| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
Breast Imaging |
1 From the Women’s Diagnostic and Breast Health Center, 3800 W 15th St, Suite 111, Plano, TX 75075. From the 2000 RSNA scientific assembly. Received July 21, 2000; revision requested September 6; revision received March 9, 2001; accepted April 6. Address correspondence to T.W.F. (e-mail: tfreer@mindspring.com).
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODS RESULTSDISCUSSIONREFERENCES |
|---|
MATERIALS AND METHODS: Over a 12-month period, 12,860 screening mammograms were interpreted with the assistance of a CAD system. Each mammogram was initially interpreted without the assistance of CAD, followed immediately by a reevaluation of areas marked by the CAD system. Data were recorded to measure the effect of CAD on the recall rate, positive predictive value for biopsy, cancer detection rate, and stage of malignancies at detection.
RESULTS: When comparing the radiologist’s performance without CAD with that when CAD was used, the authors observed the following: (a) an increase in recall rate from 6.5% to 7.7%, (b) no change in the positive predictive value for biopsy at 38%, (c) a 19.5% increase in the number of cancers detected, and (d) an increase in the proportion of early-stage (0 and I) malignancies detected from 73% to 78%.
CONCLUSION: The use of CAD in the interpretation of screening mammograms can increase the detection of early-stage malignancies without undue effect on the recall rate or positive predictive value for biopsy.
Index terms: Breast neoplasms, 00.32 • Breast neoplasms, diagnosis, 00.32 • Cancer screening, 00.11, 00.1299 • Computers, diagnostic aid
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTSDISCUSSIONREFERENCES |
|---|
One documented means of reducing the false-negative rate in screening mammography is the double reading of mammograms (6,7). Several methods of double reading have been described, some designed solely for increasing the detection of malignancies and others also incorporating joint decision making in an effort to manage the recall rate. Investigators of these methods (8) reported increases in the cancer detection rate of as much as 15%, but both the practicality and cost-effectiveness of these methods are open to question, and double reading has yet to be widely adopted, particularly in the United States.
The incorporation of computer prompting to increase the sensitivity in screening mammography has gained increasing attention in recent years (5,9–13). Findings in a number of studies to investigate early computer-aided detection (CAD) systems clearly demonstrated the ability of CAD to detect and prompt mammographic signs of cancer and reported the potential of CAD to reduce the false-negative rate by 50%–70%. Warren Burhenne et al (5), with use of a more recent version of CAD, reported that CAD successfully marked the missed findings in 77% of false-negative prior mammograms. As a form of double reading, however, the use of CAD in the interpretive process also raises reasonable concerns that it might unduly increase the recall rate and, consequently, the number of biopsies (8). This would not be counterproductive, however, unless any improvement in detection was achieved at the sacrifice of specificity.
While findings in retrospective studies clearly demonstrated the potential of CAD to reduce the false-negative rate, the necessary prospective data to measure the actual effect of current CAD systems on the radiologist’s sensitivity for detecting clinically occult breast cancers on screening mammograms is lacking. Likewise, concerns for the effect of CAD on recall rate and the outcome of patients recalled require prospective evaluation to better determine the clinical usefulness of CAD in a screening mammography program.
After the U.S. Food and Drug Administration approved the first commercially available CAD system in June 1998 and on the basis of the demonstrated potential of these devices, our institution purchased a system, and an unpaired prospective study was undertaken to measure its effect on the recall rate, positive predictive value for biopsy, and cancer detection rate. The purpose of this study was to prospectively assess the effect of CAD on interpretation of screening mammograms in a community breast center.
|
MATERIALS AND METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTSDISCUSSIONREFERENCES |
|---|
Approximately 2,500 mammograms were interpreted with CAD assistance during this period for which no data were recorded. Actual data collection was begun at the interpretation of screening mammograms obtained on February 2, 1999, and terminated after the interpretation of screening mammograms obtained on February 1, 2000.
Study Group
In the 12-month study period, a total of 12,897 women presented for screening mammography. Acquisition and interpretation of the mammograms were performed in a customary manner and in accordance with the rules of the Mammography Quality Standards Act. Routine screening guidelines were used, but no age limitations were arbitrarily imposed. Patients with a history of breast carcinoma were accepted for screening, providing a disease-free interval of at least 3 years had elapsed. Of the 12,897 women who presented for mammography, eight women reported signs of breast cancer and were evaluated in the diagnostic clinic; 11 examinations were interpreted without CAD assistance during a brief period when the CAD system was not operational; and 18 examinations could not be successfully analyzed by the CAD system, most owing to insufficient tissue volume. The remaining 12,860 patients composed the study group; they had a median age of 49 years (range, 26–88 years). A total of 3,437 (27%) women underwent baseline examinations or were new to the center, and neither group had the immediate benefit of prior mammograms for comparison. The remaining 9,423 (73%) women were returning patients; they had a median screening interval of 13 months (range, 6–122 months).
Mammographic Examinations
A total of 12,634 (98%) bilateral and 226 (2%) unilateral mammograms were obtained with one of three mammography systems (600T; GE Medical Systems, Milwaukee, Wis), with screens (Min-R; Eastman Kodak, Rochester, NY) and film (CM-H; Konica Medical Imaging, Wayne, NJ). Each woman underwent a standard two-view examination of each breast performed by one of eight experienced and mammography-certified radiologic technologists. Where possible, implant displacement views were obtained in all patients with implants and submitted for CAD analysis. Those in whom adequate implant displacement views could not be obtained were among those who were excluded from the study.
CAD Analysis
Each screening mammogram was analyzed by the CAD system before interpretation. The CAD system consists of two freestanding units: the processing unit, which digitizes and analyzes the film images; and the display unit, a dedicated mammography autoviewer equipped with a pair of 5-inch- (13-cm-) monitors, which display low-spatial-resolution digital images, of the examination hung in the panels overhead. The digital images are electronically linked by means of a bar code to the panels where the actual film images are mounted and are displayed by pressing a button on the autoviewer control panel (Figure, part a). Each digital image may contain zero or more marks, indicating areas where the detection algorithm recognizes a pattern that warrants evaluation by the radiologist. Two types of marks are used: an asterisk, which indicates a pattern suggestive of a mass or area of architectural distortion; and a solid triangle, which indicates an area of clustered bright spots suggestive of microcalcifications (Figure, part b).
|
|
Data Collection
Data were collected only on actionable findings, which were defined as any potential abnormalities that resulted in a recall of the patient. When, and if, the prior mammograms of new patients were acquired, and comparison with the prior mammogram obviated recall evaluation, the potential abnormalities were deemed to be not actionable, no recall was initiated, and no data were collected.
For each actionable finding, the following data were collected:
In addition, the number and type of computer marks (mass or microcalcification) were recorded for the first 5,204 patients, to determine the marking rate of the CAD algorithm.
Statistical Analysis
All data were entered into a spreadsheet for subsequent tabulation and computation of rates and proportions. Since our investigation was an unpaired study (lacking a control group), standard 
2 analysis was not performed. Instead, on the basis of binomial distribution, 95% CIs were calculated to measure the statistical significance of the observations.
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTSDISCUSSIONREFERENCES |
|---|
|
|
|
|
|
|
The use of CAD increased the number of actionable findings by 19% (163 of 863) and the number of patients recalled by 19% (156 of 830), ultimately increasing the recall rate from 6.5% (830 of 12,860) to 7.7% (986 of 12,860) (Table 1). The number of actionable microcalcification clusters recalled increased by 53% (80 of 150), while the number of actionable masses recalled increased by 12% (83 of 713). This produced a shift in the proportion of microcalcifications (17% to 22%) and masses (83% to 78%) detected and deemed actionable.
Table 2 lists the effect of CAD on the final BI-RADS assessment of patients at recall evaluation. Of the 986 patients recalled, 981 (99.5%) returned for evaluation and five (0.5%) were lost to follow-up (final assessment remained BI-RADS category 0). The number of patients with BI-RADS categories 1 and 2 findings increased 16% (98 of 609), of BI-RADS category 3 increased 38% (41 of 108), and of BI-RADS categories 4 and 5 increased 15% (16 of 109). As shown in the RA + CAD column, only a modest shift in the proportions for each BI-RADS category was observed.
Standard recommendations were made on the basis of final BI-RADS assessment. Of the 149 patients with BI-RADS category 3 findings, 146 (98%) agreed to imaging surveillance and three (2%) elected to undergo biopsy. Of the 110 patients with BI-RADS category 4 findings, 106 (96%) underwent biopsy, two (2%) refused biopsy but agreed to imaging surveillance, and two (2%) were lost to follow-up. All 15 patients (100%) with BI-RADS category 5 findings agreed to undergo biopsy. Ultimately, 128 actionable findings in 124 patients were subjected to biopsy.
The effect of CAD on the histopathologic diagnosis at biopsy is listed in Table 4. Among benign lesions, the largest shifts in proportion were seen in the subcategories of fibroadenoma, which decreased from 45% (30 of 66) to 39% (31 of 79), and fibrocystic disease, which increased from 39% (26 of 66) to 43% (34 of 79). Among malignant lesions, the largest shifts occurred in the subcategories of ductal carcinoma in situ, which increased from 30% (12 of 41) to 37% (18 of 49), and invasive ductal carcinoma, which decreased from 56% (23 of 41) to 51% (25 of 49).
Regardless of substantial shifts in proportion for the subcategories of both benign and malignant lesions, the overall proportion of benign lesions (62%) and malignant lesions (38%) were identical for both the radiologist and CAD; thus, no effect on the overall positive predictive value for biopsy of 38% was observed (Table 4).
The use of CAD resulted in a 19.5% (eight of 41) increase in the number of malignancies detected (Table 4). Without CAD, 41 malignancies were detected, which yielded a detection rate of 3.2 cancers for 1,000 women screened (41 of 12.86). With CAD, the detection rate increased to 3.8 cancers for 1,000 women screened (49 of 12.86).
The effect of CAD on the stage of malignancies at detection is listed in Table 5. All eight malignant lesions initially detected by CAD were stage 0 or I, increasing detection of stage 0 lesions by 42% (five of 12) and stage I lesions by 17% (three of 18). This resulted in improvement in the overall proportion of early-stage lesions detected from 73% (30 of 41) to 78% (38 of 49).
Table 6 reports the performance of the radiologist alone and CAD alone in the detection of malignancies. The radiologist alone detected 96% (26 of 27) of the malignant masses, 68% (15 of 22) of the malignant clustered microcalcifications, and 84% (41 of 49) of the total malignancies detected. Of the eight malignant lesions missed by the radiologist, seven presented as clustered microcalcifications, and the remaining lesion presented as a mass. Marks were placed by CAD alone on 67% (18 of 27) of the malignant masses, 100% (22 of 22) of the malignant clustered microcalcifications, and 82% (40 of 49) of the total malignancies detected. All nine of the malignant lesions not marked by CAD presented as masses. Of the 49 malignancies detected, 65% (32 of 49) were initially detected by the radiologist and also marked by CAD.
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTSDISCUSSIONREFERENCES |
|---|
The ability of the CAD system used in our study to detect and mark clustered microcalcifications produced the most profound effect on our performance. Microcalcifications, by virtue of their opacity, are relatively conspicuous features on many mammograms. Because of that relative conspicuity, many radiologists may be more confident in their ability to detect microcalcifications than they are in their ability to detect subtle masses on a mammogram. While this supposition may be true, some microcalcifications are quite inconspicuous owing to their small size and/or obscuration by overlying fibroglandular tissues and may be easily overlooked by a diligent radiologist. In studies analyzing the mammographic nature of missed cancers, clustered microcalcifications composed 19%–31% of lesions missed at screening (3–5). In the study by Bird et al (3), 18% (six of 33) of missed malignancies presented as clustered microcalcifications that were simply overlooked by the radiologist.
While microcalcifications accounted for only 22% (230 of 1,026) of the actionable findings, 49% (80 of 163) of the actionable findings initially detected by CAD were clustered microcalcifications and, thus, accounted for half of the increase in the recall rate. The proper use of CAD will inevitably result in an increase in the recall rate. To do otherwise would require that the radiologist be dissuaded from recalling a patient because CAD failed to mark an actionable finding initially detected by the radiologist. The use of CAD to "analyze" or "estimate" the importance of an actionable findings should never occur, as the sensitivity of current detection algorithms do not justify such use. Ultimately, the increase in the recall rate from 6.5% to 7.7% was, in the authors’ opinion, quite acceptable given the proportional improvement in cancer detection.
The improved detection of microcalcifications produced a modest increase in the number and proportion of patients with BI-RADS category 3 findings and subjected to close-interval mammographic surveillance. It also produced modest shifts in the subcategories of both benign and malignant lesions diagnosed at biopsy, which increased the proportion for those that typically present as microcalcifications, such as fibrocystic disease and ductal carcinoma in situ, and decreased the proportion for those that typically present as masses, such as fibroadenoma and invasive carcinoma.
However, the overall proportion of benign and malignant lesions sampled at biopsy and, thus, positive predictive value for biopsy were unaffected by the use of CAD in the screening interpretation. This might have been predicted, as the decision to sample a lesion at biopsy was (and should always be) based on the merits of the clinical and imaging findings at recall evaluation and not influenced in any manner by whether a mark was placed on the lesion by CAD. Nor does it appear that the additional actionable findings initiated by CAD were of a nature that would, in themselves, lead to a disproportional number of recommendations for biopsy.
The most encouraging effects of CAD were a 19.5% increase in the cancer detection rate (41 to 49 cancers detected, 3.2 to 3.8 of 1,000 women screened) and an improvement in the proportion of malignancies detected at an early stage (73% to 78%). All eight additional malignancies initially detected by CAD were found to be stage 0 or I at surgery. Seven of the eight additional cancers presented as clustered microcalcifications, five of which were ductal carcinoma in situ (two of low histologic grade and three of moderate to high histologic grade). The remaining two cases of clustered microcalcifications and the single mass initially detected by CAD were invasive carcinomas.
The radiologist and CAD system were statistically equal in their ability to detect mammographic signs of malignancy: The radiologist detected 41 of the 49 lesions (missed eight lesions) and one or more marks were placed on 40 of the 49 lesions (missed nine lesions) by CAD. While such comparison data are of natural interest to radiologists, the authors point out that current CAD systems are not designed to act independently. Ultimately, it is not the independent performance of the radiologist or CAD on which one should focus but rather the performance of the radiologist when CAD is used.
While this study evaluated a substantial dataset (12,860 women), the low incidence of occult carcinoma in a relatively young screening population (median age, 49 years) imposes severe limitations on the statistical significance of many key observations and indicates the need for additional studies of this technology. However, given that Warren Burhenne et al (5) calculated the average false-negative rate of screening mammography to be 21%, one might reasonably anticipate that a 19% increase in the cancer detection rate might eliminate many or most of the false-negative findings for the study period. To evaluate this possibility, we continue to follow the study group to identify the false-negative interpretations during the study period and to more accurately measure the effect of CAD on the sensitivity of screening mammography.
In summary, the use of CAD in the interpretation of screening mammograms resulted in a 19.5% increase in detection of early-stage cancers without undue effect on the recall rate or positive predictive value for biopsy. On the basis of these observations, the authors conclude that CAD may prove a valuable adjunct to screening mammography and the early detection of breast cancer. Additional clinical trials of this technology are needed.
|
ACKNOWLEDGMENTS |
|---|
|
FOOTNOTES |
|---|
Author contributions: Guarantor of integrity of entire study, T.W.F.; study concepts and design, T.W.F.; literature research, T.W.F., M.J.U.; clinical studies, T.W.F., M.J.U.; data acquisition, T.W.F., M.J.U.; data analysis/interpretation, T.W.F.; statistical analysis, T.W.F.; manuscript preparation and definition of intellectual content, T.W.F.; manuscript editing, revision/review, and final version approval, T.W.F., M.J.U.
|
REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTSDISCUSSIONREFERENCES |
|---|
This article has been cited by other articles:
|
|
 |
|
  R. M. Nishikawa and L. L. Pesce Computer-aided Detection Evaluation Methods Are Not Created Equal Radiology, June 1, 2009; 251(3): 634 - 636. [Full Text] [PDF]
|
|
|
|
|
|
E. S. Burnside, J. Davis, J. Chhatwal, O. Alagoz, M. J. Lindstrom, B. M. Geller, B. Littenberg, K. A. Shaffer, C. E. Kahn Jr, and C. D. Page Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings Radiology, June 1, 2009; 251(3): 663 - 672. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Chhatwal, O. Alagoz, M. J. Lindstrom, C. E. Kahn Jr., K. A. Shaffer, and E. S. Burnside A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer Diagnosis Am. J. Roentgenol., April 1, 2009; 192(4): 1117 - 1127. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. S. The, K. J. Schilling, J. W. Hoffmeister, E. Friedmann, R. McGinnis, and R. G. Holcomb Detection of Breast Cancer with Full-Field Digital Mammography and Computer-Aided Detection Am. J. Roentgenol., February 1, 2009; 192(2): 337 - 340. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Sadik, I. Hamadeh, P. Nordblom, M. Suurkula, P. Hoglund, M. Ohlsson, and L. Edenbrandt Computer-Assisted Interpretation of Planar Whole-Body Bone Scans J. Nucl. Med., December 1, 2008; 49(12): 1958 - 1965. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F. J. Gilbert, S. M. Astley, M. G.C. Gillan, O. F. Agbaje, M. G. Wallis, J. James, C. R.M. Boggis, S. W. Duffy, and the CADET II Group Single Reading with Computer-Aided Detection for Screening Mammography N. Engl. J. Med., October 16, 2008; 359(16): 1675 - 1684. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. A. Harvey, B. T. Nicholson, and M. A. Cohen Finding Early Invasive Breast Cancers: A Practical Approach Radiology, July 1, 2008; 248(1): 61 - 76. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. MINIGH Quality Assurance in Digital Mammography Radiol. Technol., May 1, 2008; 79(5): 433M - 454M. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Gur Imaging Technology and Practice Assessment Studies: Importance of the Baseline or Reference Performance Level Radiology, April 1, 2008; 247(1): 8 - 11. [Full Text] [PDF]
|
|
|
|
|
|
M. Gromet Comparison of Computer-Aided Detection to Double Reading of Screening Mammograms: Review of 231,221 Mammograms Am. J. Roentgenol., April 1, 2008; 190(4): 854 - 859. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. A. Taylor, R. Greenhalgh, R. Ilangovan, E. Tam, V. A. Sahni, D. Burling, J. Zhang, P. Bassett, P. J. Pickhardt, and S. Halligan CT Colonography and Computer-aided Detection: Effect of False-Positive Results on Reader Specificity and Reading Efficiency in a Low-Prevalence Screening Population Radiology, April 1, 2008; 247(1): 133 - 140. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Kakeda, Y. Korogi, H. Arimura, T. Hirai, S. Katsuragawa, T. Aoki, and K. Doi Diagnostic Accuracy and Reading Time to Detect Intracranial Aneurysms on MR Angiography Using a Computer-Aided Diagnosis System Am. J. Roentgenol., February 1, 2008; 190(2): 459 - 465. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. J. Kim, W. K. Moon, N. Cho, J. H. Cha, S. M. Kim, and J.-G. Im Computer-aided Detection in Full-Field Digital Mammography: Sensitivity and Reproducibility in Serial Examinations Radiology, December 1, 2007; 246(1): 71 - 80. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Georgian-Smith, R. H. Moore, E. Halpern, E. D. Yeh, E. A. Rafferty, H. A. D'Alessandro, M. Staffa, D. A. Hall, K. A. McCarthy, and D. B. Kopans Blinded Comparison of Computer-Aided Detection with Human Second Reading in Screening Mammography Am. J. Roentgenol., November 1, 2007; 189(5): 1135 - 1141. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. F. Brem Blinded Comparison of Computer-Aided Detection with Human Second Reading in Screening Mammography: The Importance of the Question and the Critical Numbers Game Am. J. Roentgenol., November 1, 2007; 189(5): 1142 - 1144. [Full Text] [PDF]
|
|
|
|
|
|
R. L. Ellis, A. A. Meade, M. A. Mathiason, K. M. Willison, and W. Logan-Young Evaluation of Computer-aided Detection Systems in the Detection of Small Invasive Breast Carcinoma Radiology, October 1, 2007; 245(1): 88 - 94. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. P. Krestin, J. C. Miller, S. J. Golding, G. G. Frija, G. M. Glazer, H. G. Ringertz, and J. H. Thrall Reinventing Radiology in a Digital and Molecular Age: Summary of Proceedings of the Sixth Biannual Symposium of the International Society for Strategic Studies in Radiology (IS3R), August 25 27, 2005 Radiology, September 1, 2007; 244(3): 633 - 638. [Full Text] [PDF]
|
|
|
|
|
|
S. Ciatto, N. Houssami, D. Gur, R. M. Nishikawa, R. A. Schmidt, C. E. Metz, J. F. Ruiz, S. A. Feig, R. L. Birdwell, M. N. Linver, et al. Computer-aided screening mammography. N. Engl. J. Med., July 5, 2007; 357(1): 83 - 84. [Full Text] [PDF]
|
|
|
|
|
|
S. K. Yang, W. K. Moon, N. Cho, J. S. Park, J. H. Cha, S. M. Kim, S. J. Kim, and J.-G. Im Screening Mammography-detected Cancers: Sensitivity of a Computer-aided Detection System Applied to Full-Field Digital Mammograms Radiology, July 1, 2007; 244(1): 104 - 111. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Jiang, D. L. Miglioretti, C. E. Metz, and R. A. Schmidt Breast Cancer Detection Rate: Designing Imaging Trials to Demonstrate Improvements Radiology, May 1, 2007; 243(2): 360 - 367. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. J. Fenton, S. H. Taplin, P. A. Carney, L. Abraham, E. A. Sickles, C. D'Orsi, E. A. Berns, G. Cutter, R. E. Hendrick, W. E. Barlow, et al. Influence of Computer-Aided Detection on Performance of Screening Mammography N. Engl. J. Med., April 5, 2007; 356(14): 1399 - 1409. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Skaane, A. Kshirsagar, S. Stapleton, K. Young, and R. A. Castellino Effect of Computer-Aided Detection on Independent Double Reading of Paired Screen-Film and Full-Field Digital Screening Mammograms Am. J. Roentgenol., February 1, 2007; 188(2): 377 - 384. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. F. Brem Clinical Versus Research Approach to Breast Cancer Detection with CAD: Where Are We Now? Am. J. Roentgenol., January 1, 2007; 188(1): 234 - 235. [Full Text] [PDF]
|
|
|
|
|
|
S. J. Kim, W. K. Moon, N. Cho, J. H. Cha, S. M. Kim, and J.-G. Im Computer-aided Detection in Digital Mammography: Comparison of Craniocaudal, Mediolateral Oblique, and Mediolateral Views Radiology, December 1, 2006; 241(3): 695 - 701. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. H. Taplin, C. M. Rutter, and C. D. Lehman Testing the effect of computer-assisted detection on interpretive performance in screening mammography. Am. J. Roentgenol., December 1, 2006; 187(6): 1475 - 1482. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. M. Ko, M. J. Nicholas, J. B. Mendel, and P. J. Slanetz Prospective assessment of computer-aided detection in interpretation of screening mammography. Am. J. Roentgenol., December 1, 2006; 187(6): 1483 - 1491. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. R. Pai, N. E. Gregory, A. E. Swinford, and M. Rebner Ductal Carcinoma in Situ: Computer-aided Detection in Screening Mammography Radiology, December 1, 2006; 241(3): 689 - 694. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 N Karssemeijer, J D M Otten, H Rijken, and R Holland Computer aided detection of masses in mammograms as decision support Br. J. Radiol., December 1, 2006; 79(Special_Issue_2): S123 - S126. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. D. O'Connor, R. M. Summers, J. Yao, P. J. Pickhardt, and J. R. Choi CT Colonography with Computer-aided Polyp Detection: Volume and Attenuation Thresholds to Reduce False-Positive Findings Owing to the Ileocecal Valve Radiology, November 1, 2006; 241(2): 426 - 432. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. J. Gilbert, S. M. Astley, M. A. McGee, M. G. C. Gillan, C. R. M. Boggis, P. M. Griffiths, and S. W. Duffy Single Reading with Computer-aided Detection and Double Reading of Screening Mammograms in the United Kingdom National Breast Screening Program Radiology, October 1, 2006; 241(1): 47 - 53. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. S. Burnside, D. L. Rubin, J. P. Fine, R. D. Shachter, G. A. Sisney, and W. K. Leung Bayesian Network to Predict Breast Cancer Risk of Mammographic Microcalcifications and Reduce Number of Benign Biopsy Results: Initial Experience Radiology, September 1, 2006; 240(3): 666 - 673. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. C. Dean and C. C. Ilvento Improved cancer detection using computer-aided detection with diagnostic and screening mammography: prospective study of 104 cancers. Am. J. Roentgenol., July 1, 2006; 187(1): 20 - 28. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. H. Sumkin, D. Gur, R. L. Birdwell, and D. M. Ikeda Computer-aided Detection with Screening Mammography: Improving Performance or Simply Shifting the Operating Point? Radiology, June 1, 2006; 239(3): 916 - 918. [Full Text] [PDF]
|
|
|
|
|
|
S. A. Taylor, S. Halligan, A. Slater, V. Goh, D. N. Burling, M. E. Roddie, L. Honeyfield, J. McQuillan, H. Amin, and J. Dehmeshki Polyp Detection with CT Colonography: Primary 3D Endoluminal Analysis versus Primary 2D Transverse Analysis with Computer-assisted Reader Software Radiology, June 1, 2006; 239(3): 759 - 767. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. K. Lindfors, M. C. McGahan, C. J. Rosenquist, and G. S. Hurlock Computer-aided Detection of Breast Cancer: A Cost-effectiveness Study Radiology, June 1, 2006; 239(3): 710 - 717. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. J. Morton, D. H. Whaley, K. R. Brandt, and K. K. Amrami Screening Mammograms: Interpretation with Computer-aided Detection--Prospective Evaluation Radiology, May 1, 2006; 239(2): 375 - 383. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. S. Soo, S. Ghate, J. A. Baker, and E. Rosen Reply Am. J. Roentgenol., March 1, 2006; 186(3): 902 - 903. [Full Text] [PDF]
|
|
|
|
|
|
R. J. Brenner, M. J. Ulissey, and R. M. Wilt Computer-Aided Detection as Evidence in the Courtroom: Potential Implications of an Appellate Court's Ruling Am. J. Roentgenol., January 1, 2006; 186(1): 48 - 51. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. S. Greenberg An Appellate Court Ruling and Potential Implications for CAD Technology in the Courtroom Am. J. Roentgenol., January 1, 2006; 186(1): 52 - 53. [Full Text] [PDF]
|
|
|
|
|
|
H.-P. Chan, J. Wei, B. Sahiner, E. A. Rafferty, T. Wu, M. A. Roubidoux, R. H. Moore, D. B. Kopans, L. M. Hadjiiski, and M. A. Helvie Computer-aided Detection System for Breast Masses on Digital Tomosynthesis Mammograms: Preliminary Experience Radiology, December 1, 2005; 237(3): 1075 - 1080. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. A. L. Khoo, P. Taylor, and R. M. Given-Wilson Computer-aided Detection in the United Kingdom National Breast Screening Programme: Prospective Study Radiology, November 1, 2005; 237(2): 444 - 449. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Hirai, Y. Korogi, H. Arimura, S. Katsuragawa, M. Kitajima, M. Yamura, Y. Yamashita, and K. Doi Intracranial Aneurysms at MR Angiography: Effect of Computer-aided Diagnosis on Radiologists' Detection Performance Radiology, November 1, 2005; 237(2): 605 - 610. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. E. Cupples, J. E. Cunningham, and J. C. Reynolds Impact of Computer-Aided Detection in a Regional Screening Mammography Program Am. J. Roentgenol., October 1, 2005; 185(4): 944 - 950. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 K. Peldschus, P. Herzog, S. A. Wood, J. I. Cheema, P. Costello, and U. J. Schoepf Computer-Aided Diagnosis as a Second Reader: Spectrum of Findings in CT Studies of the Chest Interpreted as Normal Chest, September 1, 2005; 128(3): 1517 - 1523. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. S. Burnside, J. M. Park, J. P. Fine, and G. A. Sisney The Use of Batch Reading to Improve the Performance of Screening Mammography Am. J. Roentgenol., September 1, 2005; 185(3): 790 - 796. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. L. Partain, H.-P. Chan, J. G. Gelovani, M. L. Giger, J. A. Izatt, F. A. Jolesz, K. Kandarpa, K. C. P. Li, M. McNitt-Gray, S. Napel, et al. Biomedical Imaging Research Opportunities Workshop II: Report and Recommendations Radiology, August 1, 2005; 236(2): 389 - 403. [Full Text] [PDF]
|
|
|
|
|
|
R. L. Birdwell, P. Bandodkar, and D. M. Ikeda Computer-aided Detection with Screening Mammography in a University Hospital Setting Radiology, August 1, 2005; 236(2): 451 - 457. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. A. Baker, E. L. Rosen, M. M. Crockett, and J. Y. Lo Accuracy of Segmentation of a Commercial Computer-aided Detection System for Mammography Radiology, May 1, 2005; 235(2): 385 - 390. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. V. Ghate, M. S. Soo, J. A. Baker, R. Walsh, E. I. Gimenez, and E. L. Rosen Comparison of Recall and Cancer Detection Rates for Immediate versus Batch Interpretation of Screening Mammograms Radiology, April 1, 2005; 235(1): 31 - 35. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. G. Elmore, K. Armstrong, C. D. Lehman, and S. W. Fletcher Screening for Breast Cancer JAMA, March 9, 2005; 293(10): 1245 - 1256. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. S. Soo, E. L. Rosen, J. Q. Xia, S. Ghate, and J. A. Baker Computer-Aided Detection of Amorphous Calcifications Am. J. Roentgenol., March 1, 2005; 184(3): 887 - 892. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. F. Brem, J. W. Hoffmeister, G. Zisman, M. P. DeSimio, and S. K. Rogers A Computer-Aided Detection System for the Evaluation of Breast Cancer by Mammographic Appearance and Lesion Size Am. J. Roentgenol., March 1, 2005; 184(3): 893 - 896. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. F. Brem, J. W. Hoffmeister, J. A. Rapelyea, G. Zisman, K. Mohtashemi, G. Jindal, M. P. DiSimio, and S. K. Rogers Impact of Breast Density on Computer-Aided Detection for Breast Cancer Am. J. Roentgenol., February 1, 2005; 184(2): 439 - 444. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K Doi Current status and future potential of computer-aided diagnosis in medical imaging Br. J. Radiol., January 1, 2005; 78(suppl_1): S3 - s19. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S M Astley Evaluation of computer-aided detection (CAD) prompting techniques for mammography Br. J. Radiol., January 1, 2005; 78(suppl_1): S20 - S25. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P Taylor and R M Given-Wilson Evaluation of computer-aided detection (CAD) devices Br. J. Radiol., January 1, 2005; 78(suppl_1): S26 - S30. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J Roehrig The manufacturer's perspective Br. J. Radiol., January 1, 2005; 78(suppl_1): S41 - S45. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S M Astley Computer-based detection and prompting of mammographic abnormalities Br. J. Radiol., December 1, 2004; 77(suppl_2): S194 - S200. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. A. Butler, R. J. Gabbay, D. A. Kass, D. E. Siedler, K. F. O'Shaughnessy, and R. A. Castellino Computer-Aided Detection in Diagnostic Mammography: Detection of Clinically Unsuspected Cancers Am. J. Roentgenol., November 1, 2004; 183(5): 1511 - 1515. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Gur, J. S. Stalder, L. A. Hardesty, B. Zheng, J. H. Sumkin, D. M. Chough, B. E. Shindel, and H. E. Rockette Computer-aided Detection Performance in Mammographic Examination of Masses: Assessment Radiology, November 1, 2004; 233(2): 418 - 423. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. A. Baker, J. Y. Lo, D. M. Delong, and C. E. Floyd Computer-aided Detection in Screening Mammography: Variability in Cues Radiology, November 1, 2004; 233(2): 411 - 417. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. S. M. Buist, P. L. Porter, C. Lehman, S. H. Taplin, and E. White Factors Contributing to Mammography Failure in Women Aged 40-49 Years J Natl Cancer Inst, October 6, 2004; 96(19): 1432 - 1440. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. A. Feig, E. A. Sickles, W. P. Evans, and M. N. Linver Re: Changes in Breast Cancer Detection and Mammography Recall Rates After the Introduction of a Computer-Aided Detection System J Natl Cancer Inst, August 18, 2004; 96(16): 1260 - 1261. [Full Text] [PDF]
|
|
|
|
|
|
S. V. Destounis, P. DiNitto, W. Logan-Young, E. Bonaccio, M. L. Zuley, and K. M. Willison Can Computer-aided Detection with Double Reading of Screening Mammograms Help Decrease the False-Negative Rate? Initial Experience Radiology, August 1, 2004; 232(2): 578 - 584. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. M. Hall and R. F. Brem Improved Sensitivity of Mammography with Computer-Aided Detection Am. J. Roentgenol., June 1, 2004; 182(6): 1598 - 1599. [Full Text] [PDF]
|
|
|
|
|
|
M. A. Guenin and R. F. Brem How Not to Assess Computer-Aided Detection for Mammography Am. J. Roentgenol., June 1, 2004; 182(6): 1599 - 1600. [Full Text] [PDF]
|
|
|
|
|
|
M. A. Helvie, L. Hadjiiski, E. Makariou, H.-P. Chan, N. Petrick, B. Sahiner, S.-C. B. Lo, M. Freedman, D. Adler, J. Bailey, et al. Sensitivity of Noncommercial Computer-aided Detection System for Mammographic Breast Cancer Detection: Pilot Clinical Trial Radiology, April 1, 2004; 231(1): 208 - 214. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. Zheng, J. K. Leader, G. Abrams, B. Shindel, V. Catullo, W. F. Good, and D. Gur Computer-Aided Detection Schemes: The Effect of Limiting the Number of Cued Regions in Each Case Am. J. Roentgenol., March 1, 2004; 182(3): 579 - 583. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. M. Ikeda, R. L. Birdwell, K. F. O'Shaughnessy, E. A. Sickles, and R. J. Brenner Computer-aided Detection Output on 172 Subtle Findings on Normal Mammograms Previously Obtained in Women with Breast Cancer Detected at Follow-Up Screening Mammography Radiology, March 1, 2004; 230(3): 811 - 819. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Gur, J. H. Sumkin, H. E. Rockette, M. Ganott, C. Hakim, L. Hardesty, W. R. Poller, R. Shah, and L. Wallace Changes in Breast Cancer Detection and Mammography Recall Rates After the Introduction of a Computer-Aided Detection System J Natl Cancer Inst, February 4, 2004; 96(3): 185 - 190. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Guenin and B. Zheng Long-term Retention of Mammographic Computer-assisted Diagnosis Information Is Neither Necessary Nor Desirable [letter] * Dr Zheng responds: Radiology, February 1, 2004; 230(2): 595 - 597. [Full Text] [PDF]
|
|
|
|
|
|
P M Taylor, J Champness, R M Given-Wilson, H W W Potts, and K Johnston An evaluation of the impact of computer-based prompts on screen readers' interpretation of mammograms Br. J. Radiol., January 1, 2004; 77(913): 21 - 27. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. A. Baker, E. L. Rosen, J. Y. Lo, E. I. Gimenez, R. Walsh, and M. S. Soo Computer-Aided Detection (CAD) in Screening Mammography: Sensitivity of Commercial CAD Systems for Detecting Architectural Distortion Am. J. Roentgenol., October 1, 2003; 181(4): 1083 - 1088. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. G. Elmore, C. Y. Nakano, T. D. Koepsell, L. M. Desnick, C. J. D'Orsi, and D. F. Ransohoff International Variation in Screening Mammography Interpretations in Community-Based Programs J Natl Cancer Inst, September 17, 2003; 95(18): 1384 - 1393. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. M. Aisen, L. S. Broderick, H. Winer-Muram, C. E. Brodley, A. C. Kak, C. Pavlopoulou, J. Dy, C.-R. Shyu, and A. Marchiori Automated Storage and Retrieval of Thin-Section CT Images to Assist Diagnosis: System Description and Preliminary Assessment Radiology, July 1, 2003; 228(1): 265 - 270. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. Zheng, L. A. Hardesty, W. R. Poller, J. H. Sumkin, and S. Golla Mammography with Computer-aided Detection: Reproducibility Assessment—Initial Experience Radiology, July 1, 2003; 228(1): 58 - 62. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. A. Smith, D. Saslow, K. Andrews Sawyer, W. Burke, M. E. Costanza, W. P. Evans III, R. S. Foster Jr., E. Hendrick, H. J. Eyre, and S. Sener American Cancer Society Guidelines for Breast Cancer Screening: Update 2003 CA Cancer J Clin, May 1, 2003; 53(3): 141 - 169. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. C. Harvey, B. Geller, R. G. Oppenheimer, M. Pinet, L. Riddell, and B. Garra Increase in Cancer Detection and Recall Rates with Independent Double Interpretation of Screening Mammography Am. J. Roentgenol., May 1, 2003; 180(5): 1461 - 1467. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
N. Karssemeijer, J. D. M. Otten, A. L. M. Verbeek, J. H. Groenewoud, H. J. de Koning, J. H. C. L. Hendriks, and R. Holland Computer-aided Detection versus Independent Double Reading of Masses on Mammograms Radiology, April 1, 2003; 227(1): 192 - 200. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. H. Sumkin, B. L. Holbert, J. S. Herrmann, C. A. Hakim, M. A. Ganott, W. R. Poller, R. Shah, L. A. Hardesty, and D. Gur Optimal Reference Mammography: A Comparison of Mammograms Obtained 1 and 2 Years Before the Present Examination Am. J. Roentgenol., February 1, 2003; 180(2): 343 - 346. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
W. M. Sacks, L. J. Warren Burhenne, S. A. Wood, C. J. D'Orsi, S. A. Feig, K. F. O'Shaughnessy, E. A. Sickles, C. J. Vyborny, and R. A. Castellino Estimating the Effect of Computer-aided Detection on the Sensitivity of Screening Mammography * Dr Warren Burhenne and colleagues respond: Radiology, February 1, 2003; 226(2): 597 - 599. [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| RADIOLOGY | RADIOGRAPHICS | RSNA JOURNALS ONLINE |
) marks.