Nathan Childress, Ph.D., DABR

I own Mobius Medical Systems, LP, manufacturer of Mobius3D, MobiusFX, and DoseLab

I used Mobius3D to generate results for this talk

Financial Disclaimer

Dosimetrists are exposed to every aspect of clinical care, and have the opportunity to notice many clinical improvements

There are many opportunities in modern radiotherapy clinics for mistakes

A new generation of QA software is being introduced, each containing different design elements

This presentation will focus on avoiding errors and understanding how QA systems can help automatically detect errors that slip by

Introduction

What types of errors are we looking for?◦ Commissioning◦ Treatment planning◦ Data transfer◦ Machine performance◦ Patient positioning

How do you evaluate new QA tools?◦ Efficiency◦ Accuracy◦ Reliability: false negatives / false positives

How are the current QA systems designed?◦ Treatment log files with CBCT◦ EPID only◦ EPID with CBCT

Overview

2001-2004: PhD student at MD Anderson Cancer Center◦ Developed open-source DoseLab software (IMRT QA)

2004-2010: Clinical physicist at The Methodist Hospital in Houston, TX

2010-present: Founded Mobius Medical Systems, LP◦ Designed DoseLab TG-142 for machine QA◦ Designed Mobius3D for treatment plan QA◦ Designed MobiusFX for patient delivery QA◦ Designed CBCT module for patient positioning QA

My Background

Mia Claire Childress (mc2)

Will be shamelessly used to illustrate this presentation

Will never experience a normal dating life

Common Errors

Most clinics do not verify their “CT to density” table, which can lead to errors in patients but not in QA devices

Beam modeling is a complex and long process

Many clinics do not measure MLC-defined output factors◦ IROC-Houston has published values for comparison◦ Jaws are set to 10x10◦ MLC is set to 2x2 to 6x6◦ If your TPS cannot accurately calculate these, it cannot accurately

calculate IMRT or VMAT plans

Commissioning errors

No beam model is perfect

No planning system calculation is perfect, even with a great beam model

Plan complexity magnifies these inaccuracies by delivering small segments of radiation with large MUs

Beam modeling accuracy

IROC-Houston phantom mailers are available for ~$1200◦ Includes comparing OSLD and film measurements to your TPS◦ ~18% of centers historically have failed hetereogeneous

verifications using 7% / 4 mm criteria

Perform measurements of IMRT and VMAT plans◦ Do not ignore failures

Check MLC-defined OFs against IROC-Houston published data◦ IROC-Houston finds that 65% of institutions do not match MLC

feld output factors within 2.5%◦ 10% do not match within 5%

Checking TPS beam models

All these errors can be overwhelming

In my opinion, high beam modulation is the number one preventable cause of treatment inaccuracies

More modulation creates prettier pictures and DVHs in the TPS, but magnifies beam modeling and delivery errors

Modulation can be described by the Modulation Factor

Beam modeling and delivery errors are multiplied by a patient’s Modulation Factor to determine the accuracy of the delivered dose

Treatment planning errors

A precise value is not necessary, as this value is used as a general indication of plan complexity level

Modulation Factor is typically defined as the MU of a modulated plan divided by the MU of an open field plan

But I’m lazy

So this talk will use MU / Rx dose (cGy) to calculate the Modulation Factor

Example!◦ 50 Gy / 25 fx = 200 cGy/fraction◦ Total MU = 800◦ Modulation factor: 800 / 200 = 4

Calculating the Modulation Factor

1840 MU / 1150 cGy = 1.6 Modulation

+2 mm uniform MLC error

4014 MU / 800 cGy = 5.0 Modulation

5.0 Modulation + 4 mm

4697 MU, 2 Gy, 3 splits (7.8 Modulation)

7.8 Modulation + 4 mm

Tumor Difference vs. Modulation (+2 mm uniform MLC error)

0%

5%

10%

15%

20%

25%

30%

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0

Tum

or d

ose

diffe

renc

e

Modulation factor

Don’t just copy someone else’s plan

Do not overmodulate beams◦ Delivering many <2 cm leaf

openings magnifies problems in your TPS dose calculation

◦ Small segments also magnify delivery errors

◦ Look at your modulation factor. Is it above 5?

Do not have beams that go through mobile parts of patients (such as arms)

Create safe treatment plans

We’ve found TPS => R&V and R&V => linac data transfer errors are rare◦ A couple minor errors reported in around a million fractions

It is more common for a treatment parameter (jaw setting, MU, etc.) to be manually changed without generating a new plan

Data transfer errors

QA is good for you, but not always fun

Static and Step and Shoot◦ 0.1 mm

Sliding window and VMAT◦ 0.5 mm to 3 mm, depending on MLC speed◦ Typically around 1 mm for each MLC leaf

Jaw, gantry, and collimator performance typically have negligible impact on the delivered dose

No delivery is perfect. Completing a delivery with no interlocks means MLCs were within 0-5 mm of their prescribed positions.

Delivery errors

Patient setup errors◦ External marks no longer point to the same place◦ Wrong marks were used to setup patient◦ Patient was not positioned correctly◦ IGRT system is not correctly calibrated

Anatomy changes◦ Weight loss / gain◦ Tumor migration

Patient and/or tumor motion

Patient positioning errors

How to Evaluate QA Tools

Automatic tools reduce human errors

Every QA system detects different types of errors depending on its design

Only measuring planar dose limits your ability to both segregate error sources and evaluate the clinical relevance of detected errors

Converting to 3D dose in the patient planning CT allows you to see the impact of errors on the DVH and discern clinical relevance◦ No system places measurement devices in the patient, so the

accuracy of reconstructed patient dose is critical

QA tools: Accuracy

No QA system is perfect

Errors flagged by a patient-specific QA system must be investigated and resolved before treatment◦ Frequent occurrences of false positives and false negatives are

unacceptable

Introducing new data sources to a QA system increases your potential for false positives◦ Device miscalibration◦ Device setup errors◦ Noise / artifacts in images

Ignoring data in a QA system increases your potential for false negatives◦ Homogeneous QA for a heterogeneous patient

QA tools: Reliability

Some things aren’t as great as you hope

Efficiency is not just the level of automation in a QA system

Efficiency is a result of accuracy, reliability, and automation◦ At least one clinic has a panel of clinicians to evaluate false

positive results for a daily QA system

Performing daily QA amplifies any problem in the QA system◦ When an error is detected, it must be investigated and resolved

before the next fraction◦ As clinics start to rely more heavily on automatic error detection,

they must have manual processes to detect weak areas of the system

◦ If there are a lot of false positives, the system constantly generates errors that are ignored

QA tools: Efficiency

Efficiency lets us drink and watch TV

Current QA Systems

Pick the right tools

Generated by Varian and Elekta linear accelerators

Include Planned and Delivered values

Measurements recorded 3-100 times per second

MLC and jaw positions recorded with 0.1 mm precision

Gantry and collimator angles recorded with 0.1° mm precision

Treatment log files

Log files are automatically monitored as patients are being treated

Log files are compared to the plan to verify data transfer

Their data is used to calculate the machine performance for each patient’s delivery

Measured MLC, gantry, collimator, etc. positions can be used to find the 3D dose in the patient

This process can be performed both before treatment as IMRT QA and during treatments

QA systems: Treatment log files

Pros◦ Fully automatic◦ Measurements in log files are highly precise (0.01 mm, 0.1°)◦ Measurements have primary and backup detectors◦ Allows nearly perfect data transfer checking◦ Separates error sources for easily problem resolution

Cons◦ Relies on internal dosimeters◦ Cannot detect machine calibration errors (machine QA is essential)◦ Do not contain patient positioning information

Systems◦ MMS Mobius3D, others in development

QA systems: Treatment log files

QA lets you sleep well

CBCT is widely used, but almost always qualitatively

Automatic software is used for the initial 3D matching but not for further analysis

CBCTs are not as accurate at reporting densities (HUs) compared to CTs◦ This makes dose calculation on CBCTs difficult

CBCT results can be used to identify when patients need to be rescanned and replanned, but this is also highly qualitative

CBCT today

Systems take CBCTs and quantitatively compare them to CTs◦ HU renormalization is required

This leads to actual numbers – ie, % passing

Passing rates can be used both to double check anatomy / positioning before treatment and as a metric to quantitatively indicate when to replan

Clinics can now set limits. For example:◦ >90%: treat with no further review◦ 80-90%: treat after physician review◦ <80%: resimulate

QA systems: CBCT quantitative analysis

Pros◦ Fully automatic◦ Can be performed for every CBCT before treatment◦ Can be used to indicate when replanning is necessary◦ Sensitive to patient positioning, weight loss, and weight gain

Cons◦ Cannot detect differences in soft tissues◦ Cannot detect tumor migration in soft tissue◦ Cannot be used to calculate 3D dose in patient

Systems◦ MMS Mobius3D, others in development

QA systems: CBCT quantitative analysis

Think about what your QA system does

DIR creates a 3D map of how a patient’s anatomy has moved from one image to another, ie, the planning CT to a CBCT

This 3D map can be used to automatically place contours on CBCTs, to deform the planning CT to the shape of the CBCT for dose calculations, etc.

This is an incredibly complex process that can be difficult to perform manually and harder to do automatically ◦ Anatomy can do more than move or squish – it can disappear,

reappear, change density, etc.◦ CBCT artifacts make it difficult to know which parts of the image

to trust

Deformable image registration (DIR)

Applying DIR to CBCT allows dose to be calculated on the patient’s daily treatment images

Cumulated dose and DVHs can be generated

This allows clinicians to identify the need for replanningbased on DVH objectives

QA systems: CBCT with DIR

Pros◦ Allows dose to be calculated on CBCT◦ Allows daily DVHs to be generated◦ Can detect tumor migration and other soft tissue differences

Cons◦ Relies heavily on accuracy of DIR algorithm Any inaccuracy in DIR can invalidate results

◦ Unclear what to do when minor deviations are detected ◦ Output only helps clinicians determine if a new sim is needed

Systems◦ Standard Imaging Adaptivo, others in development

QA systems: CBCT with DIR

Don’t get devastated by QA choices

Everyone knows and loves EPID images!

The EPID panel was created for patient imaging but now is being used for many other purposes

EPIDs capture the beam after it exits the patient, meaning it can theoretically detect nearly every problem in a treatment delivery◦ Patient positioning◦ Patient anatomy◦ Machine performance

EPID images

The fluence from an EPID image is compared to a previous treatment or a calculated gold standard◦ Treatment plan + CT image + 3D calculation at EPID panel

Fluences are compared to automatically detect anatomy / positioning / machine performance errors◦ If fluences are compared to the first fraction, you lose the ability

to check for systematic errors

The measured fluence can be backprojected to calculate 3D dose in the patient

QA systems: EPID images

Pros◦ Fully automatic◦ Can check patient positioning and machine performance

Cons◦ Does not detect data transfer errors◦ Typical limits flag 30% of treatments and have 99% false positives◦ Difficult to calculate gold standard EPID image◦ Fluence image differences are difficult to interpret to fix problems

Systems◦ Elekta iViewDose, Sun Nuclear PerFRACTION, Math Resolutions

Dosimetry Check, Dosisoft EPIgray, Epidose EPIQA, Standard Imaging Adaptivo

QA systems: EPID images

Don’t regret your QA program

If your QA system detects a failure, does your resolution process make sense?

If your secondary calculation fails, do you repeat with minor adjustments or analyze the cause of the failure?

If your IMRT QA fails, do you repeat with minor adjustments or analyze the cause of the failure?

If the standard process is to ignore failures until a pass is achieved, your quality system is broken

Clinical use of QA systems

Don’t cheat on your QA

The Future of QA

Technology is fun

Integrating the Healthcare Enterprise-Radiation Oncology (IHE-RO) is an ASTRO-sponsored initiative for improving the functionality of the radiation oncology clinic

The IHE-RO task force develops IHE Integration Profiles, which specify how industry standards can address specific clinical problems and ambiguities

The upcoming Quality Assurance with Plan Veto (QAPV) profile will force plans to be validated before being delivered to the patient, at every treatment day

IHE-RO

Linacs will send DICOM-RT files to Check Providers, which will detect if a plan contains critical errors

Vendors can implement custom checks◦ Must be at least single-point MU recalculation◦ Can be based on dose recalculations and previous treatment

information◦ Can be as complex as vendor would like

The entire process is automatic, and the beam cannot be turned on until a Check Provider completes a valid check

Tentative availability: 2018

IHE-RO QA Plan Veto Profile

In the future, the first fraction of a patient’s delivery can automatically be stopped for certain errors◦ Incorrect data transfer◦ Use of an unapproved treatment plan◦ Treatment plans that would likely result in injury

Subsequent fractions of a patient’s delivery can automatically be stopped for additional reasons◦ Incorrect data processing◦ Poor linac performance◦ Inaccurate patient positioning

QAPV and New QA Tools

A good QA system makes you happy

There are many sources of error in radiation treatments, and dosimetrists should be aware of how to detect them

A new generation of QA tools is becoming available to help your clinic

New tools feature automation, but clinics must be aware of their limitations

Summary

Just when you think you have things figured out, something else comes along…

Questions?