Deep Learning-based Volumetric Modulated Arc Therapy Dose Prediction with Uncertainty Quantification for Left- and Right-sided Breast Cancer: A Single-institution Feasibility Study.

BACKGROUND

Volumetric-modulated arc therapy (VMAT) enables precise tumor targeting while minimizing dose to organs at risk but requires time-consuming planning with quality variability. Most deep learning (DL) dose prediction studies focus on left-sided breast cancer, with limited exploration of right-sided cases or integrated uncertainty quantification. This study investigates the feasibility of a DL model for VMAT dose prediction in both left-and right-sided breast cancer, incorporating Monte Carlo dropout (MCD)-based uncertainty quantification within a single-institution dataset.

METHODS

A total of 68 clinically accepted breast cancer VMAT treatment plans (41 left-sided, 27 right-sided) were collected. A three-dimensional U-Net architecture was implemented. Two models with dropout rates of 0.25 (M0.25) and 0.50 (M0.50) were developed. MCD was applied for uncertainty quantification. Performance was evaluated using mean absolute error (MAE), gamma passing rate (GPR), and isodose Dice similarity coefficients.

RESULTS

The overall body MAE was 1.7 ± 0.11 Gy (M0.25) and 1.93 ± 0.11 Gy (M0.50). For the planning target volume (PTV) of 42.56 Gy, MAE was 0.93 ± 0.17 Gy (M0.25) and 0.88 ± 0.15 Gy (M0.50). The heart showed the lowest errors (M0.25: 0.87 ± 0.34 Gy; M0.50: 1.06 ± 0.38 Gy), while contralateral lung exhibited higher uncertainties. GPR at 3%/3 mm reached 85.5% for both models, with no significant difference between left- and right-sided cases. Uncertainty maps correlated with prediction errors (Pearson correlation =0.59) and identified regions with atypical anatomies.

CONCLUSIONS

The proposed model supports the feasibility of VMAT dose prediction for both left-and right-sided breast cancer, while the uncertainty maps provide additional confidence information to assist clinical interpretation and decision-making. Uncertainty maps correlated with prediction error and flagged atypical cases. External validation with a larger, multi-institutional dataset is required before clinical implementation can be considered.