GUIDE-T: A three-tier, markmap-driven platform for streamlined breast cancer trial navigation and improved enrollment.

Abstract

e23022 Background: Clinical trial enrollment in Breast Cancer (BC) remains challenging due to frequent changes in protocol status, extensive eligibility criteria, and reliance on fragmented data. These barriers can delay patient access to novel therapies and undermine accrual efforts. GUIDE-T was developed at Baylor College of Medicine (BCM) to centralize active BC trial information, generate intuitive MarkMap visualizations, and interface with BCL local enrollment tracker (ENROL). Updated weekly, GUIDE-T aims to simplify trial search for clinicians, coordinators, and patient advocates while facilitating faster equitable patient enrollment. Methods: GUIDE-T (https://elkhanany.github.io/bcm_trials_breast) employs a three-tier architecture. First, a secure, BCM-specific Google Spreadsheet is maintained weekly to reflect trial status changes (open, on hold, closed), inclusion/exclusion criteria, and relevant contacts. Second, a Python-based pipeline retrieves spreadsheet entries, normalizes text via regex-based feature extraction, and flags duplicate entries through standardized eligibility checks. Finally, the MarkMap library translates this data into an interactive dendrogram, allowing users to expand nodes by BC subtype, treatment line, and timing of therapy. Results: Since implementation, GUIDE-T has logged more than 300 patient screenings in ENROL, with increased efficiency in trial matching. Preliminary real-world data show a 50% rise in accrual rates, from approximately 8% to 16%, compared with the same period last year. Notable benefits include instant visualization of open protocols, rapid identification of key eligibility criteria, and convenient web access for providers, coordinators, and patients. Advocates input highlighted the plain-language design and ability to explore active trials independently. Conclusions: GUIDE-T addresses persistent obstacles in BC trial enrollment by merging automated data curation, structured visualization, and real-time enrollment tracking. Its three-tier structure ensures accurate updateed trial listings while reducing manual protocol reviews. Although currently employed within a single academic network, the platform’s scalability offers promising avenues for broader deployment, including other tumor types, AI-assisted patient matching, and integration with research infrastructures. GUIDE-T supports efforts to enhance accrual rates and foster equitable, evidence-based care in BC.