Sharp loss: a new loss function for radiotherapy dose prediction based on fully convolutional networks

Neural-network methods have been widely used for the prediction of dose distributions in radiotherapy. However, the prediction accuracy of existing methods may be degraded by the problem of dose imbalance. In this work, a new loss function is proposed to alleviate the dose imbalance and achieve more accurate prediction results. The U-Net architecture was employed to build a prediction model. Our study involved a total of 110 patients with left-breast cancer, who were previously treated by volumetric-modulated arc radiotherapy. The patient dataset was divided into training and test subsets of 100 and 10 cases, respectively. We proposed a novel 'sharp loss' function, and a parameter γ was used to adjust the loss properties. The mean square error (MSE) loss and the sharp loss with different γ values were tested and compared using the Wilcoxon signed-rank test.

The sharp loss achieved superior dose prediction results compared to those of the MSE loss. The best performance with the MSE loss and the sharp loss was obtained when the parameter γ was set to 100. Specifically, the mean absolute difference values for the planning target volume were 318.87 ± 30.23 for the MSE loss versus 144.15 ± 16.27 for the sharp loss with γ = 100 (p < 0.05). The corresponding values for the ipsilateral lung, the heart, the contralateral lung, and the spinal cord were 278.99 ± 51.68 versus 198.75 ± 61.38 (p < 0.05), 216.99 ± 44.13 versus 144.86 ± 43.98 (p < 0.05), 125.96 ± 66.76 versus 111.86 ± 47.19 (p > 0.05), and 194.30 ± 14.51 versus 168.58 ± 25.97 (p < 0.05), respectively.

The sharp loss function could significantly improve the accuracy of radiotherapy dose prediction.