Independent evaluation of computer classification of malignant and benign calcifications in full-field digital mammograms.

RATIONALE AND OBJECTIVES To evaluate whether a computer-aided diagnosis (CADx) technique can accurately classify breast calcifications in full-field digital mammograms (FFDMs) as malignant or benign. The computer technique was developed previously on screen-film mammograms (SFMs) in which individual calcifications were identified manually. The present study evaluated the computer technique independently on a new database of FFDM images with automatic detection of the individual calcifications. MATERIALS AND METHODS We analyzed 49 consecutive FFDM cases (19 cancers) that showed suspicious calcifications. Four mammography radiologists read soft-copy mammograms retrospectively and electronically indicated the region of calcifications in each image. The computer then automatically detected the individual calcifications within the indicated region and analyzed eight features of calcification morphology and distribution to arrive at an estimated likelihood of malignancy. The radiologists entered Breast Imaging Report and Data System assessments before and after seeing the computer results. Performance was analyzed using receiver operating characteristic analysis. RESULTS Despite variability in radiologist-indicated regions of calcifications, the computer achieved consistently high performance taking input from the four radiologists (receiver operating characteristic curve area, A(z): 0.80, 0.80, 0.78, and 0.77; differences not statistically significant). Previous results showed that the computer technique achieved an A(z) value of 0.80 on SFMs, which improved radiologists' performance significantly. CONCLUSIONS The computer technique appears to maintain consistently high performance in classifying calcifications in FFDMs as malignant or benign without requiring substantial modification from its initial development on SFMs. The computer performance appears to be robust with respect to variations in radiologists' input.

[1]  K. Berbaum,et al.  Receiver operating characteristic rating analysis. Generalization to the population of readers and patients with the jackknife method. , 1992, Investigative radiology.

[2]  C. Metz,et al.  Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data. , 1998, Statistics in medicine.

[3]  Per Skaane,et al.  Screen-film mammography versus full-field digital mammography with soft-copy reading: randomized trial in a population-based screening program--the Oslo II Study. , 2004, Radiology.

[4]  M. Giger,et al.  Malignant and benign clustered microcalcifications: automated feature analysis and classification. , 1996, Radiology.

[5]  A. Jemal,et al.  Cancer Statistics, 2006 , 2006, CA: a cancer journal for clinicians.

[6]  Luisa P. Wallace,et al.  Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system. , 2004, Journal of the National Cancer Institute.

[7]  K Doi,et al.  Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis. , 1990, Investigative radiology.

[8]  M. Giger,et al.  Breast cancer: effectiveness of computer-aided diagnosis observer study with independent database of mammograms. , 2002, Radiology.

[9]  T. Freer,et al.  Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. , 2001, Radiology.

[10]  Robert M. Nishikawa,et al.  Comparison of Computerized Image Analyses for Digitized Screen-Film Mammograms and Full-Field Digital Mammography Images , 2006, Digital Mammography / IWDM.

[11]  J. Swets,et al.  Enhanced interpretation of diagnostic images. , 1988, Investigative radiology.

[12]  C. Floyd,et al.  Artificial neural network: improving the quality of breast biopsy recommendations. , 1996, Radiology.

[13]  R M Nishikawa,et al.  Dependence of computer classification of clustered microcalcifications on the correct detection of microcalcifications. , 2001, Medical physics.

[14]  J. M. Pruneda,et al.  Computer-aided mammographic screening for spiculated lesions. , 1994, Radiology.

[15]  Berkman Sahiner,et al.  Computer aided detection of clusters of microcalcifications on full field digital mammograms. , 2006, Medical physics.

[16]  N. Petrick,et al.  Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study. , 1999, Radiology.

[17]  C. D'Orsi,et al.  Clinical comparison of full-field digital mammography and screen-film mammography for detection of breast cancer. , 2002, AJR. American journal of roentgenology.

[18]  N Karssemeijer,et al.  Automated classification of clustered microcalcifications into malignant and benign types. , 2000, Medical physics.

[19]  K. Doi,et al.  Computer-aided diagnosis in radiology: potential and pitfalls. , 1999, European journal of radiology.

[20]  R. Nishikawa,et al.  The use of a priori information in the detection of mammographic microcalcifications to improve their classification. , 2003, Medical physics.

[21]  C E Metz,et al.  Some practical issues of experimental design and data analysis in radiological ROC studies. , 1989, Investigative radiology.

[22]  M. Giger,et al.  Improving breast cancer diagnosis with computer-aided diagnosis. , 1999, Academic radiology.

[23]  C. D'Orsi,et al.  Diagnostic Performance of Digital Versus Film Mammography for Breast-Cancer Screening , 2005, The New England journal of medicine.

[24]  C. D'Orsi,et al.  Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening , 2006 .

[25]  Arthur Burgess.  On the noise variance of a digital mammography system. , 2004, Medical physics.

[26]  E. Pisano,et al.  Perspective on digital mammography. , 2001, Seminars in roentgenology.

[27]  K. Doi,et al.  Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications. , 2001, Radiology.

[28]  R. Schmidt,et al.  Comparison of independent double readings and computer-aided diagnosis (CAD) for the diagnosis of breast calcifications. , 2006, Academic radiology.