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Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset.

Publication ,  Journal Article
Li, H; Giger, ML; Yuan, Y; Chen, W; Horsch, K; Lan, L; Jamieson, AR; Sennett, CA; Jansen, SA
Published in: Acad Radiol
November 2008

RATIONALE AND OBJECTIVES: To convert and optimize our previously developed computerized analysis methods for use with images from full-field digital mammography (FFDM) for breast mass classification to aid in the diagnosis of breast cancer. MATERIALS AND METHODS: An institutional review board approved protocol was obtained, with waiver of consent for retrospective use of mammograms and pathology data. Seven hundred thirty-nine FFDM images, which contained 287 biopsy-proven breast mass lesions, of which 148 lesions were malignant and 139 lesions were benign, were retrospectively collected. Lesion margins were delineated by an expert breast radiologist and were used as the truth for lesion-segmentation evaluation. Our computerized image analysis method consisted of several steps: 1) identified lesions were automatically extracted from the parenchymal background using computerized segmentation methods; 2) a set of image characteristics (mathematic descriptors) were automatically extracted from image data of the lesions and surrounding tissues; and 3) selected features were merged into an estimate of the probability of malignancy using a Bayesian artificial neural network classifier. Performance of the analyses was evaluated at various stages of the conversion using receiver-operating characteristic analysis. RESULTS: An area under the curve value of 0.81 was obtained in the task of distinguishing between malignant and benign mass lesions in a round-robin by case evaluation on the entire FFDM dataset. We failed to show a statistically significant difference (P = .83) compared to results from our previous study in which the computerized classification was performed on digitized screen-film mammograms. CONCLUSIONS: Our computerized analysis methods developed on digitized screen-film mammography can be converted for use with FFDM. Results show that the computerized analysis methods for the diagnosis of breast mass lesions on FFDM are promising, and can potentially be used to aid clinicians in the diagnostic interpretation of FFDM.

Duke Scholars

Published In

Acad Radiol

DOI

EISSN

1878-4046

Publication Date

November 2008

Volume

15

Issue

11

Start / End Page

1437 / 1445

Location

United States

Related Subject Headings

  • Retrospective Studies
  • Radiographic Image Enhancement
  • Nuclear Medicine & Medical Imaging
  • Mammography
  • Humans
  • Female
  • Diagnosis, Computer-Assisted
  • Breast Neoplasms
  • Area Under Curve
  • 1103 Clinical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Li, H., Giger, M. L., Yuan, Y., Chen, W., Horsch, K., Lan, L., … Jansen, S. A. (2008). Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset. Acad Radiol, 15(11), 1437–1445. https://doi.org/10.1016/j.acra.2008.05.004
Li, Hui, Maryellen L. Giger, Yading Yuan, Weijie Chen, Karla Horsch, Li Lan, Andrew R. Jamieson, Charlene A. Sennett, and Sanaz A. Jansen. “Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset.Acad Radiol 15, no. 11 (November 2008): 1437–45. https://doi.org/10.1016/j.acra.2008.05.004.
Li H, Giger ML, Yuan Y, Chen W, Horsch K, Lan L, et al. Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset. Acad Radiol. 2008 Nov;15(11):1437–45.
Li, Hui, et al. “Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset.Acad Radiol, vol. 15, no. 11, Nov. 2008, pp. 1437–45. Pubmed, doi:10.1016/j.acra.2008.05.004.
Li H, Giger ML, Yuan Y, Chen W, Horsch K, Lan L, Jamieson AR, Sennett CA, Jansen SA. Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset. Acad Radiol. 2008 Nov;15(11):1437–1445.
Journal cover image

Published In

Acad Radiol

DOI

EISSN

1878-4046

Publication Date

November 2008

Volume

15

Issue

11

Start / End Page

1437 / 1445

Location

United States

Related Subject Headings

  • Retrospective Studies
  • Radiographic Image Enhancement
  • Nuclear Medicine & Medical Imaging
  • Mammography
  • Humans
  • Female
  • Diagnosis, Computer-Assisted
  • Breast Neoplasms
  • Area Under Curve
  • 1103 Clinical Sciences