Improving Breast Cancer Detection and Diagnosis with CAD

Summary

Principal Investigator: Bin Zheng
Affiliation: University of Pittsburgh
Country: USA
Abstract: Computer-aided detection (CAD) of breast cancer is rapidly becoming a well accepted clinical practice. Studies have found that radiologists' attitude toward and acceptance of CAD-cued micro-calcification clusters and masses were substantially different. Due to the high sensitivity, radiologists heavily rely on CAD-cued results while searching for micro-calcifications. However, the lower CAD sensitivity for mass detection (including a fraction of subtle masses being cued only on one view) and the higher false-positive detection (FP) rates reduce radiologists' confidence in CAD-cued masses. As a result, radiologists frequently discard CAD-cued subtle masses in the clinical practice. To improve CAD performance and increase radiologists' confidence in using CAD-cued masses in their decision making, we propose two observer-focused innovative approaches to develop and optimize CAD schemes. By maintaining a comparable FP rate to current commercial CAD systems, the new approaches aim to either increase the number of masses being cued on both ipsilateral (CC and MLO) views or cue more subtle masses by eliminating a fraction of other regions that can be easily identified and classified by radiologists without using CAD. To test these approaches, we propose three specific tasks. First, we will develop a unique multi-view based CAD scheme. To more sensitively detect and better match subtle mass regions, we introduce a concept of limited viewing of specific regions into the arena of CAD development. After detecting a matching strip on the ipsolateral view, the scheme applies a second highly sensitive detection scheme only to this strip to identify matched regions. To control for and reduce FP rates, the scheme limits the number of possible matched candidates to less than one per image. Second, we will develop an integrated CAD scheme that includes a combined score for both detection and classification. To improve direct use of features computed by the detection module in the classification task, we will apply a new dual active contour algorithm that should improve mass region segmentation. We will separately optimize two machine learning classifiers to generate a detection score (the likelihood of being a true-positive mass) and a classification score (the likelihood of each detected mass for malignancy) for each segmented region. We will then develop a fusion method to combine these two scores and generate a new summary index that is more heavily weighted for subtle masses. Using this scheme, we can change the current mass detection based cuing method to a new cancer-based cueing method. Third, we will conduct a pilot observer performance study to investigate radiologists' performance under three CADcueing modes (using the current commercial single-image based, the new multi-view based, and the new integrated CAD schemes). The reading results will be compared and analyzed using both ROC and JAFROC methodologies. We note that the approach is substantially different than focusing on incremental improvements in image based detection schemes in that the observer's actual use (or not) of the CADcued regions drives our objectives in this project, resulting in a targeted development effort.
Funding Period: 1997-09-15 - 2012-04-30
more information: NIH RePORT

Top Publications

  1. ncbi A comparison of two data analyses from two observer performance studies using Jackknife ROC and JAFROC
    Bin Zheng
    Department of Radiology, University of Pittsburgh, 300 Halket Street, Suite 4200, Pittsburgh, Pennsylvania 15213 3180, USA
    Med Phys 32:1031-4. 2005
  2. pmc Improving performance of content-based image retrieval schemes in searching for similar breast mass regions: an assessment
    Xiao Hui Wang
    Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
    Phys Med Biol 54:949-61. 2009
  3. pmc An ellipse-fitting based method for efficient registration of breast masses on two mammographic views
    Jiantao Pu
    Department of Radiology, University of Pittsburgh, 3362 Fifth Avenue, Pittsburgh, Pennsylvania 15213, USA
    Med Phys 35:487-94. 2008
  4. pmc Optimization of reference library used in content-based medical image retrieval scheme
    Sang Cheol Park
    Department of Radiology, University of Pittsburgh, 3362 Fifth Avenue, Pittsburgh, Pennsylvania 15213, USA
    Med Phys 34:4331-9. 2007
  5. pmc Interactive computer-aided diagnosis of breast masses: computerized selection of visually similar image sets from a reference library
    Bin Zheng
    Department of Radiology, Imaging Research Center, University of Pittsburgh, Pittsburgh, PA 15213, USA
    Acad Radiol 14:917-27. 2007
  6. ncbi Multiview-based computer-aided detection scheme for breast masses
    Bin Zheng
    Department of Radiology, University of Pittsburgh, 300 Halket Street, Suite 4200, Pittsburgh, Pennsylvania 15213, USA
    Med Phys 33:3135-43. 2006
  7. ncbi How does the perception of a lesion influence visual search strategy in mammogram reading?
    Claudia Mello-Thoms
    Department of Radiology, University of Pittsburgh, 300 Halket Street, Suite 4200, Pittsburgh, PA 15213, USA
    Acad Radiol 13:275-88. 2006
  8. ncbi A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment
    Bin Zheng
    Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
    Med Phys 33:111-7. 2006
  9. ncbi Head-mounted versus remote eye tracking of radiologists searching for breast cancer: a comparison
    Claudia Mello-Thoms
    University of Pittsburgh, Department of Radiology, Pittsburgh, PA 15213, USA
    Acad Radiol 13:203-9. 2006
  10. ncbi Performance and reproducibility of a computerized mass detection scheme for digitized mammography using rotated and resampled images: an assessment
    Bin Zheng
    Department of Radiology, University of Pittsburgh, 300 Halket St, Ste 4200, Pittsburgh, PA 15213 3180, USA
    AJR Am J Roentgenol 185:194-8. 2005

Detail Information

Publications11

  1. ncbi A comparison of two data analyses from two observer performance studies using Jackknife ROC and JAFROC
    Bin Zheng
    Department of Radiology, University of Pittsburgh, 300 Halket Street, Suite 4200, Pittsburgh, Pennsylvania 15213 3180, USA
    Med Phys 32:1031-4. 2005
    ..In the second study, LABMRMC showed no significant differences for any paired data sets and JAFROC showed a significant difference for one pair. In 15 of 16 pairs, p values computed by JAFROC were lower than those computed by LABMRMC...
  2. pmc Improving performance of content-based image retrieval schemes in searching for similar breast mass regions: an assessment
    Xiao Hui Wang
    Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
    Phys Med Biol 54:949-61. 2009
    ..107 +/- 0.718, 2.301 +/- 0.733 and 2.298 +/- 0.743. The study demonstrates that due to the diversity of medical images, CBIR schemes using multiple image features and mass size-based ROIs can achieve significantly improved performance...
  3. pmc An ellipse-fitting based method for efficient registration of breast masses on two mammographic views
    Jiantao Pu
    Department of Radiology, University of Pittsburgh, 3362 Fifth Avenue, Pittsburgh, Pennsylvania 15213, USA
    Med Phys 35:487-94. 2008
    ..The results demonstrate the feasibility of the proposed method to automatically identify masses depicted on CC and MLO views, which may improve future development of multiview based CAD schemes...
  4. pmc Optimization of reference library used in content-based medical image retrieval scheme
    Sang Cheol Park
    Department of Radiology, University of Pittsburgh, 3362 Fifth Avenue, Pittsburgh, Pennsylvania 15213, USA
    Med Phys 34:4331-9. 2007
    ..914 (p < 0.01). The study demonstrates that increasing reference library size and removing poorly effective references can significantly improve I-CAD performance...
  5. pmc Interactive computer-aided diagnosis of breast masses: computerized selection of visually similar image sets from a reference library
    Bin Zheng
    Department of Radiology, Imaging Research Center, University of Pittsburgh, Pittsburgh, PA 15213, USA
    Acad Radiol 14:917-27. 2007
    ..The objective of this study is to develop and test a new ICAD scheme that aims improve visual similarity of ICAD-selected reference regions...
  6. ncbi Multiview-based computer-aided detection scheme for breast masses
    Bin Zheng
    Department of Radiology, University of Pittsburgh, 300 Halket Street, Suite 4200, Pittsburgh, Pennsylvania 15213, USA
    Med Phys 33:3135-43. 2006
    ..7% (from 539 to 411). The study demonstrated that the new multiview-based CAD scheme could substantially increase the number of masses being cued on two ipsilateral views while reducing the case-based false-positive detection rate...
  7. ncbi How does the perception of a lesion influence visual search strategy in mammogram reading?
    Claudia Mello-Thoms
    Department of Radiology, University of Pittsburgh, 300 Halket Street, Suite 4200, Pittsburgh, PA 15213, USA
    Acad Radiol 13:275-88. 2006
    ....
  8. ncbi A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment
    Bin Zheng
    Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
    Med Phys 33:111-7. 2006
    ....
  9. ncbi Head-mounted versus remote eye tracking of radiologists searching for breast cancer: a comparison
    Claudia Mello-Thoms
    University of Pittsburgh, Department of Radiology, Pittsburgh, PA 15213, USA
    Acad Radiol 13:203-9. 2006
    ..We compared performance and visual search parameters of radiologists detecting masses on mammograms by using both a head-mounted (HDMT) and a remote (REM) eye tracker...
  10. ncbi Performance and reproducibility of a computerized mass detection scheme for digitized mammography using rotated and resampled images: an assessment
    Bin Zheng
    Department of Radiology, University of Pittsburgh, 300 Halket St, Ste 4200, Pittsburgh, PA 15213 3180, USA
    AJR Am J Roentgenol 185:194-8. 2005
    ....
  11. pmc Improving performance of computer-aided detection scheme by combining results from two machine learning classifiers
    Sang Cheol Park
    Department of Radiology, University of Pittsburgh, 3362 Fifth Avenue, Pittsburgh, PA 15213, USA
    Acad Radiol 16:266-74. 2009
    ....