Automated treatment planning framework for brachytherapy of cervical cancer using 3D dose predictions.

. To lay the foundation for automated knowledge-based brachytherapy treatment planning using 3D dose estimations, we describe an optimization framework to convert brachytherapy dose distributions directly into dwell times (DTs).. A dose rate kernelḋ(r,θ,φ)was produced by exporting 3D dose for one dwell position from the treatment planning system and normalizing by DT. By translating and rotating this kernel to each dwell position, scaling by DT and summing over all dwell positions, dose was computed (). We used a Python-coded COBYLA optimizer to iteratively determine the DTs that minimize the mean squared error betweenand reference dose, computed using voxels with80%-120% of prescription. As validation of the optimization, we showed that the optimizer replicates clinical plans when= clinical dose in 40 patients treated with tandem-and-ovoid (T&O) or tandem-and-ring (T&R) and 0-3 needles. Then we demonstrated automated planning in 10 T&O using= dose predicted from a convolutional neural network developed in past work. Validation and automated plans were compared to clinical plans using mean absolute differences (MAD=1N∑n=1Nabsxn-xn') over all voxels (= Dose,= #voxels) and DTs (= DT,= #dwell positions), mean differences (MD) in organand high-risk CTV D90 over all patients (where positive indicates higher clinical dose), and mean Dice similarity coefficients (DSC) for 100% isodose contours.. Validation plans agreed well with clinical plans (MAD= 1.1%, MAD= 4 s or 0.8% of total plan time,MD = -0.2% to 0.2% and D90 MD = -0.6%, DSC = 0.99). For automated plans, MAD= 6.5% and MAD= 10.3 s (2.1%). The slightly higher clinical metrics in automated plans (MD = -3.8% to 1.3% and D90 MD = -5.1%) were due to higher neural network dose predictions. The overall shape of the automated dose distributions were similar to clinical doses (DSC = 0.91).. Automated planning with 3D dose predictions could provide significant time savings and standardize treatment planning across practitioners, regardless of experience.