automated qa: is this the future? - ucsf cme...outline •qa in radiation oncology. •how to judge...

Automated QA:Is this the Future?

Olivier Morin, PhDUniversity of California San Francisco

UCSF Annual CourseMarch 23rd, 2014

Sunday, March 23, 2014

Outline

• QA in Radiation Oncology.

• How to judge the quality of a QA program?

• Automation -> road to improvements?

• Machine learning in RT. (Smart Plan Import)

• Linac QA using EPID.

• QA trend for hyprofractionation (daily Linac QA).


Outline



• Automation -> road to improvements?





Outline



• Automation road to improvements?





Outline








Outline








Outline








QA in RT

Physicians

Physicists

Therapists

Nurses

Dosimetrists

Administration

Medical residents

Physics residentsQA

IT


QA is a multi-layered series of

cross-checks done by ALL

professionals involved.

QA in RT

Physicians

Physicists

Therapists

Nurses

Dosimetrists

Administration

Medical residents

Physics residents

IT


Quality of QA program?

• QA in RT good to prevent severe errors.• According to ASTRO estimates, preventable mistakes occur in less than 0.01%

of the time (NY 2011).

• In contrast, the rate of occurrence of preventable events is 3X higher for surgery (http://www.hopkinsmedicine.org/surgery/faculty/Makary).

• Most of the treatment plan checks are performed by individuals using checklists.

• Evaluation of tests results too reliant and staff expertise.

• IMRT and VMAT delivery QA labor intensive and limited.

Good at preventing the preventables?

Room for improvement:


Quality of QA program?

• QA in RT good to prevent severe errors.• According to ASTRO estimates, preventable mistakes occur in less than 0.01%

of the time (NY 2011).

• In contrast, the rate of occurrence of preventable events is 3X higher for surgery (http://www.hopkinsmedicine.org/surgery/faculty/Makary).

• Most of the treatment plan checks are performed by individuals using checklists.

• Evaluation of tests is too reliant on staff expertise.

• IMRT and VMAT delivery QA labor intensive and limited.

Good at preventing the preventables?

Room for improvement:


Building a New Approach

From Discovery to Design: The Evolution of Human Factors in Healthcarehttp://www.longwoods.com/content/22900

• Human factor engineering for safe implementation of new technology.

• People focused approaches tend to be less effective.

• System focused and automation would be more effective.














Machine Learning in RT

• ML algorithm to determine if tests or series of parameters are considered normal.

• Algorithm must be trained with a set of normal tests or parameters.

• Multi-parameter algorithm (prescription, MU, gantry angles, modulation,etc.).

• Algorithm playing a bigger role in RT.


















• Algorithm could play an important role in RT.


Smart Plan Import

• Train (using ML) patient management system (MOSAIQ or Aria) to detect when parameters are outside the norm.

• Smart Plan Import use patient information and prescription and treatment technique to verify key parameters.

• Smart plan could detect when imaging reference information is not adequate.

• Concept of virtual machine could be used.


Smart Plan Import


• Smart Plan Import uses past experiences along with patient information, prescription and treatment technique to verify key parameters.




Smart Plan Import






Smart Plan Import






Linac QA• AAPM TG-142 provide

recommendations to assure quality of treatments.

• UCSF QA procedures take 30 mins daily + 6 hours monthly.

• IMRT and VMAT is commonly performed on all new plans prior to Tx.

• With automation and 2D detectors more and better verifications could be done.




















TG-142 / Daily QA


Make Daily QA Part of the Clinic


TG-142 / Monthly QA


Example of Monthly QA


Systematic & Complete Docs


Is the Current Daily QA Adequate for Hypofractionation?

• High dose per fraction.

• Heavily modulated treatments.

• Absolutely needs patient specific QA at the moment.

• 2D detectors and automation could open the door to a more complete daily QA.


Proposed Automated Daily QA

Therapistarrives 30

mins before Tx

Deliver daily QA sequence

(25 mins)

EPID Images analyzed by software


Daily Machine Verification


Virtual WedgesVirtual Wedges


Flatness and Symmetry


MLC SequencePatient specific QA vs. same daily MLC sequence

vs.


In Summary

• Machine learning algorithms could play a significant role in the day-to-day verifications.

• Patient-specific QA is labor intensive and effort is under way to obtain more by doing less.

• A trend towards hypofractionation may also call for revised daily QA procedure.

• Automation can save time and detect machine problems (i.e. MLC) before they lead to errors or down time.


In Summary


• Patient-specific QA is labor intensive and efforts are under way to obtain more by doing less.

• A trend towards hypofractionation may also call for revised daily QA procedure.



In Summary



• A trend towards hypofractionation may also call for a more comprehensive daily QA procedure.



In Summary



• A trend towards hypofractionation may also call for a more comprehensive daily QA procedure.



Automated QA:Is this the Future?

Olivier Morin, PhDUniversity of California San Francisco

UCSF Annual CourseMarch 23rd, 2014


automated qa: is this the future? - ucsf cme...outline •qa in radiation oncology. •how to judge...

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