Purpose: To investigate the performance of a narrow-scope knowledge-based RapidPlan (RP) model, for optimisation of intensity-modulated proton therapy (IMPT) plans applied to patients with locally advanced carcinoma in the gastroesophageal junction.
Methods: A cohort of 60 patients was retrospectively selected; 45 were used to 'train' a dose-volume histogram predictive model; the remaining 15 provided independent validation. The performance of the RP model was benchmarked against manual optimisation. Quantitative assessment was based on several dose-volume metrics.
Results: Manual and RP-optimised IMPT plans resulted dosimetrically similar, and the planning dose-volume objectives were met for all structures. Concerning the validation set, the comparison of the manual vs RP-based plans, respectively, showed for the target (PTV): the homogeneity index was 6.3 ± 2.2 vs 5.9 ± 1.2, and V98% was 89.3 ± 2.9 vs 91.4 ± 2.2% (this was 97.2 ± 1.9 vs 98.8 ± 1.1 for the CTV). Regarding the organs at risk, no significant differences were reported for the combined lungs, the whole heart, the left anterior descending artery, the kidneys, the spleen and the spinal canal. The D0.1 cm3 for the left ventricle resulted in 40.3 ± 3.4 vs 39.7 ± 4.3 Gy(RBE). The mean dose to the liver was 3.4 ± 1.3 vs 3.6 ± 1.5 Gy(RBE).
Conclusion: A narrow-scope knowledge-based RP model was trained and validated for IMPT delivery in locally advanced cancer of the gastroesophageal junction. The results demonstrate that RP can create models for effective IMPT. Furthermore, the equivalence between manual interactive and unattended RP-based optimisation could be displayed. The data also showed a high correlation between predicted and achieved doses in support of the valuable predictive power of the RP method.
Keywords: Intensity-modulated proton therapy; RapidPlan; knowledge-based planning; machine learning; oesophageal cancer.