One of the advantages of integrating automated processes in treatment planning is the reduction of manual planning variability. This study aims to assess whether a deep-learning-based auto-planning solution can also reduce the contouring variation-related impact on the planned dose for early-breast cancer treatment.
Auto- and manual plans were optimized for 20 patients using both auto- and manual OARs, including both lungs, right breast, heart, and left-anterior-descending (LAD) artery. Differences in terms of recalculated dose (ΔD _rc ^M ,ΔD _rc ^A ) and reoptimized dose (ΔD _ro ^M ,ΔD _ro ^A ) for manual (M) and auto (A)-plans, were evaluated on manual structures. The correlation between several geometric similarities and dose differences was also explored (Spearman's test).
Auto-contours were found slightly smaller in size than manual contours for right breast and heart and more than twice larger for LAD. Recalculated dose differences were found negligible for both planning approaches except for heart (ΔD _rc ^M =-0.4 Gy, ΔD _rc ^A =-0.3 Gy) and right breast (ΔD _rc ^M =-1.2 Gy, ΔD _rc ^A =-1.3 Gy) maximum dose. Re-optimized dose differences were considered equivalent to recalculated ones for both lungs and LAD, while they were significantly smaller for heart (ΔD _ro ^M =-0.2 Gy, ΔD _ro ^A =-0.2 Gy) and right breast (ΔD _ro ^M =-0.3 Gy, ΔD _ro ^A =-0.9 Gy) maximum dose. Twenty-one correlations were found for ΔD _rc ^M,A (M=8,A=13) that reduced to four for ΔD _ro ^M,A (M=3,A=1).
The sensitivity of auto-planning to contouring variation was found not relevant when compared to manual planning, regardless of the method used to calculate the dose differences. Nonetheless, the method employed to define the dose differences strongly affected the correlation analysis resulting highly reduced when dose was reoptimized, regardless of the planning approach.