Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial.

PURPOSE To evaluate a noncommercial computer-aided detection (CAD) program for breast cancer detection with screening mammography. MATERIALS AND METHODS A CAD program was developed for mammographic breast cancer detection. The program was applied to 2,389 patients' screening mammograms at two geographically remote academic institutions (institutions A and B). Thirteen radiologists who specialized in breast imaging participated in this pilot study. For each case, the individual radiologist performed a prospective Breast Imaging Reporting and Data System (BI-RADS) assessment after viewing of the screening mammogram. Subsequently, the radiologist was shown CAD results and rendered a second BI-RADS assessment by using knowledge of both mammographic appearance and CAD results. Outcome analysis of results of examination in patients recalled for a repeat examination, of biopsy, and of 1-year follow-up examination was recorded. Correct detection with CAD included a computer-generated mark indicating a possible malignancy on craniocaudal or mediolateral oblique views or both. RESULTS Eleven (0.46%) of 2,389 patients had mammographically detected nonpalpable breast cancers. Ten (91%) of 11 (95% CI: 74%, 100%) cancers were correctly identified with CAD. Radiologist sensitivity without CAD was 91% (10 of 11; 95% CI: 74%, 100%). In 1,077 patients, follow-up findings were documented at 1 year. Five (0.46%) patients developed cancers, which were found on subsequent screening mammograms. The area where the cancers developed in two (40%) of these five patients was marked (true-positive finding) by the computer in the preceding year. Because of CAD results, a 9.7% increase in recall rate from 14.4% (344 of 2,389) to 15.8% (378 of 2,389) occurred. Radiologists' recall rate of study patients prior to use of CAD was 31% higher than the average rate for nonstudy cases (10.3%) during the same time period at institution A. CONCLUSION Performance of the CAD program had a very high sensitivity of 91% (95% CI: 74%, 100%).

[1]  L. Tabár,et al.  Potential contribution of computer-aided detection to the sensitivity of screening mammography. , 2000, Radiology.

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

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

[4]  H P Chan,et al.  Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms. , 1999, Medical physics.

[5]  L. Tabár,et al.  The impact of organized mammography service screening on breast carcinoma mortality in seven Swedish counties , 2002, Cancer.

[6]  N Karssemeijer,et al.  Automated detection of breast carcinomas not detected in a screening program. , 1998, Radiology.

[7]  M L Giger,et al.  Computerized detection of clustered microcalcifications: evaluation of performance on mammograms from multiple centers. , 1995, Radiographics : a review publication of the Radiological Society of North America, Inc.

[8]  R E Hendrick,et al.  American College of Radiology guidelines for breast cancer screening. , 1998, AJR. American journal of roentgenology.

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

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

[11]  Helen C. Cowley,et al.  Improving the accuracy of mammography: volume and outcome relationships. , 2002, Journal of the National Cancer Institute.

[12]  C. Beam,et al.  Effect of human variability on independent double reading in screening mammography. , 1996, Academic radiology.

[13]  D. Ikeda,et al.  Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection. , 2001, Radiology.

[14]  Berkman Sahiner,et al.  Breast cancer detection: evaluation of a mass-detection algorithm for computer-aided diagnosis -- experience in 263 patients. , 2002, Radiology.

[15]  B. Cady,et al.  The life‐sparing potential of mammographic screening , 2001, Cancer.

[16]  C. Floyd,et al.  Differences between computer-aided diagnosis of breast masses and that of calcifications. , 2002, Radiology.

[17]  E. Thurfjell,et al.  Benefit of independent double reading in a population-based mammography screening program. , 1994, Radiology.

[18]  L. Tabár,et al.  Beyond randomized controlled trials , 2001, Cancer.

[19]  L. Fajardo,et al.  Previous mammograms in patients with impalpable breast carcinoma: retrospective vs blinded interpretation. 1993 ARRS President's Award. , 1993, AJR. American journal of roentgenology.

[20]  M J Schell,et al.  Association of recall rates with sensitivity and positive predictive values of screening mammography. , 2001, AJR. American journal of roentgenology.

[21]  C J Vyborny,et al.  Can computers help radiologists read mammograms? , 1994, Radiology.

[22]  N. Petrick,et al.  Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. , 1998, Medical physics.

[23]  K L Lam,et al.  Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network. , 1995, Medical physics.

[24]  G. P. Cohen,et al.  Characteristics of breast carcinomas missed by screening radiologists. , 1997, Radiology.

[25]  D. Wolverton,et al.  Performance parameters for screening and diagnostic mammography: specialist and general radiologists. , 2002, Radiology.