Abstract
Treatment planning in low-dose-rate prostate brachytherapy (LDR-PB) aims to produce arrangement of implantable radioactive seeds that deliver a minimum prescribed dose to the prostate whilst minimizing toxicity to healthy tissues. There can be multiple seed arrangements that satisfy this dosimetric criterion, not all deemed ‘acceptable’ for implant from a physician’s perspective. This leads to plans that are subjective where quality of treatment depends on the expertise of the planner. We propose a method that learns to generate consistent treatment plans from a large pool of successful clinical data (961 patients). Our model is based on conditional generative adversarial networks that use a novel loss function for penalizing the model on spatial constraints of the seeds. An optional optimizer based on a simulated annealing (SA) algorithm can be used to further fine-tune the plans if necessary (determined by the treating physician). Performance analysis was conducted on 150 test cases demonstrating comparable results to that of the manual plans. On average, the clinical target volume covered by \(100\%\) of the prescribed dose was \(98.9\%\) for our method compared to \(99.4\%\) for manual plans. Moreover, using our model, the planning time was significantly reduced to an average of 3 s/plan (2.5 min/plan with the optional SA). Compared to this, manual planning at our centre takes around 20 min/plan.
This work was supported by the Canadian Institutes of Health Research (CIHR).
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References
Aleef, T.A., Spadinger, I.T., Peacock, M.D., Salcudean, S.E., Mahdavi, S.S.: Centre-specific autonomous treatment plans for prostate brachytherapy using CGANs. Int. J. Comput. Assist. Radiol. Surg., 1–10 (2021)
D’Souza, W.D., Meyer, R., Thomadsen, B.R., Ferris, M.: An iterative sequential mixed-integer approach to automated prostate brachytherapy treatment plan optimization. Phys. Med. Biol. 46(2), 297 (2001)
Ferrari, G., Kazareski, Y., Laca, F., Testuri, C.E.: A model for prostate brachytherapy planning with sources and needles position optimization. Oper. Res. Health Care 3(1), 31–39 (2014)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Guthier, C., Aschenbrenner, K., Buergy, D., Ehmann, M., Wenz, F., Hesser, J.: A new optimization method using a compressed sensing inspired solver for real-time LDR-brachytherapy treatment planning. Phys. Med. Biol. 60(6), 2179 (2015)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
John, S.: The seattle prostate institute approach to treatment planning for permanent implants. In: Dicker, A.P., Merrick, G., Gomella, L., Valicenti, R.K., Waterman, F. (eds.) Basic and Advanced Techniques in Prostate Brachytherapy, chap. 15, pp. 178–201. CRC Press, London (2005)
Karimi, D., Salcudean, S.E.: Reducing the hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Trans. Med. Imaging 39(2), 499–513 (2019)
Mahdavi, S.S., Peacock, M.D., Morris, W.J., Spadinger, I.T.: Automatic dual air kerma strength treatment planning for focal low-dose-rate prostate brachytherapy boost using dosimetric and geometric constraints. arXiv preprint arXiv:2010.12617 (2020)
Nicolae, A., et al.: Evaluation of a machine-learning algorithm for treatment planning in prostate low-dose-rate brachytherapy. Int. J. Radiat. Oncol. Biol. Phys. 97(4), 822–829 (2017)
Nouranian, S., Ramezani, M., Spadinger, I., Morris, W.J., Salcudean, S.E., Abolmaesumi, P.: Automatic prostate brachytherapy preplanning using joint sparse analysis. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 415–423. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24571-3_50
Pouliot, J., Tremblay, D., Roy, J., Filice, S.: Optimization of permanent 125I prostate implants using fast simulated annealing. Int. J. Radiat. Oncol. Biol. Phys. 36(3), 711–720 (1996)
Stish, B.J., Davis, B.J., Mynderse, L.A., McLaren, R.H., Deufel, C.L., Choo, R.: Low dose rate prostate brachytherapy. Transl. Androl. Urol. 7(3), 341 (2018)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
Yu, Y., et al.: Permanent prostate seed implant brachytherapy: report of the American association of physicists in medicine task group no. 64. Med. Phys. 26(10), 2054–2076 (1999)
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Aleef, T.A., Spadinger, I.T., Peacock, M.D., Salcudean, S.E., Mahdavi, S.S. (2021). Rapid Treatment Planning for Low-dose-rate Prostate Brachytherapy with TP-GAN. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12904. Springer, Cham. https://doi.org/10.1007/978-3-030-87202-1_56
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