Hwiyoung Kim PhD candidate

1. Automation and advanced computing In clinical radiation oncology 140114 Tuesday Seminar Radiological Physics Lab. • Hwiyoung KimPhD candidate

2. Automation and advanced computing In clinical radiation oncology

3. Background and Introduction Cloud computing in CRO Aggregate data analysis Parallel computation Automation in CRO Improving radiotherapy throughadvanced clinical informatics How do we get there? Conclusions INDEX

4. Automation and advanced computing In clinical radiation oncology 01 Background andIntroduction

5. Automation and advanced computing In clinical radiation oncology Background and Introduction Single workstationmodel Virtual machinesparallel computing environments We’re still here (1980’s!)

6. Automation and advanced computing In clinical radiation oncology A question • Is the current computing infrastructures are ideal for the task of modern clinical radiotherapy? • If radiotherapy computing systems were designed from scratch in 2013, what would they look like?

7. Automation and advanced computing In clinical radiation oncology In this vision 20/20 paper • Identify trends in advanced computing • Consider how an ideal computing environment could enhance patient care • Expected developments for a new paradigm in RT computing systems • Cloud-based service models • Aggregate data analysis • Parallel computation • Automation

8. Automation and advanced computing In clinical radiation oncology 02 Cloud computing in clinical radiation oncology

9. Automation and advanced computing In clinical radiation oncology Cloud computing in clinical radiation oncology • Definition of “cloud computing” by NIST • “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released w/ minimal management effort or service provider interaction” • E.g., Google Gmail/drive etc.

10. Automation and advanced computing In clinical radiation oncology Check the email Go home or find PC-room Turn on the computer Connect to the internet Launch the browser and access to Gmail Take your phone Launch the Gmail application Whenever and Everywhere

11. Automation and advanced computing In clinical radiation oncology Cloud-based RT informatics • Off-sitestorage of clinical RT data • Imaging, treatment planning data, etc. • Service as a Service(SaaS) modules for clinical RT applications • Treatment Management System(TMS, e.g., RT chart) • Treatment Planning System(TPS, e.g., Eclipse) • Protected health information and clinical data access controlled by the local institution • Multiple institutions housing data ona single platform Platform as a Service(PaaS) • NO local hardware platform • NO HW/SW administration

12. Automation and advanced computing In clinical radiation oncology Cloud computing in clinical radiation oncology • ARIA V11 app. For iPad • https://itunes.apple.com/kr/app/aria-v11/id489782767?mt=8 • http://www.redjournal.org/article/S0360-3016(12)01351-X • Not a cloud computing technically • But a nice movement • Realization awaits a time • Data/system transfer to an extra-institutional provider • Uncertainty of how and when • Benefit is not immediately obvious

13. Automation and advanced computing In clinical radiation oncology A trend • http://www.businessinsider.com/

14. Automation and advanced computing In clinical radiation oncology 03 Aggregate data analysis

15. Automation and advanced computing In clinical radiation oncology Aggregate data analysis • Definition: The synthesis of quantitative information from a multiplicity of measurements • Summary statistic of interest across • Multiple patients • Multiple treatment plans • Multiple treatment fractions • Multiple treatment devices, etc. • Basis of clinical trials • Distinguished from the “single-patient” focus of clinical RT

16. Automation and advanced computing In clinical radiation oncology Aggregate data analysis • Recognition that investigations over “multiple patients” • Quality improvement investigations are hindered by the fact that clinical data repositories are not designed to facilitate multi-patient retrospective data analysis • Variations in local SW and data management Single-patient analysis Multi-patient analysis

17. Automation and advanced computing In clinical radiation oncology Examples • Wants to know whether IMRT differs from VMATin terms of the average rectum V75 • Quantify, based on CBCT imaging, how frequently the prostate is found to be outside of the PTV margins

18. Automation and advanced computing In clinical radiation oncology Aggregated data analysis • Standardizedclinical practice • In the use of common cloud-based informatics system • Should give clinicians the ability to construct system-level queries • Not a application-level queries: some versatility would likely be lost • Not involve any data transfer • in this era of IGRT where a multitude of secondary image studies exist • Plug-in service modules are prerequisite • Such as auto-segmentation • Data transfer • Image manipulation • Data analysis JUST QUERY IT!

19. Automation and advanced computing In clinical radiation oncology Machine learning • Discover correlations in multivariate data • Make predictions based on those correlations • E.g., prediction of acute toxicity in OAR following prostate RT • http://link.aip.org/link/?MPHYA6/38/2859/1

20. Automation and advanced computing In clinical radiation oncology 04 Parallel computation

21. Automation and advanced computing In clinical radiation oncology Parallel computation • Medical images • Involve finer detail • Multimodality •  rapidly increased computational demands • High Performance Computing (HPC) • Graphics Processing Unit (GPU) • General-Purpose GPU (GPGPU) • Compute Unified Device Architecture (CUDA) • Grid computing • Grid Analysis of Radiological Data (AGIR) • European Grid Infrastructure (EGI), Open-Science Grid

22. Automation and advanced computing In clinical radiation oncology FYI • Distributed Calculation Framework (DCF) on Eclipse

23. Automation and advanced computing In clinical radiation oncology 05 Automation inclinical radiation oncology

24. Automation and advanced computing In clinical radiation oncology Examples • Image registration • Treatment planning • Time consuming task w/ great output variability • Plans are created much faster and with greater consistency and quality • Using machine learning on prior aggregate data • MCO (Raysearch): Multi-Criteria Optimization(Pareto)http://www.raysearchlabs.com/en/RayStation/PlanOptimization • RapidPlan (Varian): Knowledge-based planning https://www.varian.com/us/oncology/software/rapidplan.html • Plan evaluation • QA and QC tasks • QUASAR™ ADQ (Automated Delivery QA, MODUS Medical Devices)http://modusmed.com/adq-software • QUASAR™ eQA 2.0: automated EPID image analysis for QA (TG142)http://modusmed.com/eqa-software

25. Automation and advanced computing In clinical radiation oncology AutoMQA • Automated mechanical QA using a smartphone • Motion sensors (gyroscope, accelerator sensor, magnetic field sensor) • Gantry/collimator rotation indicators • High-resolution camera • Jaw position indicator • Light/radiation field coincidence • Cross-hair centering • Distance indicator (ODI) • Table translation and rotation

26. Automation and advanced computing In clinical radiation oncology Automation in clinical radiation oncology • Standardization of communication/database formats: ease the interconnectivity of the systems • DICOM, DICOM-RT, IHE*-RO • Benefits • (*integrating the Healthcare Enterprise) • Cost Reduction • Productivity • More tasks could be completedin a typical work day • Equipment , labor cost • Availability • Performance • Materials are availablein timely manner • Lower cost, higher qualitysafer/faster/predictable workflow • Reliability • Reduced variability/repetitive tasks

27. Automation and advanced computing In clinical radiation oncology A challenge of medical physicist • Adapt quality and safety programs to a new era of clinical automation • Large amount of works • Increased complexity of modalities • Failure modes and effects analysis(FMEA) methodology needed • TG 100, forthcoming

28. Automation and advanced computing In clinical radiation oncology 06 Improving radiotherapythrough advancedclinical informatics

29. Automation and advanced computing In clinical radiation oncology Improvements of RT through advanced clinical informatics • PlanVeto • Sharing LINAC measurement data • FMEA analysis • Improved productivity Quality and Safety Adaptive RT Efficiency Clinical Effectiveness • On-board image processing • Automated plan adaptation • Direct QA/QC analysis • Improved reliability • Aggregated data analysis • Extra-Institutional data sharing • http://www.nature.com/news/specials/datasharing/index.html

30. Automation and advanced computing In clinical radiation oncology 07 How do we get there?

31. Automation and advanced computing In clinical radiation oncology How do we get there? • User-configurable Application Programming Interfaces(API) • Direct access to data and the tools • Academic competition better aggregate clinical studies • Vendor competition faster and better SW implementations • Widespread expectation from clinicians that next-generation computing must be a part of clinical SW (consensus)

32. Automation and advanced computing In clinical radiation oncology Medical Physicist • Do Foundational work and implementation • Safe implementation of any practice-altering technologies • Relevant topics in IT could be incorporated into education programs • Integrating more explicit IT/programming components into medical physics (CAMPEP) residencies might be warranted

33. Automation and advanced computing In clinical radiation oncology 08 Conclusions

34. Automation and advanced computing In clinical radiation oncology Conclusions • Though the integration of at least some of the ideas is nearly certain, large uncertainties remain • The progress toward next-generation clinical informatics systems will bring about extremely valuable developments in some aspects

35. Thank youfor your attention