A comprehensive dose-volume histogram-based index for radiotherapy treatment plan quality evaluation: application to breast cancer radiotherapy

Abstract

Advances in radiotherapy have increased treatment plan complexity, making manual quality evaluation more subjective and variable. While deep learning approaches offer automation in planning, evaluation remains a manual bottleneck. Existing indices evaluate individual aspects in isolation and lack a unified perspective. To address this, we developed the advanced dose distribution index (ADDI), a comprehensive dose-volume histogram- and biological dose response-based metric for automation-friendly quality evaluation. ADDI summarizes treatment plan quality as a polar vector, ADDI(r, θ), where the magnitude (r) reflects overall quality and the angle (θ) captures the balance between target and organ-at-risk dose. We validated ADDI across four breast cancer groups involving whole-breast irradiation with and without regional nodal irradiation for both lateralities, comprising 778 patients. ADDI demonstrated significant differences between groups, with left-sided and node-inclusive treatments having worse scores (i.e. higher dose exposure) than right-sided and node-exclusive cases. Additionally, ADDI responded to anatomical features such as planning target volume size, cardiothoracic ratio, central lung distance, and maximal heart distance, highlighting its sensitivity to patient-specific anatomical factors. These results indicate that ADDI effectively captures clinically meaningful variations in dose distribution and provides a unified, objective tool that is compatible with both automated and manual treatment planning workflows.