Training and Validating a Knowledge-Based Model for Intensity Modulated Proton Therapy of Prostate and Pelvic Lymph Nodes

Introduction In this work, we aimed to create and assess the performance of a knowledge-based planning (KBP) model for optimizing intensity-modulated proton therapy (IMPT) in the treatment of prostate cancer involving pelvic lymph nodes (LNs). Materials and methods Fifty patients previously treated with IMPT to the prostate/prostate bed, including LNs and optional gross tumor volume (GTV) boost, were used for the training of a KBP model. The model was iteratively refined by replanning a subset of 20 of these patients. For validation, 20 patients not included in the model training set were used. Treatment plans were optimized using the objective list predicted by the model. Plan quality was evaluated using dosimetric metrics for both target and organs at risk (OARs), and the results were compared with manually generated plans using paired t-tests (p < 0.05). Results Eighteen out of 20 plans generated by the model were deemed to be clinically acceptable without the need for additional adjustments. The plans produced by the model demonstrated comparable robustness in clinical target volume (CTV) coverage. Significant improvements in OAR sparing were achieved for the rectum (V40Gy = -4.26 ± 3.00%), bladder (V40Gy = -6.36 ± 4.34%), and penile bulb (Dmean = -1.61 ± 9.76 Gy) when using the KBP model, compared to the manual plans. Other significant differences include slightly higher doses to the cauda equina (D0.03cc = 3.44 ± 6.09 Gy) and the left femur (D0.03cc = 2.50 ± 3.69 Gy) when compared to manual plans. No statistically significant differences were found for other OARs. Conclusions This study demonstrated that the KBP model produced plans comparable to manually generated clinical plans, and these plans are clinically acceptable. The iterative tuning process improved the quality of plans generated by the KBP model.