An Automated knowledge-based planning routine for stereotactic body radiotherapy of peripheral lung tumors via DCA-based volumetric modulated arc therapy

Purpose: To develop a knowledge-based planning (KBP) routine for stereotactic body radiotherapy (SBRT) of peripherally located early-stage non-small-cell lung cancer (NSCLC) tumors via dynamic conformal arc (DCA)-based volumetric modulated arc therapy (VMAT) using the commercially available RapidPlan^TM software. This proposed technique potentially improves plan quality, reduces complexity, and minimizes interplay effect and small-field dosimetry errors associated with treatment delivery. Methods: KBP model was developed and validated using 70 clinically treated high quality non-coplanar VMAT lung SBRT plans for training and 20 independent plans for validation. All patients were treated with 54 Gy in three treatments. Additionally, a novel k-DCA planning routine was deployed to create plans incorporating historical three-dimensional-conformal SBRT planning practices via DCA-based approach prior to VMAT optimization in an automated planning engine. Conventional KBPs and k-DCA plans were compared with clinically treated plans per RTOG-0618 requirements for target conformity, tumor dose heterogeneity, intermediate dose fall-off and organs-at-risk (OAR) sparing. Treatment planning time, treatment delivery efficiency, and accuracy were recorded. Results: KBPs and k-DCA plans were similar or better than clinical plans. Average planning target volume for validation was 22.4 ± 14.1 cc (7.1–62.3 cc). KBPs and k-DCA plans provided similar conformity to clinical plans with average absolute differences of 0.01 and 0.01, respectively. Maximal doses to OAR were lowered in both KBPs and k-DCA plans. KBPs increased monitor units (MU) on average 1316 (P < 0.001) while k-DCA reduced total MU on average by 1114 (P < 0.001). This routine can create k-DCA plan in less than 30 min. Independent Monte Carlo calculation demonstrated that k-DCA plans showed better agreement with planned dose distribution. Conclusion: A k-DCA planning routine was developed in concurrence with a knowledge-based approach for the treatment of peripherally located lung tumors. This method minimizes plan complexity associated with model-based KBP techniques and improve plan quality and treatment planning efficiency.