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Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review

  • Image & Signal Processing
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Abstract

Radiation oncology for prostate cancer is important as it can decrease the morbidity and mortality associated with this disease. Planning for this modality of treatment is both fundamental, time-consuming and prone to human-errors, leading to potentially avoidable delays in start of treatment. A fundamental step in radiotherapy planning is contouring of radiation targets, where medical specialists contouring, i.e., segment, the boundaries of the structures to be irradiated. Automating this step can potentially lead to faster treatment planning without a decrease in quality, while increasing time available to physicians and also more consistent treatment results. This can be framed as an image segmentation task, which has been studied for many decades in the fields of Computer Vision and Machine Learning. With the advent of Deep Learning, there have been many proposals for different network architectures achieving high performance levels. In this review, we searched the literature for those methods and describe them briefly, grouping those based on Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). This is a booming field, evidenced by the date of the publications found. However, most publications use data from a very limited number of patients, which presents an obstacle to deep learning models training. Although the performance of the models has achieved very satisfactory results, there is still room for improvement, and there is arguably a long way before these models can be used safely and effectively in clinical practice.

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References

  1. American Cancer Society (2019) Facts & Figures 2019. Technical report, American Cancer Society

  2. Reda I., Khalil A., Elmogy M., El-Fetouh A. A., Shalaby A., El-Ghar M. A., Elmaghraby A., Ghazal M., El-Baz A.: Deep learning role in early diagnosis of prostate cancer. Technol. Cancer. Res. Treat. 17: 1–11, 2018

    Google Scholar 

  3. Noone A. M., Howlader N., Krapcho M., Miller D., Brest A., Yu M., Ruhl J., Tatalovich Z., Mariotto A., Lewis D. R., Chen H. S., Feuer E. J., Cronin K. A. (2017) SEER Cancer statistics review. Technical report, National Cancer Institute

  4. Nezhad M. Z., Sadati N., Yang K., Zhu D.: A Deep Active Survival Analysis approach for precision treatment recommendations Application of prostate cancer. Expert. Syst. Appl. 115: 16–26, 2019

    Google Scholar 

  5. Kazmierska J., Sala N. J., Leech M., Jereczek-Fossa B. A., Lievens Y., Yarnold J. (2018) Radiotherapy: Seizing the opportunity in cancer care. Technical report, ESTRO Cancer Foundation

  6. Borras J. M., Lievens Y., Barton M., Corral J., Ferlay J., Bray F., Grau C.: How many new cancer patients in Europe will require radiotherapy by 2025? An ESTRO-HERO analysis. Radiother. Oncol. 119 (1): 5–11, 2016

    PubMed  Google Scholar 

  7. Borras J. M., Lievens Y., Dunscombe P., Coffey M., Malicki J., Corral J., Gasparotto C., Defourny N., Barton M., Verhoeven R., Van Eycken L., Primic-Zakelj M., Trojanowski M., Strojan P., Grau C.: The optimal utilization proportion of external beam radiotherapy in European countries: An ESTRO-HERO analysis. Radiother. Oncol. 116 (1): 38–44, 2015

    PubMed  Google Scholar 

  8. Moore K. L.: Automated radiotherapy treatment planning. Semin. Radiat. Oncol. 29 (3): 209–218, 2019

    PubMed  Google Scholar 

  9. Emami B., Lyman J., Brown A., Cola L., Goitein M., Munzenrider J. E., Shank B., Solin L. J., Wesson M.: Tolerance of normal tissue to therapeutic irradiation. Int. J. Radiat. Oncol. Biol. Phys. 21 (1): 109–122, 1991

    CAS  PubMed  Google Scholar 

  10. Pereira G. C., Traughber M., Muzic R. F. (2014) The role of imaging in radiation therapy planning: Past, present, and future. Biomed Res Int 2014(2)

  11. Meyer P., Noblet V., Mazzara C., Lallement A.: Survey on deep learning for radiotherapy. Comput. Biol. Med. 98: 126–146, 2018

    PubMed  Google Scholar 

  12. Gay H. A., Barthold H. J., O’Meara E., Bosch W. R., Naga I. E., Al-Lozi R., Rosenthal S. A., Lawton C., Lee W. R., Sandler H., Zietman A., Myerson R., Dawson L. A., Willett C., Kachnic L. A., Jhingran A., Portelance L., Ryu J., Small W., Gaffney D., Viswanathan A. N., Michalski J. F. (2007) Male pelvis normal tissue - RTOG consensus contouring guidelines. Technical report, Radiation Therapy Oncology Group Foundation

  13. Fiorino C., Reni M., Bolognesi A., Cattaneo G. M., Calandrino R.: Intra- and inter-observer variability in contouring prostate and seminal vesicles: Implications for conformal treatment planning. Radiother. Oncol. 47 (3): 285–292, 1998

    CAS  PubMed  Google Scholar 

  14. Gao Z., Wilkins D., Eapen L., Morash C., Wassef Y., Gerig L.: A study of prostate delineation referenced against a gold standard created from the visible human data. Radiother. Oncol. 85 (2): 239–246, 2007

    PubMed  Google Scholar 

  15. Mohler J. L., Srinivas S., Antonarakis E. S., Armstrong A. J., D’Amico A. V., Davis B. J., Dorff T. (2019) Prostate cancer NCCN guidelines version 4.1029. Technical report, National Comprehensive Cancer Network

  16. The Royal College of Radiologists, Society of Radiographers, College, Institute of Physics in Medicine, and Engineering: On target: ensuring geometric accuracy in radiotherapy. Technical report, The Royal College of Radiologists (2008)

  17. Shen D., Wu G., Suk H.-I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19: 221–248, 2017

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Suarez-Ibarrola R., Hein S., Reis G., Gratzke C., Miernik A.: Current and future applications of machine and deep learning in urology: A review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer Ml: World J Urol, 2019

    Google Scholar 

  19. Boldrini L., Bibault J.-E., Masciocchi C., Shen Y., Bittner M.-I. (2019) Deep Learning: A review for the radiation oncologist. Front Oncol 9(October)

  20. LeCun Y., Bengio Y., Hinton G.: Deep learning. Nature 521 (7553): 436–444, 2015

    Article  CAS  Google Scholar 

  21. Krizhevsky A., Sutskever I., Hinton G. E.: Imagenet Classification with Deep Convolutional Neural Networks. Adv. Neural. Inf. Process. Syst. 15: 474–483, 2014

    Google Scholar 

  22. LeCun Y., Boser B. E., Denker J. S., Henderson D., Howard R. E., Hubbard W. E., Jackel L. D.: Handwritten Digit Recognition with a Back-Propagation Network. In: (Touretzky D. S., Ed.) Adv Neural Inf Process Syst, vol 2. Morgan-Kaufmann, 1990, pp 396–404

  23. Yu L., Yang X., Chen H., Qin J., Heng P.-A.: Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images.. In: Thirty-First AAAI Conf Artif Intell, 2017, pp 66–72

  24. Shelhamer E., Long J., Darrell T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern. Anal. Mach. Intell. 39: 1, 2016

    Google Scholar 

  25. Ronneberger O., Fischer P., Brox T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: (Navab N., Hornegger J., Wells W. M., Frangi A. F., Eds.) Med Image Comput Comput Interv – MICCAI 2015, Cham, 2015, pp 234–241. Springer International Publishing

  26. Çiçek Ö., Abdulkadir A., Lienkamp S. S., Brox T., Ronneberger O. (2016) 3D U-net: Learning dense volumetric segmentation from sparse annotation. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics), 9901 LNCS 424–432

  27. Milletari F., Navab N., Ahmadi S. A. (2016) V-Net: Fully convolutional neural networks for volumetric medical image segmentation. Proc - 2016 4th Int Conf 3D Vision, 3DV 2016, pp. 565–571

  28. Kazemifar S., Balagopal A., Nguyen D., McGuire S., Hannan R., Jiang S., Owrangi A.: Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning. Biomed. Phys. Eng. Express. 4 (5): 55003, 2018

    Google Scholar 

  29. Oktay O., Schlemper J., Folgoc L., Lee M., Heinrich M., Misawa K., Mori K., McDonagh S., Hammerla N., Kainz B., Glocker B., Rueckert D. (2018) Attention U-Net: Learning where to look for the pancreas. 1st Conf Med Imaging with Deep Learn (MIDL 2018)

  30. Cardenas C. E., Yang J., Anderson B. M., Court L. E., Brock K. B.: Advances in Auto-Segmentation. Semin. Radiat. Oncol. 29 (3): 185–197, 2019

    PubMed  Google Scholar 

  31. Litjens G., Kooi T., Bejnordi B. E., Setio A. A. A., Ciompi F., Ghafoorian M., van der Laak J. A. W. M., van Ginneken B., Sánchez C. I.: Van der A survey on deep learning in medical image analysis. Med. Image. Anal. 42 (2012): 60–88, 2017

    PubMed  Google Scholar 

  32. Bi W. L., Hosny A., Schabath M. B., Giger M. L., Birkbak N. J., Mehrtash A., Allison T., Arnaout O., Abbosh C., Dunn I. F., Mak R. H., Tamimi R. M., Tempany C. M., Swanton C., Hoffmann U., Schwartz L. H., Gillies R. J., Huang R. Y., Aerts H. J. W. L.: Artificial intelligence in cancer imaging clinical challenges and applications. CA Cancer. J. Clin. 0 (0): 1–31, 2019

    Google Scholar 

  33. Sahiner B., Pezeshk A., Hadjiiski L. M., Wang X., Drukker K., Cha K. H., Summers R. M., Giger M. L.: Deep learning in medical imaging and radiation therapy. Med. Phys. 46 (1): e1–e36, 2019

    PubMed  Google Scholar 

  34. Boon I., Yong T. A., Boon C.: Assessing the Role of Artificial Intelligence (AI) in Clinical Oncology: Utility of Machine Learning in Radiotherapy Target Volume Delineation. Medicines 5 (4): 131, 2018

    PubMed Central  Google Scholar 

  35. Cuocolo R., Cipullo M. B., Stanzione A., Ugga L., Romeo V., Radice L., Brunetti A., Imbriaco M. (2019) Machine learning applications in prostate cancer magnetic resonance imaging. Eur Radiol Exp 3(1)

  36. Larry Goldenberg S., Nir G., Salcudean S. E.: A new era: artificial intelligence and machine learning in prostate cancer. Nat. Rev. Urol. 16 (7): 391–403, 2019

    PubMed  Google Scholar 

  37. Taghanaki S. A., Zheng Y., Zhou S. K., Georgescu B., Sharma P., Xu D., Comaniciu D., Hamarneh G.: Combo loss: Handling input and output imbalance in multi-organ segmentation. Comput. Med. Imaging Graph. 75: 24–33, 2019

    PubMed  Google Scholar 

  38. Litjens G., Toth R., van de Ven W., Hoeks C., Kerkstra S., van Ginneken B., Vincent G., Guillard G., Birbeck N., Zhang J., Strand R., Malmberg F., Ou Y., Davatzikos C., Kirschner M., Jung F., Yuan J., Wu Q., Gao Q., Edwards P. E., Maan B., van der Heijden F., Ghose S., Mitra J., Dowling J., Barratt D., Huisman H., Madabhushi A.: Evaluation of prostate segmentation algorithms for MRI The PROMISE12 challenge. Med. Image. Anal. 18 (2): 359–373, 2014

    PubMed  Google Scholar 

  39. Bloch N., Madabhushi A., Huisman H., Freymann J., Kirby J., Grauer M., Enquobahrie A., Jaffe C. (2015) Larry clarke, and keyvan farahani NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures

  40. The Brigham Hospital and Women’s. BWH Prostate MR Image Database, 2008

  41. Litjens G., Debats O., Barentsz J., Karssemeijer N., Huisman H. (2017) ProstateX Challenge data

  42. Ke Y., Wang X., Kim J., Khadra M., Fulham M., Dagan F.: A propagation-DNN Deep combination learning of multi-level features for MR prostate segmentation. Comput. Methods Programs Biomed. 170: 11–21, 2019

    Google Scholar 

  43. Tian Z., Liu L., Zhang Z., Fei B.: PSNEt: prostate segmentation on MRI based on a convolutional neural network. J. Med. Imaging. 5 (02): 1, 2018

    Google Scholar 

  44. Liao S., Gao Y., Oto A., Shen D.: Representation learning: A unified deep learning framework for automatic prostate MR segmentation. Med. Image. Comput. Comput. Assist. Interv. 16 (02): 254–261, 2013

    PubMed  PubMed Central  Google Scholar 

  45. Guo Y., Gao Y., Shen D.: Deformable MR prostate segmentation via deep feature learning and sparse patch matching. Deep Learn. Med. Image. Anal. 0062 (c): 197–222, 2017

    Google Scholar 

  46. Drozdzal M., Chartrand G., Vorontsov E., Shakeri M., Di Jorio L., Tang A., Romero A., Bengio Y., Pal C., Kadoury S.: Learning normalized inputs for iterative estimation in medical image segmentation. Med. Image. Anal. 44: 1–13, 2018

    PubMed  Google Scholar 

  47. Zhu Q., Du B., Turkbey B., Choyke P. L., Yan P. (2017) Deeply-supervised CNN for prostate segmentation. Proc Int Jt Conf Neural Networks, pp. 178–184

  48. Cheng R., Roth H. R., Lay N., Lu L., Turkbey B., Gandler W., McCreedy E. S., Choyke P., Summers R. M., McAuliffe M. J.: Automatic MR prostate segmentation by deep learning with holistically-nested networks. Med. Imaging. 2017. Image. Process. 10133 (4): 101332H, 2017

    Google Scholar 

  49. Yi Z., Wei R., Ge G., Ding L., Zhang X., Wang X., Zhang J.: Fully automatic segmentation on prostate MR images based on cascaded fully convolution network. J. Magn. Reson. Imaging 49 (4): 1149–1156, 2018

    Google Scholar 

  50. Zabihollahy F., Schieda N., Jeyaraj S. K., Ukwatta E.: Automated segmentation of prostate zonal anatomy on T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images using U-Nets. Med. Phys. 46 (7): 3078–3090, 2019

    PubMed  Google Scholar 

  51. Geng L., Wang J., Xiao Z., Tong J., Zhang F., Wu J.: Encoder-decoder with dense dilated spatial pyramid pooling for prostate MR images segmentation. Comput. Assist. Surg. 24 (sup2): 13–19, 2019

    Google Scholar 

  52. Lu T., Liang A., Li L., Liu W., Kang H., Chen C. (2019) Automatic prostate segmentation based on fusion between deep network and variational methods. J Xray Sci Technol, 1–17

  53. Zhu Q., Du B., Wu J., Yan P. (2018) A Deep Learning Approach: Health Data Analysis Automatic 3D Prostate MR Segmentation with Densely-Connected Volumetric ConvNets. Proc Int Jt Conf Neural Networks

  54. Karimi D., Samei G., Kesch C., Nir G., Salcudean S. E.: Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models. Int. J. Comput. Assist. Radiol. Surg. 13 (8): 1211–1219, 2018

    PubMed  Google Scholar 

  55. Feng Z., Nie D., Li W., Shen D. (2018) Semi-supervised learning for pelvic MR image segmentation based on multi-task residual fully convolutional networks. Proc - Int Symp Biomed Imaging, 2018-April(Isbi), pp 885–888

  56. Jia H., Xia Y., Song Y., Zhang D., Huang H., Zhang Y., Cai W.: 3D APA-net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images. IEEE Trans. Med. Imaging PP (c): 1–1, 2019

    Google Scholar 

  57. Nie D., Li W., Gao Y., Lian J., Shen D.: STRAINet: Spatially Varying sTochastic Residual AdversarIal Networks for MRI Pelvic Organ Segmentation. IEEE Trans. Neural. Networks Learn. Syst. 30 (5): 1552–1564, 2019

    Google Scholar 

  58. Zhu Q., Bo D., Yan P. (2019) Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation. IEEE Trans Med Imaging:1–1

  59. To M. N. N., Vu D. Q., Turkbey B., Choyke P. L., Kwak J. T.: Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging. Int. J. Comput. Assist. Radiol. Surg. 13 (11): 1687–1696, 2018

    PubMed  Google Scholar 

  60. Ma L., Guo R., Zhang G., Tade F., Schuster D. M., Nieh P., Master V., Fei B.: Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion. Med. Imaging 2017 Image Process. 10133: 101332O, 2017

    Google Scholar 

  61. Zhou S., Nie D., Adeli E., Yin J., Lian J., Shen D.: High-Resolution Encoder-Decoder Networks for Low-Contrast medical image segmentation. IEEE Trans. Image Process. 29 (X): 461–475, 2019

    Google Scholar 

  62. Shi Y., Yang W., Gao Y., Shen D.: Does Manual Delineation only Provide the Side Information in CT Prostate Segmentation?. In: (Descoteaux M., Ed.) MICCAI 2017, Part III, LNCS 10435, vol 10435. Springer International Publishing, 2017, pp 692–700

  63. Dong X., Lei Y., Tian S., Wang T., Patel P., Curran W. J., Jani A. B., Liu T., Yang X. (2019) Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network. Radiother Oncol:1–8

  64. Liu C., Gardner S. J., Wen N., Elshaikh M. A., Siddiqui F., Movsas B., Chetty I. J.: Automatic segmentation of the prostate on CT images using deep neural networks (DNN). Int. J. Radiat. Oncol. Biol. Phys. 104 (4): 924–932, 2019

    PubMed  Google Scholar 

  65. He K., Cao X., Shi Y., Nie D., Gao Y., Shen D.: Pelvic organ segmentation using distinctive curve guided fully convolutional networks. IEEE Trans. Med. Imaging 38 (2): 585–595, 2019

    PubMed  Google Scholar 

  66. Wang S., He K., Nie D., Zhou S., Gao Y., Shen D.: CT Male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation. Med. Image. Anal. 54: 168–178, 2019

    PubMed  PubMed Central  Google Scholar 

  67. Balagopal A., Kazemifar S., Nguyen D., Lin M.-H., Hannan R., Owrangi A., Jiang S. (2018) Fully automated organ segmentation in male pelvic CT images. Phys Med Biol 63(24)

  68. Kearney V., Chan J. W., Wang T., Perry A., Yom S. S., Solberg T. D.: Attention-enabled 3D boosted convolutional neural networks for semantic CT segmentation using deep supervision. Phys. Med. Biol. 64 (13): 135001, 2019

    PubMed  Google Scholar 

  69. Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A. N., Kaiser L., Polosukhin I. (2017) Attention is all you need

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Funding

The authors would like to thank Fundação para a Ciência e Tecnologia (FCT) for the PhD grant (reference SFRH/BD/146887/2019) awarded to the first author, which this work is a part of.

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  1. Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    Gonçalo Almeida

  2. Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R.S. Tavares

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Almeida, G., Tavares, J.M.R. Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review. J Med Syst 44, 179 (2020). https://doi.org/10.1007/s10916-020-01641-3

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