Profile: CT Lung Nodule Volume Measurement for Primary/Regional Nodes and Metastatic Sites

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Please review and consider:

  • are the claims appropriate, useful, sufficient
  • would it make sense to move any claims to a different profile
  • Precursors under Claims should be about validating a Profile Claim is achievable
  • Precursors under Details should be about determining a Profile Detail

Summary: You'll be able to measure lung tumor volume with a repeatability of 18% for tumors greater than 10mm in Longest Diameter.

Profile Claims (what users will be able to achieve)

Claim #1: Can create, store, retrieve images of lung tumors

Precursor: None; proven DICOM (CT Storage)

Claim #2: Can create, store, retrieve linear, area and volume measurements made on lung tumor images

Precursor: None; proven DICOM (SR Storage w Templates, e.g. Chest CAD)

Claim #3: Can create, store, and retrieve mark ups of lung tumors, i.e., region of interest (ROI) boundaries

Precursor: Need Sample Implementation
Chest CAD polylines or New DICOM Segmentation objects (by pixel) are likely sufficient, but should try out a sample implementation to confirm (and identify key Details to require in the Profile). Possibilities for data storage include polylines, voxels, and polygons/triangles. See also Segmentation and Markup Formats

Claim #4: Can measure lung tumor volume with repeatability of 18% for tumors greater than 10mm in Longest Diameter.

Rationale: For uniformly expanding cubes and solid spheres, an increase in the RECIST defined uni-dimensional Longest Diameter of a Measurable Lesion corresponds to an increase in volume of about 72%. To diagnose Progressive Disease at a change of about one half that volume, 36%, the noise needs to be less than about 18%. The claim is thus set to be "twice as sensitive as RECIST".
<What do we mean by repeatability>
How should the repeatability be expressed? It's easier to meet % targets for larger tumors. Should we use mm3 instead? Or should we state % for a certain sized tumor? There is a description in Jim Mulshines work that we can copy here?
Precursor: Demonstrate this accuracy and repeatability is easily achievable
Groundwork: Test-Retest measurements of FDA phantoms i.e., very-best-case-scenario, with variability one order of magnitude less than variability in "real life", i.e., algorithm returns variability of less than 1.5%
<Relevant Groundwork Link 2:> Test-Retest measurements of small sample of NIST cases, i.e., nearly-best-case-clinical-scenario, with variability for measurement of isolated, simple lung tumors of less than 3% (up to 4 times the noise in phantoms and less than one fifth the noise expected in real life scenarios).
<Relevant Groundwork Link 3:> Test-Retest measurements of a few well behaved masses in the MSKCC coffee break study of less than 10% between Image Set 1 and Image Set 2 of each patient studied twice in succession. This 10% threshold is somewhat capriciously based on the assumption that the precision of measurement in selected MSKCC coffee break tumors will be twice as good as that which can be achieved in most clinical trial scenarios.
Precursor: Should thought be given to revising the RECIST definitions?

Profile Details (what equipment and users must do to achieve it)

The Profile defines the following roles and several transactions and activities they participate in:

  • Acquisition System
  • Measurement System
  • Measurer
  • ...

Activity: Acquisition System Calibration

Detail: Site staff shall conform to the QA program defined by the device manufacturer.

Activity: Patient Preparation

Detail: Staff shall prepare the patient according to the local standard of care.

Precursor: Decide if we need/can be prescriptive about any of the details in efforts to "standardize human behavior" or local procedures.

Detail: The following details shall be recorded in the <???> System, manually by the Staff if necessary.

Contrast administration: (Agent, dose, route)
  • The standards for this are currently evolving.
  • To be comparable (e.g. to subtract to get change values), measurements must be made under consistent contrast administration.
  • Requiring no contrast (like ACRIN 6678 did) would be less of an issue than requiring contrast which has potential patient health issues.
Creatinine Clearance: (renal function).
Patient Positioning: (prone-supine, arms up/down, etc.)
  • Probably don't need to specify for metastatic lung cancer except that the same patient should be imaged the same way each time .
Breath Hold:
  • Either "single breath hold" acquisitions or suspended respiration with high % of end inspiration are necessary to separate structures and make lesion more conspicuous.

Precursor: How should the details be recorded about the preparation of each actual patient?
  • DICOM provides a way to encode most of these details in the image headers, but we may need to require the operator to enter them.

Activity: Image Acquisition

Detail: The acquisition system shall support saving and easily calling up saved acquisition protocols.

Precursor: Do we need standard naming?
  • Could use UPICT or ACRIN proper name.
  • Sites might prefer “site recognizable” aliases (but need to still know it is the prescribed protocol)

Detail: The acquisition system shall produce images with the following characteristics:

Precursor: Determine which characteristics of the resulting images matter?
  • Slice width - Ideal: Target: Acceptable:
    • direct component of voxel size
  • Slice interval - Ideal: no gap or overlap Target: ditto Acceptable: ???
    • gaps may "truncate" the spatial extent of the tumor, overlap may distort it's boundaries
  • Isotropic Voxels - Ideal: yes Target: yes Acceptable: ???
    • isotropic voxels reduce the volume measurement error effect of tumor orientation (which is difficult to control)
  • Voxel Size - Ideal: 4mm Target: 5mm Acceptable: 8mm
    • smaller voxels reduce partial volume effects and (likely) provide higher precision
    • but larger voxels reduce storage/network requirements and (likely) reduce reading time
  • Motion Artifact - Ideal: no artifact Target: no artifact Acceptable: "minimal??"
    • motion artifacts may produce false targets and distort the size of existing targets
  • Noise Level - Ideal: "minimal?" Target: "low" Acceptable: "predictable?"
    • greater levels of noise may degrade segmentation by humans or algorithms
  • Spatial Registration:
    • <Does it need to be accurately registered to a known frame of reference?>

Detail: The acquisition system shall support configuration of the following acquisition parameters:

Precursor: Determine which acquisition parameters matter
  • KVP - Ideal: Target: 120 Acceptable:
  • mAs (medium patient) - Ideal: Target: 95-125 Acceptable: 140
    • higher mAs lowers noise but increases dose
  • Rotation Speed - Ideal: Target: Acceptable:
    • faster rotation reduces the breath hold requirements and reduces the likelihood of motion artifacts
  • Collimation width - Ideal: Target: Acceptable:
  • # of channels - Ideal: Target: Acceptable:
  • Recon. Kernel Characteristics: - Ideal: Target: Acceptable:
    • <the relationship between kernel characteristics and our goals/claims is likely complex. What can we say or at least identify as needing investigation>
  • Recon. Algorithm:
  • Recon. Kernel Name - informational
  • Scanner Model - informational
    • indicates the model has been used successfully with the described parameters

Precursor: What value ranges for each parameter constitute an acceptable “baseline”?
  • Late Stage (IIIb and IV) Lung Cancer in World Wide Clinical Trials (pharma base case): Outer ring of quality must be RI = 5 mm; next ring RI = 3 mm. Inner ring specified by Professor Mulshine and colleagues for top-shelf clinical trials of neoadjuvant therapy in earlier stage disease at RI < 1.5 mm.
  • Note some earlier stage NSCLC trials done with radiofrequency ablation (RAPTURE, R. Lencioni, PI) included some stage I cancers, with all lesions < 3.5 cm}
  • Some parameters need ranges to "normalize" results across different patient sizes
Consider existing protocols:
ACRIN 6678 Quality Control Parameters for CT Scan Tumor Volumetric Measurements specified:
  • (Slice width, Slice interval, Voxel Size, Absence of Motion Artifact) &
  • (KVP, mAs, Rotation Speed, Collimation width, # of channels, Scanner Model, Recon. Algorithm, Non-use of Intravenous Contrast)
Note: many parameters are specified as a range, some depending on the size of the patient
NLST (National Lung Screening Trial) Acquisition Parameters specified:
  • (Slice width, Slice interval, # of Images) &
  • (KVP, mA, mAs, Effective mAs, Rotation Speed, Collimation width, # of channels, Detector "width", "MODE", Pitch, Table increment, Table speed, Scan time, Scanner Model, Recon. Algorithm, Dose)
Note: some of these parameters are redundant (i.e. can be calculated from other parameters), many parameters are specified as a range, some depending on the size of the patient
The ACRIN protocol may be prefereable since NLST was for a screening study, not for measuring progressive disease
<Insert link to UPICT protocol specifications by Professor McNitt-Gray and colleagues>?

RAPTURE Trial Phase II trial NCT00690703 (now closed) at ClinicalTrials.Gov
  • Perhaps Dr. Lencioni could suggest methods which would have helped him assess the results in this trial?

Precursor: What uniform language should be used for documenting image acquisition protocols in the profile.
DICOM is working on a new object for storing protocols electronically (prescribed or performed)
Perhaps Manufacturers should provide CDs with acquisition parameters as they did for the MRI study of brain volumes to the Alzheimer's Disease Neuroimaging Trial sponsored by the Foundation for NIH
UPICT is working on common terms for the protocol parameters and possibly a standard presentation
CT Acquisition Protocol Groundwork

Activity: Image Reconstruction

<Is there any reason not to fold the Image Reconstruction activity into the Image Acquisition activity and just treat them as a pair? Is there any need/value for them to be separate?>

E.g. what kernel to use? Kernel will be important. Even more so in liver than lung, and in spinal mets assessments.

Detail: The acquisition system shall be able to perform reconstruction with the following parameters:

  • Reconstruction interval: 5mm without any gaps
  • Kernel: <???>
Further discussion may be necessary. Some sites complain that when compared to slices with an 8mm interval and a 5mm gap (a common clinical standard), the use of a 5mm interval with no gap slows down throughput and increases reading time, radiation exposure and storage requirements. It seems likely that 5mm interval with no gap is necessary to achieve the claims, so the related costs are unavoidable and manageable.
This may also tie in to specifying the characteristics of the resulting images rather than the parameters of reconstruction for certain makes/models. Specify what to achieve, rather than how to achieve it.

Transaction: Transfer Images

Detail: The acquisition system shall support DICOM CT Storage as SCU.

Detail: The measurement system shall support DICOM CT Storage as SCP and DICOM Q/R as SCU

Activity: Measurement

Detail: The measurement system shall support the following measurements:

Precursor: What measurements are useful for evaluating lung tumors
  • Bitvol <because it is the typical “detailed” volume measurement>
  • RECIST <because it is the current gold standard and we need it to compare>
  • Modified RECIST (J. Natl. Cancer Inst. 2008;100:698-711) <to support wider cancer etiology than HCC>
<consider just adding a bunch of tools if they are easy to implement>
Precursor: What types of cases/issues must the measurement system demonstrate being able to handle?
  • E.g. attachment points,
Precursor: What accuracy is initially sufficient to be useful? Precision of measurement is the primary objective. Accuracy is less important to the base case for pharma. Accuracy becomes increasingly important to the inner rings of quality, reaching its maximum in screening studies of asymptomatic people with risk factors for lung cancer.
Precursor: What repeatability is initially sufficient to be useful?
Precursor: What accuracy/repeatability can be easily achieved?
<Insert link to relevant Groundwork>
<Insert link to very preliminary image analysis in very-best-case-scenario of extremely well demarcated, simple lung tumors which suggest test-retest variability is less than 1% when RI is 5 mm>
Precursor: What is the theoretical limit for accuracy/repeatability with typical equipment
<Insert link to relevant Groundwork>
<Insert links to image analysis of well demarcated tumors in the MSKCC coffee break images as the most optimistic boundary, and analysis of complex masses invading solid tissues as the most realistic boundary. First link will be to image analysis by RadPharm, Inc. Other links will be provided by software developers as the data become available.

Detail: The measurer shall be able to diagnose Progressive Disease at one half the change in volume associated with RECIST line-lengths.

Precursor: What do we need to specify about the measurer? Human oversight will be required. In the first stage, a trained technologist or image analysis specialist will select tumors for automatic boundary demarcation. In the next stage, the image analysis specialist will be able to manually correct portions of the boundary where either the algorithm failed or the mass becomes too complex to reliably follow over the course of treatment. In the final stage, a trained radiologist will accept or revise the mark ups.
Precursor: What is the limit on accuracy/repeatability due to the measurer (reader)? The limit of inter-rater reliability will be such that thresholds for diagnosing Progressive Disease will be within one time-point assessment in a series of time-points for patients enrolled in longitudinal trials. The need for adjudication between discrepant time-point assessments will be less for volumetric image analysis than for ordinary RECIST 1.1 assessments.
See 1A Reader Variability Study

<Should we add an Activity: Measurer Training to train/confirm the skill of each measurer>

Transaction: Transfer Measurements

Detail: The Measuring System shall support storage of the measurements in <???> format.

Detail: The Measuring System shall support storage of the segmentation in <???> format.

Precursor: Need to choose Segmentation and Markup Formats

... Consider CDISC as a way to require measurement systems to provide data in a format that is easily consumed by Clinical Trials systems/databases. Note that CDISC has done image work on CT Oncology (related to RECIST). Might not be interested in CDISC change categories, but the measurements they specify is useful (have included volume).

<Insert link to CDISC imaging work>

IHE has worked with CDISC on some general IT profiles and so there may be some IHE transactions we could borrow.


RECIST: Response Evaluation Criteria In Solid Tumors.

  • “New Guidelines to Evaluate the Response to Treatment in Solid Tumors”, by Patrick Therasse, Journal of the National Cancer Institute, Vol. 92, No. 3, February 2, 2000, pp. 205-216.
  • Eisenhauera EA, Therasseb P, Bogaertsc J, et a. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer 2009; 45: 228-247.

To Do

  • Review standard requirements traceability structures to see if they can help/be used
  • Consider regrouping the Details under the Roles rather than Activities if that makes it clearer what each system or person must do to comply.