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01.06.2020 | Review Article

Artificial Intelligence in radiotherapy: state of the art and future directions

verfasst von: Giulio Francolini, Isacco Desideri, Giulia Stocchi, Viola Salvestrini, Lucia Pia Ciccone, Pietro Garlatti, Mauro Loi, Lorenzo Livi

Erschienen in: Medical Oncology | Ausgabe 6/2020

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Abstract

Recent advances in computing capability allowed the development of sophisticated predictive models to assess complex relationships within observational data, described as Artificial Intelligence. Medicine is one of the several fields of application and Radiation oncology could benefit from these approaches, particularly in patients’ medical records, imaging, baseline pathology, planning or instrumental data. Artificial Intelligence systems could simplify many steps of the complex workflow of radiotherapy such as segmentation, planning or delivery. However, Artificial Intelligence could be considered as a “black box” in which human operator may only understand input and output predictions and its application to the clinical practice remains a challenge. The low transparency of the overall system is questionable from manifold points of view (ethical included). Given the complexity of this issue, we collected the basic definitions to help the clinician to understand current literature, and overviewed experiences regarding implementation of AI within radiotherapy clinical workflow, aiming to describe this field from the clinician perspective.

El Naqa I, et al. Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century. Br J Radiol. 2020;93:1106.CrossRef

Meyer P, et al. Survey on deep learning for radiotherapy. Comput Biol Med. 2018;98:126–46.PubMedCrossRef

Sharp G, et al. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys. 2014;5:050902.CrossRef

Eldesoky AR, et al. Internal and external validation of an ESTRO delineation guideline: dependent automated segmentation tool for loco-regional radiation therapy of early breast cancer. Radiother Oncol. 2016;3:424–30.CrossRef

Nguyen A et al. Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit; 2015; pp. 427–436.

Charron O, et al. Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput Biol Med. 2018;95:43–544.PubMedCrossRef

Shickel B, et al. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J Biomed Health Inform. 2018;22(5):1589–604.PubMedCrossRef

Boden MA. Artificial intelligence and natural man. New York: Basic Books; 1977.

Tsay D, Patterson C. From machine learning to artificial intelligence applications in cardiac care. Circulation. 2018;138:2569–75.PubMedCrossRef

10.

LeCun Y, et al. Deep learning. Nature. 2015;521:436–44.PubMedCrossRef

11.

Mitchell TM. Machine learning. New York: McGraw-Hill; 1997.

12.

Speight, et al. Evaluation of atlas based auto-segmentation for head and neck target volume delineation in adaptive/replan IMRT. J Phys Conf Ser. 2014;489:012060.CrossRef

13.

Men K, et al. Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images. Front Oncol. 2017;7:315.PubMedPubMedCentralCrossRef

14.

Cardenas CE, et al. Deep learning algorithm for auto-delineation of high-risk oropharyngeal clinical target volumes with built-in dice similarity coefficient parameter optimization function. Int J Radiat Oncol Biol Phys. 2018;101:468–78.PubMedCrossRefPubMedCentral

15.

McCarroll RE, et al. Retrospective validation and clinical implementation of automated contouring of organs at risk in the head and neck: a step toward automated radiation treatment planning for low- and middle-income countries. J Glob Oncol. 2018;4:1–11.PubMed

16.

Nikolov S et al. Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy. ArXiv, 2018; arXiv:1809.04430.

17.

Li Q, et al. Tumor segmentation in contrast-enhanced magnetic resonance imaging for nasopharyngeal carcinoma: deep learning with convolutional neural network. Biomed Res Int. 2018. https://doi.org/10.1155/2018/9128527.CrossRefPubMedPubMedCentral

18.

Tong N, et al. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks. Med Phy. 2018;45(10):4558–677.CrossRef

19.

Van Rooij W, et al. Deep learning-based delineation of head and neck organs at risk: geometric and dosimetric evaluation. Int J Radiat Oncol Biol Phys. 2019;104(3):677–84.PubMedCrossRef

20.

Van Dijk LV, et al. Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring. Radiother Oncol. 2020;142:115–23.PubMedCrossRef

21.

Martin S, et al. A multiphase validation of atlas-based automatic and semiautomatic segmentation strategies for prostate MRI. Int J Radiat Oncol Biol Phys. 2013;85:95–100.PubMedCrossRef

22.

Macomber MW, et al. Autosegmentation of prostate anatomy for radiation treatment planning using deep decision forests of radiomic features. Phys Med Biol. 2018;63(23):235002.PubMedCrossRef

23.

Lustberg T, et al. Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol. 2018;126:312–7.PubMedCrossRef

24.

Men K, et al. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys. 2017;44(12):6377–89.PubMedCrossRef

25.

Liu Y, et al. A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery. PLoS ONE. 2017;12(10):e0185844.PubMedPubMedCentralCrossRef

26.

Men K, et al. Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning. Phys Med. 2018;50:13–9.PubMedCrossRef

27.

Menze BH, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging. 2015;34:1993–2024.PubMedCrossRef

28.

Fan J, et al. Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique. Med Phys. 2019;1:370–81.CrossRef

29.

Chen X, et al. A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning. Med Phys. 2019;1:56–64.CrossRef

30.

Liu F, et al. MR-based treatment planning in radiation therapy using a deep learning approach. J Appl Clin Med Phys. 2019;20(3):105–14.PubMedPubMedCentralCrossRef

31.

Ma M, et al. Dosimetric features-driven machine learning model for DVH prediction in VMAT treatment planning. Med Phys. 2019;2:857–67.

32.

Barragán-Montero AM, et al. Three-dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations. Med Phys. 2019;46(8):3679–91.PubMed

33.

Bai X, et al. Approach and assessment of automated stereotactic radiotherapy planning for early stage non-small-cell lung cancer. Biomed Eng Online. 2019;18(1):101.PubMedPubMedCentralCrossRef

34.

Carlson JN, et al. A machine learning approach to the accurate prediction of multi-leaf collimator positional errors. Phys Med Biol. 2016;61(6):2514–31.PubMedCrossRef

35.

Liu Z, et al. A deep learning method for prediction of three-dimensional dose distribution of helical tomotherapy. Med Phys. 2019;46(5):1972–83.PubMedCrossRef

36.

Osman AFI, et al. Prediction of the individual multileaf collimator positional deviations during dynamic IMRT delivery priori with artificial neural network. Med Phys. 2020. https://doi.org/10.1002/mp.14014.CrossRefPubMed

37.

Mahdavi SR, et al. Use of artificial neural network for pretreatment verification of intensity modulation radiation therapy fields. Br J Radiol. 2019;92(1102):20190355. https://doi.org/10.1259/bjr.20190355.CrossRefPubMedPubMedCentral

38.

Zhao W, et al. Markerless pancreatic tumor target localization enabled by deep learning. Int J Radiat Oncol Biol Phys. 2019;105(2):432–9.PubMedCrossRefPubMedCentral

39.

Malone C, et al. Using a neural network to predict deviations in mean heart dose during the treatment of left-sided deep inspiration breath hold patients. Phys Med. 2019;65:137–42.PubMedCrossRef

40.

Kann BH, Aneja S, Loganadane GV, Kelly JR, Smith SM, Decker RH, et al. Pretreatment identification of head and neck cancer nodal metastasis and extranodal extension using deep learning neural networks. Sci Rep. 2018;8:14036. https://doi.org/10.1038/s41598-018-32441-y.CrossRefPubMedPubMedCentral

41.

Li S, Wang K, Hou Z, Yang J, Ren W, Gao S, Meng F, Wu P, Liu B, Liu J, Yan J. Use of radiomics combined with machine learning method in the recurrence patterns after intensity-modulated radiotherapy for nasopharyngeal carcinoma: a preliminary study. Front Oncol. 2018;8:648. https://doi.org/10.3389/fonc.2018.00648.CrossRefPubMedPubMedCentral

42.

Baek S, He Y, Allen BG, Buatti JM, Smith BJ, Tong L, Sun Z, Wu J, Diehn M, Loo BW, Plichta KA, Seyedin SN, Gannon M, Cabel KR, Kim Y, Wu X. Deep segmentation networks predict survival of non-small cell lung cancer. Sci Rep. 2019;9(1):17286. https://doi.org/10.1038/s41598-019-53461-2.CrossRefPubMedPubMedCentral

43.

Bibault JE, Giraud P, Housset M, Durdux C, Taieb J, Berger A, Coriat R, Chaussade S, Dousset B, Nordlinger B, Burgun A. Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep. 2018;8(1):12611.PubMedPubMedCentralCrossRef

44.

Gabryś HS, et al. Design and selection of machine learning methods using radiomics and dosiomics for normal tissue complication probability modeling of xerostomia. Front Oncol. 2018;8:35.PubMedPubMedCentralCrossRef

45.

Jiang W, et al. Machine learning methods uncover radiomorphologic dose patterns in salivary glands that predict xerostomia in patients with head and neck cancer. Adv Radiat Oncol. 2018;4:401–12.PubMedPubMedCentralCrossRef

46.

Men K, et al. A deep learning model for predicting xerostomia due to radiation therapy for head and neck squamous cell carcinoma in the RTOG 0522 clinical trial. Int J Radiat Oncol Biol Phys. 2019;2:440–7.CrossRef

47.

Lee S, et al. Machine learning on a genome-wide association study to predict late genitourinary toxicity after prostate radiation therapy. Int J Radiat Oncol Biol Phys. 2018;101:128–35.PubMedPubMedCentralCrossRef

48.

Tian Z, et al. A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies. Brachytherapy. 2019;4:530–8.CrossRef

49.

Tseng HH, Luo Y, Cui S, Chien JT, Ten Haken RK, Naqa IE. Deep reinforcement learning for automated radiation adaptation in lung cancer. Med Phys. 2017;44(12):6690–705.PubMedCrossRef

50.

Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: Addressing ethical challenges. PLoS Med. 2018;15(11):e1002689. https://doi.org/10.1371/journal.pmed.1002689.CrossRefPubMedPubMedCentral

51.

Price WN. Regulating black-box medicine. Mich L Rev. 2017;116(1):421–74.

Titel: Artificial Intelligence in radiotherapy: state of the art and future directions
verfasst von: Giulio Francolini
Isacco Desideri
Giulia Stocchi
Viola Salvestrini
Lucia Pia Ciccone
Pietro Garlatti
Mauro Loi
Lorenzo Livi
Publikationsdatum: 01.06.2020
Verlag: Springer US
Erschienen in: Medical Oncology / Ausgabe 6/2020
Print ISSN: 1357-0560
Elektronische ISSN: 1559-131X
DOI: https://doi.org/10.1007/s12032-020-01374-w

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