Internet-based computer technology on radiotherapy.

Recent rapid development of Internet-based computer technologies has made possible many novel applications in radiation dose delivery. However, translational speed of applying these new technologies in radiotherapy could hardly catch up due to the complex commissioning process and quality assurance protocol. Implementing novel Internet-based technology in radiotherapy requires corresponding design of algorithm and infrastructure of the application, set up of related clinical policies, purchase and development of software and hardware, computer programming and debugging, and national to international collaboration. Although such implementation processes are time consuming, some recent computer advancements in the radiation dose delivery are still noticeable. In this review, we will present the background and concept of some recent Internet-based computer technologies such as cloud computing, big data processing and machine learning, followed by their potential applications in radiotherapy, such as treatment planning and dose delivery. We will also discuss the current progress of these applications and their impacts on radiotherapy. We will explore and evaluate the expected benefits and challenges in implementation as well.

[1]  Antonio Lioy,et al.  On the Robustness of Applications Based on the SSL and TLS Security Protocols , 2007, EuroPKI.

[2]  Sanja Petrovic,et al.  Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning , 2016, Artif. Intell. Medicine.

[3]  B. Dean,et al.  Review: Use of Electronic Medical Records for Health Outcomes Research , 2009, Medical care research and review : MCRR.

[4]  Shu-Hsien Liao,et al.  Data mining techniques and applications - A decade review from 2000 to 2011 , 2012, Expert Syst. Appl..

[5]  J. Arabloo,et al.  Health technology assessment of image-guided radiotherapy (IGRT): A systematic review of current evidence , 2016, Medical journal of the Islamic Republic of Iran.

[6]  J. Chow,et al.  Dose-volume consistency and radiobiological characterization between prostate IMRT and VMAT plans , 2016 .

[7]  Ying Xiao,et al.  How Will Big Data Improve Clinical and Basic Research in Radiation Therapy? , 2016, International journal of radiation oncology, biology, physics.

[8]  Tae-Suk Suh,et al.  Toward a web-based real-time radiation treatment planning system in a cloud computing environment. , 2013, Physics in medicine and biology.

[9]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[10]  Andre Dekker,et al.  Standardized data collection to build prediction models in oncology: a prototype for rectal cancer. , 2016, Future oncology.

[11]  Ivan Laptev,et al.  Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  C Gomà,et al.  CloudMC: a cloud computing application for Monte Carlo simulation , 2013, Physics in medicine and biology.

[13]  James C. L. Chow,et al.  A performance evaluation on monte carlo simulation for radiation dosimetry using cell processor , 2011, J. Comput. Methods Sci. Eng..

[14]  Steve B. Jiang,et al.  GPU-based high-performance computing for radiation therapy , 2014, Physics in medicine and biology.

[15]  Mehmet Engin,et al.  Early prostate cancer diagnosis by using artificial neural networks and support vector machines , 2009, Expert Syst. Appl..

[16]  J. V. Trapp,et al.  Radiotherapy Monte Carlo simulation using cloud computing technology , 2012, Australasian Physical & Engineering Sciences in Medicine.

[17]  Will Venters,et al.  A critical review of cloud computing: researching desires and realities , 2012, J. Inf. Technol..

[18]  Jaydip Sen,et al.  Security and Privacy Issues in Cloud Computing , 2013, ArXiv.

[19]  Andrea De Mauro,et al.  What is big data? A consensual definition and a review of key research topics , 2015, AIP Conference Proceedings.

[20]  Fang-Fang Yin,et al.  Utilizing knowledge from prior plans in the evaluation of quality assurance , 2015, Physics in medicine and biology.

[21]  Ivan K. W. Lai,et al.  Knowledge cloud system for network collaboration: A case study in medical service industry in China , 2012, Expert Syst. Appl..

[22]  Yoshihito Namito,et al.  [EGS5 code, outline and how to start using the code]. , 2013, Igaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics.

[23]  Kevin Souris,et al.  Fast multipurpose Monte Carlo simulation for proton therapy using multi- and many-core CPU architectures. , 2016, Medical physics.

[24]  James C L Chow,et al.  Some computer graphical user interfaces in radiation therapy. , 2016, World journal of radiology.

[25]  J. Alberto Espinosa,et al.  Big Data: Issues and Challenges Moving Forward , 2013, 2013 46th Hawaii International Conference on System Sciences.

[26]  Dorian C. Arnold,et al.  Radiation therapy calculations using an on-demand virtual cluster via cloud computing , 2010, ArXiv.

[27]  Jürgen Debus,et al.  Image-guided radiotherapy: a new dimension in radiation oncology. , 2011, Deutsches Arzteblatt international.

[28]  Issam El Naqa,et al.  Big Data Analytics for Prostate Radiotherapy , 2016, Front. Oncol..

[29]  Andre Dekker,et al.  Creating a data exchange strategy for radiotherapy research: Towards federated databases and anonymised public datasets , 2014, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[30]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[31]  Ulrike Schick,et al.  Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapy. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[32]  Marios D. Dikaiakos,et al.  Cloud Computing: Distributed Internet Computing for IT and Scientific Research , 2009, IEEE Internet Computing.

[33]  R. Arenson.  Picture archiving and communication systems. , 1992, The Western journal of medicine.

[34]  Gordon E. Moore,et al.  Progress in digital integrated electronics , 1975 .

[35]  Vojtech Huser,et al.  Impending Challenges for the Use of Big Data. , 2016, International journal of radiation oncology, biology, physics.

[36]  J. Flickinger,et al.  Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective. , 2015, International journal of radiation oncology, biology, physics.

[37]  I. Kawrakow,et al.  The EGSnrc Code System: Monte Carlo Simulation of Electron and Photon Transport , 2016 .

[38]  Big Data in Radiation Oncology: Challenges and Opportunities , 2014 .

[39]  Ravi Vadapalli,et al.  Grid-Enabled Treatment Planning for Proton Therapy Using Monte Carlo Simulations , 2011, Nuclear technology.

[40]  Zahid Anwar,et al.  Data mining techniques and applications — A decade review , 2017, 2017 23rd International Conference on Automation and Computing (ICAC).

[41]  F Lohr,et al.  A machine learning tool for re-planning and adaptive RT: A multicenter cohort investigation. , 2016, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[42]  Mark D. Hill,et al.  Amdahl's Law in the Multicore Era , 2008 .

[43]  Arenson Rl.  Picture archiving and communication systems. , 1992 .

[44]  Antonio Ruiz-Martínez,et al.  Architectures and Protocols for Secure Information Technology Infrastructures , 2014 .

[45]  Sasa Mutic,et al.  A Systems Approach Using Big Data to Improve Safety and Quality in Radiation Oncology. , 2016, International journal of radiation oncology, biology, physics.

[46]  Garth A. Gibson,et al.  DiskReduce: RAID for data-intensive scalable computing , 2009, PDSW '09.

[47]  James C. L. Chow.  Performance optimization in 4D radiation treatment planning using Monte Carlo simulation on the cloud , 2016, J. Comput. Methods Sci. Eng..

[48]  Xun Jia,et al.  A GPU-based finite-size pencil beam algorithm with 3D-density correction for radiotherapy dose calculation. , 2011, Physics in medicine and biology.

[49]  Vojtech Huser,et al.  Overview of the American Society for Radiation Oncology-National Institutes of Health-American Association of Physicists in Medicine Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big Data. , 2016, International journal of radiation oncology, biology, physics.

[50]  D. Jaffray,et al.  Review of image-guided radiation therapy , 2007, Expert review of anticancer therapy.

[51]  D. G. Lewis,et al.  High-performance computing for Monte Carlo radiotherapy calculations , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[52]  Georgios Kalantzis,et al.  Accelerated event-by-event Monte Carlo microdosimetric calculations of electrons and protons tracks on a multi-core CPU and a CUDA-enabled GPU , 2014, Comput. Methods Programs Biomed..

[53]  Xun Jia,et al.  MO‐F‐BRB‐03: A GPU‐Based Finite‐Size Pencil Beam (FSPB) Algorithm with 3D‐ Density Correction for Radiotherapy Dose Calculation , 2011 .

[54]  J. Baró,et al.  PENELOPE: An algorithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter , 1995 .

[55]  So-Yeon Park,et al.  A machine learning approach to the accurate prediction of multi-leaf collimator positional errors , 2016, Physics in medicine and biology.

[56]  Lei Xing,et al.  Toward real-time Monte Carlo simulation using a commercial cloud computing infrastructure , 2011, Physics in medicine and biology.

[57]  Elizabeth M. Belding-Royer,et al.  A secure routing protocol for ad hoc networks , 2002, 10th IEEE International Conference on Network Protocols, 2002. Proceedings..

[58]  S. Mutic,et al.  Advances and future of Radiation Oncology. , 2013, Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology.

[59]  H. Shirato,et al.  Four-dimensional treatment planning and fluoroscopic real-time tumor tracking radiotherapy for moving tumor. , 2000, International journal of radiation oncology, biology, physics.

[60]  M. Bulsara,et al.  Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods. , 2016, Medical physics.

[61]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[62]  Issam El Naqa,et al.  Introduction to Big Data in Radiation Oncology: Exploring Opportunities for Research, Quality Assessment, and Clinical Care. , 2016, International journal of radiation oncology, biology, physics.