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Radyasyon Onkolojisinde Makine Öğrenmesi

Yıl 2020, Cilt: 42 Sayı: 3, 339 - 349, 27.05.2020
https://doi.org/10.20515/otd.691331

Öz

Yapay zeka (YZ), belirli görevleri yerine getirmek için doğrudan insan uyaranları olmadan bilgisayar yazılımı ve algoritmaları kullanan makinelerde insan benzeri zekayı taklit etmeye çalışan bir bilgisayar bilimidir. Makine öğrenimi (MÖ), önceki bir örneğe veya deneyime dayanarak insan davranışını taklit etmeyi öğrenen veri odaklı algoritmalar kullanan yapay zekanın alt birimidir. Derin öğrenme (DÖ), bir model oluşturmak için derin sinir ağlarını kullanan bir MÖ tekniğidir. Verilerin büyümesi ve paylaşımı, artan bilgi işlem gücü ve MÖ'deki gelişmeler sağlık hizmetlerinde bir dönüşüm başlatmıştır. Radyasyon onkolojisindeki ilerlemeler, her fraksiyon öncesi yapılan bilgisayarlı tomografi (BT) görüntülemesi, dozimetri ve görüntüleme ile entegre edilmesi gereken önemli miktarda veri üretmiştir. Radyasyon Onkolojisinde kullanılan birçok algoritma vardır. Bu yöntemlerin her birinin farklı hesaplama gücü gereksinimleriyle avantajları ve sınırlamaları vardır.Bu derlemede, radyoterapi (RT) sürecinin, MÖ ile kalitesinin ve verimliliğinin arttırılabileceği belirli alanları belirleyerek iş akışı sırası ile gözden geçirme amaçlanmıştır. RT aşaması, hasta değerlendirmesi, simülasyon, konturlama, planlama, kalite kontrol, tedavi uygulama ve hasta takibi olarak yedi gruba ayrılmıştır. Her aşamaya MÖ algoritmalarının uygulanabilirliği, sınırlamaları, avantajları ile ilgili sistematik bir değerlendirme yapılmıştır.

Kaynakça

  • 1- Meyer P, Noblet V, Mazzara C, et al. Survey on deep learning for radiotherapy. Comput Biol Med. 2018;98:126–46.
  • 2- LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–44.
  • 3- Jarrett D, Stride E, Vallis K, et al. Applications and limitations of machine learning in radiation oncology. Br J Radiol. 2019;92:20190001.
  • 4- Boldrini L, Bibault J-E, Masciocchi C, et al. Deep learning: A review for the radiation oncologist. Front Oncol. 2019;9:977.
  • 5- Chen C, He M, Zhu Y, et al. Five critical elements to ensure the precision medicine. Cancer Metastasis Rev. 2015;34:313–8.
  • 6- Bibault JE, Giraud P, Burgun A. Big data and machine learning in radiation oncology: State of the art and future prospects. Cancer Lett. 2016;382:110–7.
  • 7- Lambin P, van Stiphout RG, Starmans MH, et al. Predicting outcomes in radiation oncology—multifactorial decision support systems. Nat Rev Clin Oncol. 2013;10:27–40.
  • 8- Quinlan JR. Induction of decision trees. Mach Learn. 1986;1:81–106.
  • 9- Langley P, Sage S. Induction of selective Bayesian classifiers. Proceedings of the Tenth International Conference on Uncertainty in Artificial Intelligence; San Francisco, USA. Morgan Kaufmann Publishers Inc.; 1994.
  • 10- Patrick EA, Fischer FP. A generalized k-nearest neighbor rule. Inf Control. 1970;16:128–52.
  • 11- Vapnik V. (1982). Estimation of Dependences Based on Empirical Data. New York: Springer-Verlag.
  • 12- Rumelhart DE, McClelland J. (1986) Parallel distributed processing: Explorations in the microstructure of cognition. Cambridge: MIT Press.
  • 13- Miller AA. Developing an ontology for radiation oncology, master of information and communication technology. Research thesis, School of Information Systems and Technology, University of Wollongong; 2012.
  • 14- Feng M, Valdes G, Dixit N, et al. Machine learning in radiation oncology: opportunities, requirements, and needs. Front Oncol. 2018;8:110.
  • 15- Valdes G, Luna JM, Eaton E, et al. MediBoost: a patient stratification tool for interpretable decision making in the era of precision medicine. Sci Rep. 2016;6:37854.
  • 16- Caruana R, Lou Y, Gehrke J, et al. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Sidney, Australia. ACM; 2015.
  • 17- Caruana R, Niculescu-Mizil A. An empirical comparison of supervised learning algorithms. Proceedings of the 23rd International Conference on Machine Learning; Pittsburgh, Pennsylvania. ACM; 2006. 18- Fernández-Delgado M, Cernadas E, Barro S, et al. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 2014;15:3133–81.
  • 19- Roques TW. Patient selection and radiotherapy volume definition — can we improve the weakest links in the treatment chain? Clin Oncol. 2014;26:353–5.
  • 20- Sharp G, Fritscher KD, Pekar V, et al. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys. 2014;41:050902.
  • 21- Peressutti D, Schipaanboord B, van Soest J, et al. TU-AB-202-10: how effective are current atlas selection methods for atlas-based Auto-Contouring in radiotherapy planning? Med Phys. 2016;43:3738–9.
  • 22- Lustberg T, van Soest J, Gooding M, et al. Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol. 2018;126:312–7.
  • 23- Ibragimov B, Xing L. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys. 2017;44:547–57.
  • 24- Guo Y, Gao Y, Shen D. Deformable MR prostate segmentation via deep feature learning and sparse patch matching. IEEE Trans Med Imaging 2016; 35:1077–89.
  • 25- Kamnitsas K, Ledig C, Newcombe VFJ, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017;36:61–78.
  • 26- Men K, Zhang T, Chen X, 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;50:13–19.
  • 27- Cardenas CE, McCarroll RE, Court LE, 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.
  • 28- Men K, Dai J, Li Y. 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;44:6377–89.
  • 29- Boutilier JJ, Craig T, Sharpe MB, et al. Sample size requirements for knowledge-based treatment planning. Med Phys. 2016;43:1212–21.
  • 30- Schreibmann E, Fox T. Prior-knowledge treatment planning for volumetric arc therapy using feature-based database mining. J Appl Clin Med Phys. 2014;15:4596.
  • 31- Tol JP, Delaney AR, Dahele M, et al. Evaluation of a knowledge-based planning solution for head and neck cancer. Int J Radiat Oncol Biol Phys. 2015;91:612–20.
  • 32- Shiraishi S, Tan J, Olsen LA, et al. Knowledge-based prediction of plan quality metrics in intracranial stereotactic radiosurgery. Med Phys. 2015;42:908–17.
  • 33- Moore KL, Brame RS, Low DA, et al. Experience-based quality control of clinical intensity-modulated radiotherapy planning. Int J Radiat Oncol Biol Phys. 2011;81:545–51.
  • 34- Ahmed S, Nelms B, Gintz D, et al. A method for a priori estimation of best feasible DVH for organs-at-risk: validation for head and neck VMAT planning. Med Phys. 2017;44:5486–97.
  • 35- Fried DV, Chera BS, Das SK. Assessment of PlanIQ feasibility DVH for head and neck treatment planning. J Appl Clin Med Phys. 2017;18:245–50.
  • 36- McIntosh C, Welch M, McNiven A, et al. Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method. Phys Med Biol. 2017;62:5926–44.
  • 37- Valdes G, Simone CB, Chen J, et al. Clinical decision support of radiotherapy treatment planning: a data-driven machine learning strategy for patient-specific dosimetric decision making. Radiother Oncol. 2017;125:392–7.
  • 38- Chanyavanich V, Das SK, Lee WR, et al. Knowledge-based IMRT treatment planning for prostate cancer. Med Phys. 2011;38:2515–22.
  • 39- Kusters JMAM, Bzdusek K, Kumar P, et al. Automated IMRT planning in Pinnacle: a study in head-and-neck cancer. Strahlenther Onkol. 2017;193:1031–8.
  • 40- Rowbottom CG, Webb S, Oldham M. Beam-orientation customization using an artificial neural network. Phys Med Biol. 1999;44:2251.
  • 41- Llacer J, Li S, Agazaryan N, et al. Non-coplanar automatic beam orientation selection in cranial IMRT: a practical methodology. Phys Med Biol. 2009;54:1337-68.
  • 42- Valdes G, Morin O, Valenciaga Y, et al. Use of TrueBeam developer mode for imaging QA. J Appl Clin Med Phys. 2015;16:322-33.
  • 43- Li Q, Chan MF. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study. Ann N Y Acad Sci. 2017;1387:84–94.
  • 44- Valdes G, Scheuermann R, Hung CY, et al. A mathematical framework for virtual IMRT QA using machine learning. Med Phys. 2016;43:4323–34.
  • 45- Valdes G, Chan MF, Lim SB, et al. IMRT QA using machine learning: a multi-institutional validation. J Appl Clin Med Phys. 2017;18:279–84.
  • 46- Carlson JN, Park JM, Park SY, et al. A machine learning approach to the accurate prediction of multi-leaf collimator positional errors. Phys Med Biol. 2016;61:2514-31.
  • 47- Boon IS, Yong TPT, Boon CS. Assessing the role of artificial intelligence (AI) in clinical oncology: utility of machine learning in radiotherapy target volume delineation. Medicines (Basel); 2018;5:E131.
  • 48- Guidi G, Maffei N, Vecchi C, et al. Expert system classifier for adaptive radiation therapy in prostate cancer. Australas Phys Eng Sci Med. 2017;40:337–48.
  • 49- Guidi G, Maffei N, Meduri B, et al. A machine learning tool for re-planning and adaptive RT: a multicenter cohort investigation. Phys Med. 2016;32:1659–66.
  • 50- Tseng HH, Luo Y, Cui S, et al. Deep reinforcement learning for automated radiation adaptation in lung cancer. Med Phys. 2017;44:6690–705.
  • 51- Varfalvy N, Piron O, Cyr MF, et al. Classification of changes occurring in lung patient during radiotherapy using relative γ analysis and hidden Markov models. Med Phys. 2017;44:5043–50.
  • 52- Oakden-Rayner L, Carneiro G, Bessen T, et al. Precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework. Sci Rep. 2017;7:1648.
  • 53- Lao J, Chen Y, Li ZC, et al. A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci Rep. 2017;7:10353.
  • 54- Li Z, Wang Y, Yu J, et al. Deep learning based radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Sci Rep. 2017;7:5467.
  • 55- Cha KH, Hadjiiski L, Chan HP, et al. Bladder cancer treatment response assessment in CT using radiomics with deep- learning. Sci Rep. 2017;7:8738.
  • 56- Bryce TJ, Dewhirst MW, Floyd CE, et al. Artificial neural network model of survival in patients treated with irradiation with and without concurrent chemotherapy for advanced carcinoma of the head and neck. Int J Radiat Oncol Biol Phys. 1998;41:339–45.
  • 57- Gulliford SL, Webb S, Rowbottom CG, et al. Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate. Radiother Oncol. 2004;71:3–12.
  • 58- Kang J, Schwartz R, Flickinger J, et al. Machine learning approaches for predicting radiation therapy outcomes: a clinician’s perspective. Int J Radiat Oncol Biol Phys. 2015;93:1127–35.
  • 59- Zhang HH, D’Souza WD, Shi L, et al. Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT framework. Int J Radiat Oncol Biol Phys. 2009;74:1617–26.
  • 60- Pella A, Cambria R, Riboldi M, et al. Use of machine learning methods for prediction of acute toxicity in organs at risk following prostate radiotherapy. Med Phys. 2011;38:2859–67.
  • 61- Zhen X, Chen J, Zhong Z, et al. Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study. Phys Med Biol. 2017;62:8246–63.
  • 62- Vial A, Stirling D, Field M, et al. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Transl Cancer Res. 2018;7:803–16.

Machine Learning in Radiation Oncology

Yıl 2020, Cilt: 42 Sayı: 3, 339 - 349, 27.05.2020
https://doi.org/10.20515/otd.691331

Öz

Artificial intelligence (AI) is a computer science that tries to imitate human-like intelligence on machines using computer software and algorithms without direct human stimuli to perform certain tasks. Machine learning (ML) is the subunit of AI that uses data-driven algorithms that learn to emulate human behavior based on a previous example or experience. Deep learning (DL) is an ML technique that utilizes deep neural networks to construct a model. The growth and sharing of data, increased computing power, and developments in ML have initiated a transformation in healthcare. Advances in radiation oncology have generated substantial data that must be integrated with computed tomography (CT) imaging, dosimetry, and other imaging modalities before each fraction. There are many algorithms used in Radiation Oncology. Each of these methods has advantages and limitations and different computing requirements. In this paper, we aimed to review the radiotherapy (RT) process by identifying the specific areas in which the quality and efficiency of ML can be increased and a workflow chart can be created. The RT stage is divided into seven groups as patient assessment, simulation, contouring, planning, quality assessment (QA), treatment application, and patient follow-up. A systematic evaluation of the applicability, limitations and advantages of ML algorithms was performed at each stage.

Kaynakça

  • 1- Meyer P, Noblet V, Mazzara C, et al. Survey on deep learning for radiotherapy. Comput Biol Med. 2018;98:126–46.
  • 2- LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–44.
  • 3- Jarrett D, Stride E, Vallis K, et al. Applications and limitations of machine learning in radiation oncology. Br J Radiol. 2019;92:20190001.
  • 4- Boldrini L, Bibault J-E, Masciocchi C, et al. Deep learning: A review for the radiation oncologist. Front Oncol. 2019;9:977.
  • 5- Chen C, He M, Zhu Y, et al. Five critical elements to ensure the precision medicine. Cancer Metastasis Rev. 2015;34:313–8.
  • 6- Bibault JE, Giraud P, Burgun A. Big data and machine learning in radiation oncology: State of the art and future prospects. Cancer Lett. 2016;382:110–7.
  • 7- Lambin P, van Stiphout RG, Starmans MH, et al. Predicting outcomes in radiation oncology—multifactorial decision support systems. Nat Rev Clin Oncol. 2013;10:27–40.
  • 8- Quinlan JR. Induction of decision trees. Mach Learn. 1986;1:81–106.
  • 9- Langley P, Sage S. Induction of selective Bayesian classifiers. Proceedings of the Tenth International Conference on Uncertainty in Artificial Intelligence; San Francisco, USA. Morgan Kaufmann Publishers Inc.; 1994.
  • 10- Patrick EA, Fischer FP. A generalized k-nearest neighbor rule. Inf Control. 1970;16:128–52.
  • 11- Vapnik V. (1982). Estimation of Dependences Based on Empirical Data. New York: Springer-Verlag.
  • 12- Rumelhart DE, McClelland J. (1986) Parallel distributed processing: Explorations in the microstructure of cognition. Cambridge: MIT Press.
  • 13- Miller AA. Developing an ontology for radiation oncology, master of information and communication technology. Research thesis, School of Information Systems and Technology, University of Wollongong; 2012.
  • 14- Feng M, Valdes G, Dixit N, et al. Machine learning in radiation oncology: opportunities, requirements, and needs. Front Oncol. 2018;8:110.
  • 15- Valdes G, Luna JM, Eaton E, et al. MediBoost: a patient stratification tool for interpretable decision making in the era of precision medicine. Sci Rep. 2016;6:37854.
  • 16- Caruana R, Lou Y, Gehrke J, et al. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Sidney, Australia. ACM; 2015.
  • 17- Caruana R, Niculescu-Mizil A. An empirical comparison of supervised learning algorithms. Proceedings of the 23rd International Conference on Machine Learning; Pittsburgh, Pennsylvania. ACM; 2006. 18- Fernández-Delgado M, Cernadas E, Barro S, et al. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 2014;15:3133–81.
  • 19- Roques TW. Patient selection and radiotherapy volume definition — can we improve the weakest links in the treatment chain? Clin Oncol. 2014;26:353–5.
  • 20- Sharp G, Fritscher KD, Pekar V, et al. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys. 2014;41:050902.
  • 21- Peressutti D, Schipaanboord B, van Soest J, et al. TU-AB-202-10: how effective are current atlas selection methods for atlas-based Auto-Contouring in radiotherapy planning? Med Phys. 2016;43:3738–9.
  • 22- Lustberg T, van Soest J, Gooding M, et al. Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol. 2018;126:312–7.
  • 23- Ibragimov B, Xing L. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys. 2017;44:547–57.
  • 24- Guo Y, Gao Y, Shen D. Deformable MR prostate segmentation via deep feature learning and sparse patch matching. IEEE Trans Med Imaging 2016; 35:1077–89.
  • 25- Kamnitsas K, Ledig C, Newcombe VFJ, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017;36:61–78.
  • 26- Men K, Zhang T, Chen X, 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;50:13–19.
  • 27- Cardenas CE, McCarroll RE, Court LE, 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.
  • 28- Men K, Dai J, Li Y. 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;44:6377–89.
  • 29- Boutilier JJ, Craig T, Sharpe MB, et al. Sample size requirements for knowledge-based treatment planning. Med Phys. 2016;43:1212–21.
  • 30- Schreibmann E, Fox T. Prior-knowledge treatment planning for volumetric arc therapy using feature-based database mining. J Appl Clin Med Phys. 2014;15:4596.
  • 31- Tol JP, Delaney AR, Dahele M, et al. Evaluation of a knowledge-based planning solution for head and neck cancer. Int J Radiat Oncol Biol Phys. 2015;91:612–20.
  • 32- Shiraishi S, Tan J, Olsen LA, et al. Knowledge-based prediction of plan quality metrics in intracranial stereotactic radiosurgery. Med Phys. 2015;42:908–17.
  • 33- Moore KL, Brame RS, Low DA, et al. Experience-based quality control of clinical intensity-modulated radiotherapy planning. Int J Radiat Oncol Biol Phys. 2011;81:545–51.
  • 34- Ahmed S, Nelms B, Gintz D, et al. A method for a priori estimation of best feasible DVH for organs-at-risk: validation for head and neck VMAT planning. Med Phys. 2017;44:5486–97.
  • 35- Fried DV, Chera BS, Das SK. Assessment of PlanIQ feasibility DVH for head and neck treatment planning. J Appl Clin Med Phys. 2017;18:245–50.
  • 36- McIntosh C, Welch M, McNiven A, et al. Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method. Phys Med Biol. 2017;62:5926–44.
  • 37- Valdes G, Simone CB, Chen J, et al. Clinical decision support of radiotherapy treatment planning: a data-driven machine learning strategy for patient-specific dosimetric decision making. Radiother Oncol. 2017;125:392–7.
  • 38- Chanyavanich V, Das SK, Lee WR, et al. Knowledge-based IMRT treatment planning for prostate cancer. Med Phys. 2011;38:2515–22.
  • 39- Kusters JMAM, Bzdusek K, Kumar P, et al. Automated IMRT planning in Pinnacle: a study in head-and-neck cancer. Strahlenther Onkol. 2017;193:1031–8.
  • 40- Rowbottom CG, Webb S, Oldham M. Beam-orientation customization using an artificial neural network. Phys Med Biol. 1999;44:2251.
  • 41- Llacer J, Li S, Agazaryan N, et al. Non-coplanar automatic beam orientation selection in cranial IMRT: a practical methodology. Phys Med Biol. 2009;54:1337-68.
  • 42- Valdes G, Morin O, Valenciaga Y, et al. Use of TrueBeam developer mode for imaging QA. J Appl Clin Med Phys. 2015;16:322-33.
  • 43- Li Q, Chan MF. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study. Ann N Y Acad Sci. 2017;1387:84–94.
  • 44- Valdes G, Scheuermann R, Hung CY, et al. A mathematical framework for virtual IMRT QA using machine learning. Med Phys. 2016;43:4323–34.
  • 45- Valdes G, Chan MF, Lim SB, et al. IMRT QA using machine learning: a multi-institutional validation. J Appl Clin Med Phys. 2017;18:279–84.
  • 46- Carlson JN, Park JM, Park SY, et al. A machine learning approach to the accurate prediction of multi-leaf collimator positional errors. Phys Med Biol. 2016;61:2514-31.
  • 47- Boon IS, Yong TPT, Boon CS. Assessing the role of artificial intelligence (AI) in clinical oncology: utility of machine learning in radiotherapy target volume delineation. Medicines (Basel); 2018;5:E131.
  • 48- Guidi G, Maffei N, Vecchi C, et al. Expert system classifier for adaptive radiation therapy in prostate cancer. Australas Phys Eng Sci Med. 2017;40:337–48.
  • 49- Guidi G, Maffei N, Meduri B, et al. A machine learning tool for re-planning and adaptive RT: a multicenter cohort investigation. Phys Med. 2016;32:1659–66.
  • 50- Tseng HH, Luo Y, Cui S, et al. Deep reinforcement learning for automated radiation adaptation in lung cancer. Med Phys. 2017;44:6690–705.
  • 51- Varfalvy N, Piron O, Cyr MF, et al. Classification of changes occurring in lung patient during radiotherapy using relative γ analysis and hidden Markov models. Med Phys. 2017;44:5043–50.
  • 52- Oakden-Rayner L, Carneiro G, Bessen T, et al. Precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework. Sci Rep. 2017;7:1648.
  • 53- Lao J, Chen Y, Li ZC, et al. A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci Rep. 2017;7:10353.
  • 54- Li Z, Wang Y, Yu J, et al. Deep learning based radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Sci Rep. 2017;7:5467.
  • 55- Cha KH, Hadjiiski L, Chan HP, et al. Bladder cancer treatment response assessment in CT using radiomics with deep- learning. Sci Rep. 2017;7:8738.
  • 56- Bryce TJ, Dewhirst MW, Floyd CE, et al. Artificial neural network model of survival in patients treated with irradiation with and without concurrent chemotherapy for advanced carcinoma of the head and neck. Int J Radiat Oncol Biol Phys. 1998;41:339–45.
  • 57- Gulliford SL, Webb S, Rowbottom CG, et al. Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate. Radiother Oncol. 2004;71:3–12.
  • 58- Kang J, Schwartz R, Flickinger J, et al. Machine learning approaches for predicting radiation therapy outcomes: a clinician’s perspective. Int J Radiat Oncol Biol Phys. 2015;93:1127–35.
  • 59- Zhang HH, D’Souza WD, Shi L, et al. Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT framework. Int J Radiat Oncol Biol Phys. 2009;74:1617–26.
  • 60- Pella A, Cambria R, Riboldi M, et al. Use of machine learning methods for prediction of acute toxicity in organs at risk following prostate radiotherapy. Med Phys. 2011;38:2859–67.
  • 61- Zhen X, Chen J, Zhong Z, et al. Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study. Phys Med Biol. 2017;62:8246–63.
  • 62- Vial A, Stirling D, Field M, et al. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Transl Cancer Res. 2018;7:803–16.
Toplam 61 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sağlık Kurumları Yönetimi
Bölüm DERLEMELER / REVIEWS
Yazarlar

Melek Akçay 0000-0002-9042-9489

Durmuş Etiz 0000-0002-2225-0364

Yayımlanma Tarihi 27 Mayıs 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 42 Sayı: 3

Kaynak Göster

Vancouver Akçay M, Etiz D. Machine Learning in Radiation Oncology. Osmangazi Tıp Dergisi. 2020;42(3):339-4.


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