Purpose: As outcomes improve in early-stage breast cancer, clinical trials are undergoing a paradigm shift from intensification trials (more therapy) to improve survival to optimization trials, which assess the potential for using less toxic therapy while preserving survival outcomes. However, little is known about perspectives in community and academic settings about possible barriers and facilitators that could affect accrual to optimization clinical trials and the generalizability of future findings.
Methods: We conducted a qualitative study with semistructured interviews of medical oncologists from different academic and community practices to assess their perspectives on optimization trials. Interviews were audio-recorded and transcribed. Three independent coders used a content analysis approach to analyze transcripts using NVivo. Major themes and exemplary quotes were extracted.
Results: All 39 physicians reported that they would enroll patients in optimization clinical trials. Oncologists highlighted specific reasons to consider optimization trials. These included quality-of-life improvement by reducing toxicity, reduction in financial toxicity, fertility preservation, ability to avoid chemotherapy, minimization of overtreatment in patients with comorbid conditions, personalized treatment, opportunities to test novel therapies, and leveraging the availability of targeted therapies. Oncologists also identified accrual barriers, such as tumor-specific biology, individual (host) factors, prognostic markers of risk, access to therapies, provider experience, and system constraints. They voiced recommendations regarding preliminary data, trial design, and tools to support enrollment in optimization trials.
Conclusion: Although oncologists are generally willing to enroll patients on optimization clinical trials, barriers affect their acceptance. A scientific focus on overcoming these barriers is needed to support future enrollment on trials tailoring therapy on the basis of risk and potential benefit to allow true personalization of treatment.
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