172-2.
- Reh N, Caston NE, Williams CP, et al. Therapeutic Clinical Trial Eligibility and Enrollment among Women with Breast Cancer: Implications for Understanding Trial Disparities. Annals of Surgical Oncology. 2025;32(3):2038-2044. doi:10.1245/s10434-024-16607-9.
- Fairley R, Lillard JW Jr, Berk A, et al. Increasing Clinical Trial Participation of Black Women Diagnosed with Breast Cancer. Journal of Racial and Ethnic Health Disparities. 2024;11(3):1701-1717. doi:10.1007/s40615-023-01644-z.
- Wiggins P, Robles-Rodriguez E, Jerome-D'Emilia B. Impact of COVID-19 on Recruitment in a Community-Based Breast Cancer Awareness Project. ABNFF Journal. 2024;1(1):70-74.
- Taye A, Elkhanany A, Stringer-Reasor E. Increasing inclusion and equity for Black women in breast cancer clinical trials. Clinical Advances in Hematology and Oncology. 2024;22(4):175-182. PMID:38739720.
- Levine MN, Kemppainen J, Rosenberg M, et al. Breast cancer learning health system: Patient information from a data and analytics platform characterizes care provided. Learning Health Systems. 2024;8(3):e10409. doi:10.1002/lrh2.10409.
- Ten Tusscher MR, Stuiver MM, Kampshoff CS, et al. Translating Evidence from Dutch Exercise Oncology Trials in Patients with Breast Cancer into Clinical Practice Using the RE-AIM Framework. European Journal of Cancer Care. 2023:2296881. doi:10.1155/2023/2296881.
- Nguyen RH, Silva Y, Lu J, et al. Race and Ethnicity Reporting and Enrollment Disparities in Clinical Trials Leading to FDA Approvals for Breast Cancer Between 2010 and 2020. Clinical Breast Cancer. 2023;23(6):591-597. doi:10.1016/j.clbc.2023.05.001.
- Park KU, Birken S, Garvin J, et al. Practical Guide to Implementation Science for Surgical Oncologists: Case Study of Breast Cancer Short Stay Program. Annals of Surgical Oncology. 2022;29(1):699-705. doi:10.1245/s10434-021-10479-z.