OVH‐guided planning for superior heart and lung sparing in breast cancer radiotherapy

Background and purpose Manual planning in breast cancer radiotherapy is often time‐consuming and operator‐dependent, leading to inconsistencies in plan quality. This study validated an automated workflow using overlap volume histograms (OVH) to predict patient‐specific dose–volume histogram (DVH) constraints, aiming to enhance cardiopulmonary sparing and planning efficiency. Materials and methods A historical database of 322 patients was stratified into four groups: left/right post‐mastectomy radiotherapy (PMRMRT) and left/right breast‐conserving radiotherapy (BCRT). Linear regression models were established to correlate OVH‐derived geometric metrics ( L x ) with corresponding DVH‐based dose constraints ( D x ). These predictive models were integrated into the Monaco treatment planning system via a custom Python script to provide an improved automated planning workflow. The workflow's performance was prospectively validated on 80 independent testing cases (20 per group). Automated plans were generated using the predicted constraints and compared dosimetrically against clinically approved manual plans. Results Significant linear correlations were observed between L x and D x for all OARs ( r 2 = 0.51–0.72, p < 0.001). In the PMRMRT testing cohorts, the automated workflow significantly reduced doses to the heart and ipsilateral lung compared to manual planning ( p < 0.05). For left‐sided PMRMRT, the heart dose was reduced by 15.6% ( D 10 ), 18.7% ( D 5 ), and 9.8% ( D mean ), while the ipsilateral lung dose decreased by up to 6.3% ( D mean ). In BCRT cases, automated plans were not significant improved compared to manual plans. Importantly, all automated plans maintained target volume coverage and dose homogeneity comparable to manual plans ( p > 0.05). Conclusion The OVH‐based framework effectively translated anatomy into achievable objectives, significantly improving heart and lung sparing for complex PMRMRT cases while streamlining clinical workflows.