Retrospective Analysis of a WashU-Based Knowledge-Based Planning (KBP) Model for Breast and Chest Wall Planning in Guatemala: Compliance With American Society for Radiation Oncology (ASTRO) 2026 Guidelines.

内容摘要

Background/purpose Breast cancer is among the most prevalent malignancies treated at the Liga Nacional Contra el Cáncer (LNCC) in Guatemala, representing a significant proportion of annual radiotherapy cases. Access to high-quality, standardized treatment planning in resource-constrained settings remains a critical challenge. This study evaluates the dosimetric performance of knowledge-based planning (KBP) models adapted from Washington University (WashU) in St. Louis for breast and chest wall radiotherapy at LNCC, validated against a retrospective 2025 clinical cohort, and benchmarked against the ASTRO 2026 Practical Radiation Oncology guidelines. Materials and methods A retrospective analysis of 84 treatment plans (40 left, 44 right) for whole-breast or chest-wall treatment with regional nodal involvement was performed. All patients were treated using volumetric modulated arc therapy (VMAT) under a moderate hypofractionation scheme (40.05 Gy in 15 fractions). KBP models were developed during 2022-2024 using Eclipse V18.0 (Varian Medical Systems, Palo Alto, CA) RapidPlanTM, originally built at Washington University, and augmented with 194 left-breast and 103 right-breast cases from LNCC. Model validity was confirmed with Varian's model analytics tool. Dosimetric metrics for the planning target volume (PTV) and organs at risk (OARs) were extracted using a custom ESAPI (the OWASP enterprise security application programming interface) application and compared against ASTRO 2026 Table 3 benchmarks, categorized as recommended (green), acceptable (yellow), or unacceptable (red). Results PTV coverage was adequate, with an average V95% of 97.3% ± 1.8%; V90% of 99.7% ± 0.4 (right), and average V95% of 96.6%±3.8%; V90% of 99.0%, ± 2.8% (left). Most plans met ASTRO's recommended range. The heart mean dose was well-controlled, with median values of 2.4 Gy (right) and 4.2 Gy (left). Ipsilateral lung V18Gy showed a median of 18.6% (right) and 17.7% (left), and V10Gy of 33.3% (right) and 31.6% (left), both within acceptable ranges. Spinal cord D0.035cc had a median of 8.3 Gy (right) and 10.1 Gy (left), well below neurological tolerance thresholds. Contralateral breast D10% had median doses of 2.6 Gy (right) and 2.9 Gy (left), with ranges of 1.7-3.1 Gy and 2.2-5.4 Gy, respectively. KBP model analytics validation confirmed institutional model statistics fell within acceptable quality benchmarks across all evaluated structures. Conclusion KBP models developed at a high-income institution and iteratively refined with local data can be successfully deployed for breast and chest wall radiotherapy in a resource-constrained low- and middle-income country (LMIC) setting, achieving dosimetric outcomes consistent with ASTRO 2026 guidelines. The temporal separation between model training (2022-2024) and retrospective validation (2025) further confirms the models' robustness and generalizability. This approach supports standardized, high-quality treatment planning at scale, contributing to more equitable access to radiotherapy for breast cancer patients.