ESTRO 2022 - Abstract Book

S547

Abstract book

ESTRO 2022

Based on the cCTs, we simulated realistic PGI profiles, including Poisson noise and a positioning uncertainty of the PGI slit camera, and extracted spot-wise range shifts by comparison with the expected reference profiles for the planning CT. Spots with reliable PGI information (inside field-of-view and >5E7 protons), were considered with their Bragg peak position for generating two independent 3D spatial maps of 16x16x16 voxels (0.74x0.74x0.66 cm 3 ): (1) The PGI-determined range shift in each voxel is the weighted average taking the spot-wise proton number into account. (2) The proton number in each voxel is summed over all respective spots and normalized per field (Fig. 1). With these maps and the IDD classification, 3D-CNNs (6 convolutional & 2 downsampling layers) were trained using patient- wise 10-fold cross-validation on the binary task to detect anatomical changes.

Results The CNNs achieved a mean training and validation accuracy of 0.85 (range: 0.77-0.91) and 0.83 (0.70-0.93), respectively (Fig. 2). Based on the validation results, anatomical changes were detected with a sensitivity of 0.88 and a specificity of 0.76.

Conclusion Our work shows that CNNs can reliably detect anatomical changes in prostate cancer patients from realistically simulated PGI data of clinical irradiations. With a validation on measured PGI data as the next step, this study highlights the potential