Cross-technique transfer learning for autoplanning in magnetic resonance imaging-guided adaptive radiotherapy for rectal cancer

Purpose: Automated treatment plan generation is essential for magnetic resonance imaging (MRI)-guided adaptive radiotherapy (MRIgART) to ensure standardized treatment-plan quality. We proposed a novel cross-technique transfer learning (CTTL)-based strategy for online MRIgART autoplanning.

Method: We retrospectively analyzed the data from 210 rectal cancer patients. A source dose prediction model was initially trained using a large volume of volumetric-modulated arc therapy data. Subsequently, a single patient's pretreatment data was employed to construct a CTTL-based dose prediction model (CTTL_M) for each new patient undergoing MRIgART. The CTTL_M predicted dose distributions for subsequent treatment fractions. We optimized an auto plan using the parameters based on dose prediction. Performance of our CTTL_M was assessed using dose-volume histogram and mean absolute error (MAE). Our auto plans were compared with clinical plans regarding plan quality, efficiency, and complexity.

Results: CTTL_M significantly improved the dose prediction accuracy, particularly in planning target volumes (median MAE: 1.27 % vs. 7.06 %). The auto plans reduced high-dose exposure to the bladder (D_0.1cc: 2,601.93 vs. 2,635.43 cGy, P < 0.001) and colon (D_0.1cc: 2,593.22 vs. 2,624.89 cGy, P < 0.001). The mean colon dose decreased from 1,865.08 to 1,808.16 cGy (P = 0.035). The auto plans maintained similar planning time, monitor units, and plan complexity as clinical plans.

Conclusions: We proposed an online ART autoplanning method for generating high-quality plans with improved organ sparing. Its high degree of automation can standardize planning quality across varying expertise levels, mitigating subjective assessment and errors.