Replacing manual planning with automatic iterative planning for locally advanced rectal cancer VMAT treatment

Purpose: To develop and implement a fully automatic iterative planning (AIP) system in the clinical practice, generating volumetric-modulated arc therapy plans combined with simultaneous integrated boost technique VMAT (SIB-VMAT) for locally advanced rectal cancer (LARC) patients. Method: The designed AIP system aimed to automate the entire planning process through a web-based service, including auxiliary structure generation, plan creation, field configuration, plan optimization, dose calculation, and plan assessment. The system was implemented based on the Eclipse scripting application programming interface and an efficient iterative optimization algorithm was proposed to reduce the required iterations in the optimization process. To verify the performance of the implemented AIP system, we retrospectively selected a total of 106 patients and performed dosimetric comparisons between the automatic plans (APs) and the manual plans (MPs), in terms of dose-volume histogram (DVH) metrics, homogeneity index (HI), and conformity index (CI) for different volumes of interest. Result: The AIP system has successfully created 106 APs within clinically acceptable timeframes. The average planning time per case was 36.8 +/- 6.5 min, with an average iteration number of 6.8 (+/- 1.1) in plan optimization. Compared to MPs, APs exhibited better performance in the planning target volume conformity and hotspot control (p<0.001$p < 0.001$). The organs at risk (OARs) sparing was significantly improved in APs, with mean dose reductions in the femoral heads, the bone marrow, and the SmallBowel-Avoid of 0.53 Gy, 1.18 Gy, and 1.00 Gy, respectively (p<0.001$p < 0.001$). Slight improvement was also observed in the urinary bladder V40Gy${{V}_{40{\mathrm{\ Gy}}}}$ and the small bowel D2cc(p<0.001)${{D}_{2{\mathrm{\ cc}}}}\ (p < 0.001)$. Additionally, quality variation between plans from different planners was observed in DVH metrics while the APs represented better plan quality consistency. Conclusion: An AIP system has been implemented and integrated into the clinical treatment planning workflow. The AIP-generated SIB-VMAT plans for LARC have demonstrated superior plan quality and consistency compared with the manual counterparts. In the meantime, the planning time has been reduced by the AIP approach. Based on the reported results, the implemented AIP framework has been proven to improve plan quality and planning efficiency, liberating planners from the laborious parameter-tuning in the optimization phase.