3D dose prediction for Gamma Knife radiosurgery using deep learning and data modification.

PURPOSE

To develop a machine learning-based, 3D dose prediction methodology for Gamma Knife (GK) radiosurgery. The methodology accounts for cases involving targets of any number, size, and shape.

METHODS

Data from 322 GK treatment plans was modified by isolating and cropping the contoured MRI and clinical dose distributions based on tumor location, then scaling the resulting tumor spaces to a standard size. An accompanying 3D tensor was created for each instance to account for tumor size. The modified dataset for 272 patients was used to train both a generative adversarial network (GAN-GK) and a 3D U-Net model (U-Net-GK). Unmodified data was used to train equivalent baseline models. All models were used to predict the dose distribution of 50 out-of-sample patients. Prediction accuracy was evaluated using gamma, with criteria of 4 %/2mm, 3 %/3mm, 3 %/1mm and 1 %/1mm. Prediction quality was assessed using coverage, selectivity, and conformity indices.

RESULTS

The predictions resulting from GAN-GK and U-Net-GK were similar to their clinical counterparts, with average gamma (4 %/2mm) passing rates of 84.9 ± 15.3 % and 83.1 ± 17.2 %, respectively. In contrast, the gamma passing rate of baseline models were significantly worse than their respective GK-specific models (p < 0.001) at all criterion levels. The quality of GK-specific predictions was also similar to that of clinical plans.

CONCLUSION

Deep learning models can use GK-specific data modification to predict 3D dose distributions for GKRS plans with a large range in size, shape, or number of targets. Standard deep learning models applied to unmodified GK data generated poorer predictions.