Automation and advanced computing
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Automation and advanced computing In clinical radiation oncology. 140114 Tuesday Seminar. Radiological Physics Lab. Hwiyoung Kim PhD candidate. Automation and advanced computing In clinical radiation oncology. Background and Introduction Cloud computing in CRO Aggregate data analysis

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Hwiyoung kim phd candidate

Automation and advanced computing

In clinical radiation oncology

140114 Tuesday Seminar

Radiological Physics Lab.

  • Hwiyoung KimPhD candidate


Hwiyoung kim phd candidate

Automation and advanced computing

In clinical radiation oncology


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

Automation and advanced computing

In clinical radiation oncology

01

Background andIntroduction


Hwiyoung kim phd candidate

Automation and advanced computing

In clinical radiation oncology

Background and Introduction

Single workstationmodel

Virtual machinesparallel computing environments

We’re still here (1980’s!)


Hwiyoung kim phd candidate

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?


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

Automation and advanced computing

In clinical radiation oncology

02

Cloud computing in

clinical radiation oncology


Hwiyoung kim phd candidate

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.


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

Automation and advanced computing

In clinical radiation oncology

A trend

  • http://www.businessinsider.com/


Hwiyoung kim phd candidate

Automation and advanced computing

In clinical radiation oncology

03

Aggregate data analysis


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

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!


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

Automation and advanced computing

In clinical radiation oncology

04

Parallel computation


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

Automation and advanced computing

In clinical radiation oncology

FYI

  • Distributed Calculation Framework (DCF) on Eclipse


Hwiyoung kim phd candidate

Automation and advanced computing

In clinical radiation oncology

05

Automation inclinical radiation oncology


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

Automation and advanced computing

In clinical radiation oncology

06

Improving radiotherapythrough advancedclinical informatics


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

Automation and advanced computing

In clinical radiation oncology

07

How do we get there?


Hwiyoung kim phd candidate

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)


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

Automation and advanced computing

In clinical radiation oncology

08

Conclusions


Hwiyoung kim phd candidate

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


Hwiyoung kim phd candidate

Thank youfor your attention


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