Generating deliverable DICOM RT treatment plans for prostate VMAT by predicting MLC motion sequences with an encoder-decoder network

BackgroundDeep learning-based auto-planning is an active research field; however, for some tasks a treatment planning system (TPS) is still required. PurposeTo introduce a deep learning-based model generating deliverable DICOM RT treatment plans that can be directly irradiated by a linear accelerator (LINAC). The model was based on an encoder-decoder network and can predict multileaf collimator (MLC) motion sequences for prostate VMAT radiotherapy. MethodsA total of 619 treatment plans from 460 patients treated for prostate cancer with single-arc VMAT were included in this study. An encoder-decoder network was trained using 465 clinical treatment plans and validated on 77 plans. The performance was analyzed on a separate test set of 77 treatment plans. Separate L1 losses were computed for the leaf and jaw positions as well as the monitor units, with the leaf loss being weighted by a factor of 100 before being added to the other losses. The generated treatment plans were recalculated in a treatment planning system and the dose-volume metrics and gamma passing rates were compared to the original dose. ResultsAll generated treatment plans showed good agreement with the original data, with an average gamma passing rate (3%/3 mm) of 91.9 +/- 7.1%. However, the coverage of the PTVs. was slightly lower for the generated plans (D-98% = 92.9 +/- 2.6%) in comparison to the original plans (D-98% = 95.7 +/- 2.2%). There was no significant difference in mean dose to the bladder between the predicted and original plan (D-mean of 28.0 +/- 13.5 vs. 28.1 +/- 13.3% of prescribed dose) or rectum (D-mean of 42.3 +/- 7.4 vs. 42.6 +/- 7.5%). The maximum dose to bladder was only slightly higher in the predicted plans (D2% of 100.7 +/- 5.3 vs. 99.8 +/- 4.0%) and for the rectum it was even lower (D2% of 100.5 +/- 3.7 vs. 100.1 +/- 4.3). ConclusionsThe deep learning-based model could predict MLC motion sequences in prostate VMAT plans, eliminating the need for sequencing inside a TPS, thus revolutionizing autonomous treatment planning workflows. This research completes the loop in deep learning-based treatment planning processes, enabling more efficient workflows for real-time or online adaptive radiotherapy.