Nathan Childress, Ph.D., DABR

Transcription

1 Nathan Childress, Ph.D., DABR

2 Financial Disclaimer I own Mobius Medical Systems, LP, manufacturer of Mobius3D, MobiusFX, and DoseLab I used Mobius3D to generate results for this talk

3 Introduction 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

4 Overview 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

5 My Background : PhD student at MD Anderson Cancer Center Developed open-source DoseLab software (IMRT QA) : 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

6 Mia Claire Childress (mc 2 ) Will be shamelessly used to illustrate this presentation Will never experience a normal dating life

7 Common Errors

8 Commissioning 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

9 Beam modeling accuracy 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

10 Checking TPS beam models 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%

11 All these errors can be overwhelming

12 Treatment planning errors 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

13 Calculating the Modulation Factor 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

14 1840 MU / 1150 cgy = 1.6 Modulation

15 +2 mm uniform MLC error

16 4014 MU / 800 cgy = 5.0 Modulation

17 5.0 Modulation + 4 mm

18 4697 MU, 2 Gy, 3 splits (7.8 Modulation)

19 7.8 Modulation + 4 mm

20 Tumor Difference vs. Modulation (+2 mm uniform MLC error) 30% 25% Tumor dose difference 20% 15% 10% 5% 0% Modulation factor

21 Don t just copy someone else s plan

22 Create safe treatment plans 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)

23 Data transfer errors 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

24 QA is good for you, but not always fun

25 Delivery errors 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.

26 Patient positioning 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

27 How to Evaluate QA Tools

28 Automatic tools reduce human errors

29 QA tools: Accuracy 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

30 QA tools: Reliability 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

31 Some things aren t as great as you hope

32 QA tools: Efficiency 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

33 Efficiency lets us drink and watch TV

34 Current QA Systems

35 Pick the right tools

36 Treatment log files Generated by Varian and Elekta linear accelerators Include Planned and Delivered values Measurements recorded times per second MLC and jaw positions recorded with 0.1 mm precision Gantry and collimator angles recorded with 0.1 mm precision

37 QA systems: 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

38 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

39 QA lets you sleep well

40 CBCT today 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

41 QA systems: CBCT quantitative analysis 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

42 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

43 Think about what your QA system does

44 Deformable image registration (DIR) 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

45 QA systems: CBCT with 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 replanning based on DVH objectives

46 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

47 Don t get devastated by QA choices

48 EPID images 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

49 QA systems: 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

50 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

51 Don t regret your QA program

52 Clinical use of QA systems 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

53 Don t cheat on your QA

54 The Future of QA

55 Technology is fun

56 IHE-RO 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

57 IHE-RO QA Plan Veto Profile 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

58 QAPV and New QA Tools 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

59 A good QA system makes you happy

60 Summary 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

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

62 Questions?