Dose-Guided Automatic IMRT Planning: A Feasibility Study

Purpose: To develop and evaluate an automatic IMRT planning technique for prostate cancer utilizing prior expert plan's dose distribution as guidance.

Methods and Materials: In this study, the anatomical information of prostate cancer cases was parameterized and quantified into two measures: the percent distance-to-prostate (PDP) and the concaveness angle. Based on these two quantities, a plan atlas composed of 5 expert prostate IMRT plans was built out of a 70-case pool at our institution using k-medoids clustering analysis.

Extra 20 cases were used as query cases to evaluate the dose-guided automatic planning (DAP) scheme. Each query case was matched to an atlas case based on PTV-OAR anatomical features followed by deformable registration to enhance fine local matching. Using the deformation field, the expert dose in the matched atlas case was warped onto the query case, creating the goal dose conformal to the query case's anatomy. Dose volume histograms (DVHs) objectives were sampled from the goal dose to guide automatic IMRT treatment planning. Dosimetric comparison between DAP plans and clinical plans were performed.

Results: Generating goal dose is highly efficient by using MIM^TM workflows. The deformable registration provides high-quality goal dose tailored to query case's anatomy in terms of the dose falloff at the PTV-OAR boundary and the overall conformity. Automatic planning in Eclipse^TM takes ~2.5 min (~70 iterations) without human intervention. Compared to clinical plans, DAP plans improved the conformity index from 0.85±0.04 to 0.88±0.02 (p=0.0045), the bladder-gEUD from 40.7±3.2 Gy to 40.0±3.1 Gy (p=0.0003), and rectum-gEUD from 40.4±2.0 Gy to 39.9±2.1 Gy (p=0.0167). Other dosimetric parameter is similar (p>0.05): homogeneity indices are 7.4±0.9% and 7.1±1.5%, for DAP plans and clinical plans, respectively.

Conclusions: Dose-guided automatic treatment planning is feasible and efficient. Atlas-based patient-specific dose objectives can effectively guide the optimizer to achieve similar or better plan quality compared to clinical plans.