The Cancer Imaging Archive (TCIA) Public Access

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Derived objects from the DWI and DCE acquisitions are included for the convenience of the user. Some of these are not strictly DICOM compliant and may not be readable by all DICOM software packages. Parametric maps and segmentations are believed to be identical to those utilized in the primary analysis and test/retest repeatability analyses of the 6698 Trial, but in isolated cases modifications may have occurred in subsequent processing.

DWI “Package” for each study with analyzable diffusion scan

  1. 4 b-value DW Trace Images
    1. Multiple 2-b MSMB DWI acquisitions were combined (1 site)
    2. 3 directional DW images were geometrically averaged to generate trace images for studies where directional DWI data was saved
  2. 4-b value ADC maps
    1. A low threshold (b=0 image > 10) was applied to all studies
    2. ADC calculated as linear fit to log(S(b)) = log(S(0)) – ADC * b
    3. Voxels below threshold or with ADC < 0.0 are set to 0
  3. Manually defined multi-slice tumor segmentations
    1. ROIs defined for published primary analysis
    2. Tumor segmented on all slices with identifiable tumor referencing high-b image, ADC map, and DCE subtraction image (for localization)
    3. The segmentations are provided both as DICOM SEG objects and as DICOM MRI objects on TCIA

These DWI derived series can be identified either by Series Description (0008,103e) or by Series Number (0020,0011). See Table 1 for series identification details.

Manual DWI Whole-Tumor Segmentation Method

The segmentations provided in the derived diffusion objects are those generated for and used in the primary analysis and test/retest analyses for the ACRIN-6698 study. Tumor was identified on post-contrast DCE subtraction images and then localized on the ADC map. (apparent diffusion coefficient) Multi-slice, whole-tumor regions of interest (ROIs) were manually defined by selecting regions with low ADC and hyperintensity on a high b-value DWI (b=600 or 800 s/mm2) while avoiding adjacent adipose and fibroglandular tissue, biopsy clip artifacts, and regions of high T2 signal (e.g., seroma and necrosis). For multicentric/multifocal disease, all disease regions were included in the ROI. Region definition was done at the UCSF processing lab using in-house software tools developed with IDL (Exelis Visual Information Solutions, Boulder, Colorado).

DCE “Package” for each study

  1. Single-breast ipsilateral cropped DCE images
    1. As this was done primarily for computational efficiency, low matrix-sized images (<384 voxels per row) were stored and processed without cropping
  2. Enhancement maps
    1. PEearly: Enhancement at effective post-contrast time 120-150 sec
    2. PElate: Enhancement at effective post-contrast time ~450 sec
    3. SER: Signal enhancement ratio    SER =  PEearly / PElate
      Note: SER values were scaled by 1000 for integer storage. DICOM software utilizing the rescale slope and intercept fields [ DICOM fields (0028, 1052-1054)] should return the original SER values in the range from 0.0 – 3.0.
    4. All maps were stored as DICOM MRI objects
  3. Functional Tumor Volume (FTV) analysis mask
    1. Segmentation storing FTV masking. Encodes:
      1. Pre-contrast background threshold
      2. Minimum PE threshold (70% nom.)
      3. Manually defined rectangular volume of interest (VOI) for enhancing tumor analysis
      4. Manually defined “OMIT” regions used to exclude non-tumor enhancing regions that encroached on the rectangular VOI
    2. Stored as DICOM SEG object
    3. See this document for further information: <Analysis mask files description.docx>

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ACRIN 6698 analysis included use of an image quality control system developed specifically for determination of the analyzability of breast DWI for quantitative cancer treatment monitoring. The image quality control system was comprised of three sequential but independent assessment stages: protocol compliance, image quality and usability, and ROI confidence. Since protocol compliance, image quality, and ROI confidence varied between exams for the same patient, each step was performed independently for each MRI exam.

Protocol Compliance Assessment

First, image metadata in DICOM tags was assessed for compliance with specified imaging parameters. Deviations from the specified imaging protocol were documented and classified into minor (non-critical to study aims) and major (critical to the study aims). Images exhibiting major protocol deviations were flagged and omitted from further analysis.

Image Quality and Usability Assessment

Next, the overall image quality and the usability of images for the study endpoints were evaluated. Readers scored image quality using a standard letter grading scale of A (best), B (good), C (average), D (poor) and F (worst) on fat suppression, severity of artifacts (displacement, ghosts, and distortions) and signal-to-noise ratio (SNR). These metrics were selected to reflect the most common and debilitating limitations in breast DWI. Fat suppression and SNR were scored using only the b=0 images while the severity of artifacts was evaluated using all DWI images. Scores were assigned numeric values (A=4 to F=0) and the geometric mean of the scores for fat suppression, artifacts and SNR was computed to yield a composite quality score. Each composite quality score was classified into overall high-, moderate-, or low-quality categories.

Separate from this image quality assessment, a subjective pass/fail assessment was made of image usability for evaluating diffusion parameters such as lesion ADC. This criterion was established to ensure that images of sufficient quality to address the scientific aim of the study were not rejected, e.g., a poor quality score due to excessive imaging artefacts might not result in rejection if the artefacts did not encroach on the region of the enhancing tumor.

ROI Confidence Assessment

After the full multi-slice/multi-region ROIs were placed, they were scored qualitatively based on reader confidence that the ROI faithfully represented the tissue of interest. The goal was to characterize the whole tumor while excluding fat, tissue, and tumor necrosis. ROIs were scored on a numerical scale of 3 for high confidence, 2 for moderate confidence and 1 for low confidence. Inclusion in the primary analysis was determined based on the image quality and ROI confidence assessments.

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