Abstract
During a course of radiotherapy, patients may have weight loss and radiation induced anatomical changes. To avoid delivering harmful dose to normal organs, the treatment may need adaptation according to the change. In this study, we proposed a novel deep neural network for predicting parotid glands (PG) anatomical changes by using the displacement fields (DFs) between the planning CT and weekly cone beam computed tomography (CBCT) acquired during the treatment. Sixty three HN patients treated with volumetric modulated arc therapy of 70 Gy in 35 fractions were retrospectively studied. We calculated DFs between week 1–3 CBCT and the planning CT by a B-spline deformable image registration algorithm. The resultant DFs were subsequently used as input to a novel network combining convolutional neural networks and recurrent neural networks for predicting the DF between the Week 4–6 CBCT and the planning CT. Finally, we reconstructed the warped PG contour using the predicted DF. For evaluation, we calculated DICE coefficient and mean volume difference by comparing the predicted PG contours, and manual contours at weekly CBCT. The average DICE was 0.82 (week 4), 0.81 (week 5), and 0.80 (week 6) and the average of volume difference between predict contours and manual contours was 1.85 cc (week 4), 2.20 cc (week 5) and 2.51 cc (week 6). In conclusion, the proposed deep neural network combining CNN and RNN was capable of predicting anatomical and volumetric changes of the PG with clinically acceptable accuracy.
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Lee, D., Alam, S., Nadeem, S., Jiang, J., Zhang, P., Hu, YC. (2020). Longitudinal Prediction of Radiation-Induced Anatomical Changes of Parotid Glands During Radiotherapy Using Deep Learning. In: Rekik, I., Adeli, E., Park, S.H., Valdés Hernández, M.d.C. (eds) Predictive Intelligence in Medicine. PRIME 2020. Lecture Notes in Computer Science(), vol 12329. Springer, Cham. https://doi.org/10.1007/978-3-030-59354-4_12
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