Automatic radiotherapy planning for deliverable plans using deep learning dose prediction and dose rings optimization in cervical cancer.

BACKGROUND

Automatic radiotherapy (RT) planning based on deep learning (DL) has been extensively researched. However, it is challenging to import the predicted dose distribution into mainstream treatment planning systems (TPSs) and generate clinically deliverable plans.

PURPOSE: To investigate the feasibility and accuracy of an automatic volumetric modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT) planning method for generation of universally deliverable plans based on DL dose prediction and dose rings optimization.

METHODS

First, dose distributions were predicted using a three-dimensional (3D) Fusion Residual Unet (F-ResUnet) DL network with data from two hospitals, which included 230 and 210 gynecological cancer (GC) patients underwent VMAT and IMRT, respectively. Then, the predicted dose distributions were discretized into dose rings to optimize the plans automatically in two mainstream TPSs based on the dose rings. Finally, the deliverability of generated plans was verified with patient-specific quality assurance (PSQA).

RESULTS

The predicted dose distributions were clinically acceptable with a target coverage over 95%. Compared with the clinical plans, the automatic plans optimized with dose rings achieved a similar dose coverage on planning target volumes (PTV) with an average target coverage over 96.5%. For organs at risk (OARs) sparing, automatic VMAT plans markedly decreased the V30Gy of left femoral head (p = 0.05), right femoral head (p = 0.004), and small intestine (p = 0.04). The V45Gy of bladder and rectum in the automatic IMRT plans were reduced by approximately 7% and 9%, respectively. Deliverability verification with PSQA achieved a mean gamma passing rate of 99.1%, 97.1% and 98.3%, 95.0% under the criteria of 3%/3 mm and 3%/2 mm for VMAT and IMRT plans, respectively.

CONCLUSIONS

The proposed automatic planning method combining DL dose prediction and dose rings optimization was feasible to generate universally deliverable VMAT and IMRT plans for gynecological cancer (GC) patients.