Nested CNN architecture for three-dimensional dose distribution prediction in tomotherapy for prostate cancer

The hypothesis of changing network layers to increase the accuracy of dose distribution prediction, instead of expanding their dimensions, which requires complex calculations, has been considered in our study.

A total of 137 prostate cancer patients treated with the tomotherapy technique were categorized as 80% training and validating as well as 20% testing for the nested UNet and UNet architectures. Mean absolute error (MAE) was used to measure the dosimetry indices of dose-volume histograms (DVHs), and geometry indices, including the structural similarity index measure (SSIM), dice similarity coefficient (DSC), and Jaccard similarity coefficient (JSC), were used to evaluate the isodose volume (IV) similarity prediction. To verify a statistically significant difference, the two-way statistical Wilcoxon test was used at a level of 0.05 (p < 0.05).

Use of a nested UNet architecture reduced the predicted dose MAE in DVH indices. The MAE for planning target volume (PTV), bladder, rectum, and right and left femur were D_98% = 1.11 ± 0.90; D_98% = 2.27 ± 2.85, D_mean = 0.84 ± 0.62; D_98% = 1.47 ± 12.02, D_mean = 0.77 ± 1.59; D_2% = 0.65 ± 0.70, D_mean = 0.96 ± 2.82; and D_2% = 1.18 ± 6.65, D_mean = 0.44 ± 1.13, respectively. Additionally, the greatest geometric similarity was observed in the mean SSIM for UNet and nested UNet (0.91 vs. 0.94, respectively).

The nested UNet network can be considered a suitable network due to its ability to improve the accuracy of dose distribution prediction compared to the UNet network in an acceptable time.