Clinical performance of computer-assisted detection (CAD system in detecting carcinoma in breasts of different densities

OBJECTIVES: To determine the clinical performance of a computer-assisted detection (CAD) system in detecting carcinoma in breasts of different densities. MATERIALS AND METHODS: A total of 264 sets of bilateral screening mammograms taken in craniocaudal and medial-lateral oblique projections during the year 1997 were divided into four groups according to the BI-RADS density classification: fatty (pattern 1), scattered fibroglandular (pattern 2), heterogeneously dense (pattern 3) and extremely dense (pattern 4). Each group contained about 60% normal and 40% biopsy-proven cancer cases. Of the malignant cases, there were a mixture of mammographic findings including focal masses (<2.5 cm), asymmetrical density, architectural distortion or microcalcifications. Films with artefacts and obvious masses>2.5 cm were not included. The chosen cases were then digitized and analysed by the CAD system. Sensitivity was calculated as detection of cancer by at least one marker in at least one view. Specificity was calculated as the number of false-positive marks per image on normal cases. Statistical tests of significance were performed by using contingency tables and Chi square test. RESULTS: The CAD system detected 14 out of the total 15 cancer cases in totally fatty breasts with a sensitivity of 93.3% at a specificity of 1.3 false-positive marks per image. In breasts with scattered fibroglandular pattern, the sensitivity was 93.9% (31/33) and the specificity was 1.6 false-positive marks per image while in heterogeneously dense breasts, the sensitivity of the CAD system fell to 84.8% at a specificity of 1.6 false-positive marks per image. The sensitivity of the CAD system further dropped to 64.3% in markedly dense breasts while maintaining a specificity of 1.2 false-positive marks per image. The decrease in sensitivity in dense breast was found to be significant (p=0.046). CONCLUSION: The sensitivity of the CAD system deteriorated significantly as the density of the breast increased while the specificity of the system remained relatively constant.     

数字乳腺摄影计算机辅助诊断对乳腺癌的检出价值

目的：探讨全数字化乳腺摄影（DM）计算机辅助诊断（CAD）在不同乳腺结构内检出乳腺癌的价值。方法：185例经病理证实的单乳单灶性乳腺癌及179例正常乳腺均行DM检查,根据BI-RADS将所有乳腺分为非致密组和致密组。将所有乳腺摄影图像采用CAD法进行诊断,计算CAD的诊断敏感性,记录CAD在病例组及正常组的平均每例假阳性标记数,并进行不同结构乳腺组间及病例组与正常组的比较。结果：CAD检出乳腺癌的敏感度为88．6％;在非致密组与致密组中检出乳腺癌的敏感度分别为97．4％和82．4％,两组比较,差异具有统计学意义（P〈0．05）。在病例组和正常组中cAD的假阳性标记数的中位数（最小值,最大值）分别为1（0,12）个和2（0,8）个,两组间的差异有统计学意义（P〈0．05）。结论：cAD检出乳腺癌的敏感性较高,乳腺密度可能影响DM对单纯肿块型乳腺癌的检出。     

Screening mammography-detected cancers: sensitivity of a computer-aided detection system applied to full-field digital mammograms

PURPOSE: To retrospectively evaluate the sensitivity of the performance of a computer-aided detection (CAD) system applied to full-field digital mammograms for detection of breast cancers in a screening group, with histologic findings as the reference standard. MATERIALS AND METHODS: This study had institutional review board approval, and patient informed consent was waived. A commercially available CAD system was applied to the digital mammograms of 103 women (mean age, 51 years; range, 35-69 years) with 103 breast cancers detected with screening. Sensitivity values of the CAD system according to mammographic appearance, breast composition, and histologic findings were analyzed. Normal mammograms from 100 women (mean age, 54 years; age range, 35-75 years) with no mammographic and clinical abnormality during 2-year follow-up were used to determine false-positive CAD system marks. Differences between the cancer detection rates in fatty and dense breasts for the CAD system were compared by using the chi(2) test. RESULTS: The CAD system correctly marked 99 (96.1%) of 103 breast cancers. The CAD system marked all 44 breast cancers that manifested as microcalcifications only, all 23 breast cancers that manifested as a mass with microcalcifications, and 32 (89%) of 36 lesions that appeared as a mass only. The sensitivity of the CAD system in the fatty breast group was 95% (59 of 62) and in the dense breast group was 98% (40 of 41) (P = .537). The CAD system correctly marked all 31 lesions of ductal carcinoma in situ (DCIS), all 22 lesions of invasive ductal carcinoma with DCIS, the single invasive lobular carcinoma lesion, and 45 (92%) of 49 lesions of invasive ductal carcinoma. On normal mammograms, the mean number of false-positive marks per patient was 1.80 (range, 0-10 marks; median, 1 mark). CONCLUSION: The CAD system can correctly mark most (96.1%) asymptomatic breast cancers detected with digital mammographic screening, with acceptable false-positive marks (1.80 per patient).     

Computer-assisted detection as a second reader in symptomatic Asian women with palpable breast cancer

Purpose: To determine the potential role of a computer-assisted detection (CAD) algorithm as a second reader for experienced and inexperienced radiologists in mammography reading in Asian women.

Material and Methods: Two-view mammograms performed in 124 consecutive patients who presented with palpable breast cancer masses were retrospectively evaluated by two experienced breast radiologists (7 and 10 years' experience). The original reports of the session radiologists with variable experience of reading mammograms (2 to more than 10 years) were also evaluated. The number of suspicious masses and microcalcification clusters detected in each patient by both groups of radiologists were recorded. The radiologists then re-evaluated the films with the CAD system as a second reader. Any improvement in the detectability of breast pathology by either the experienced radiologists and/or by the session radiologists was then assessed. A total of 127 breasts had biopsy-proven carcinoma; 74 breasts had mastectomy performed. All the imaging results were correlated with tru-cut biopsy or mastectomy histology.

Results: With CAD-aided interpretation, there were altogether 95 visible masses and 77 suspicious microcalcification clusters in 109 mammographically detectable cancers correlated with histology results. There was a 7.4% (7/95) and 10.4% (8/77) increase in the number of masses and microcalcification clusters detected, respectively, by the experienced radiologists after application of CAD, whereas the increase was 13.7% (13/95) and 27.3% (21/77) for detection of masses and microcalcifications by the session radiologists, respectively. In 9 patients, a secondary focus detected by CAD was confirmed by histology. Three patients had contralateral breast tumors, 1 had a satellite invasive tumor while 5 had ductal carcinoma in situ on the same breast. Based on the biopsies and 74 mastectomies, the true-positive and false-positive detection rate of CAD was 92.6% and 31.8% for detection of carcinomas. The true-positive and false-positive detection rates were 100% and 58.8% for microcalcification clusters.

Conclusion: The current generation CAD algorithm helped to improve the detection rate of carcinomas, calcifications and multifocality in Asian breasts.     

Performance of computer-aided detection applied to full-field digital mammography in detection of breast cancers

Objective

The aim of this retrospective study was to evaluate performance of computer-aided detection (CAD) with full-field digital mammography (FFDM) in detection of breast cancers.

Materials and Methods

CAD was retrospectively applied to standard mammographic views of 127 cases with biopsy proven breast cancers detected with FFDM (Senographe 2000, GE Medical Systems). CAD sensitivity was assessed in total group of 127 cases and for subgroups based on breast density, mammographic lesion type, mammographic lesion size, histopathology and mode of presentation.

Results

Overall CAD sensitivity was 91% (115 of 127 cases). There were no statistical differences (p > 0.1) in CAD detection of cancers in dense breasts 90% (53/59) versus non-dense breasts 91% (62/68). There was statistical difference (p < 0.05) in CAD detection of cancers that appeared mammographically as microcalcifications only versus other mammographic manifestations. CAD detected 100% (44/44) of cancers manifesting as microcalcifications, 89% (47/53) as no-calcified masses or asymmetries, 88% (14/16) as masses with associated calcifications, and 71% (10/14) as architectural distortions. CAD sensitivity for cancers 1-10 mm was 84% (38/45); 11-20 mm 93% (55/59); and >20 mm 97% (22/23).

Conclusion

CAD applied to FFDM showed 100% sensitivity in identifying cancers manifesting as microcalcifications only and high sensitivity 86% (71/83) for other mammographic appearances of cancer. Sensitivity is influenced by lesion size. CAD in FFDM is an adjunct helping radiologist in early detection of breast cancers.     

Computer-aided detection in direct digital full-field mammography: initial results

For the first time, full-field digital mammography (FFDM) allows computer-aided detection (CAD) analysis of directly acquired digital image data. The purpose of this study was to evaluate a CAD system in patients with histologically correlated breast cancer depicted with FFDM. Sixty-three cases of histologically proven breast cancer detected with FFDM (Senographe 2000D, GE Medical Systems, Buc, France) were analyzed using a CAD system (Image Checker V2.3, R2 Technology, Los Altos, Calif.). Fourteen of these malignancies were characterized as microcalcifications, 37 as masses, and 12 as both. The mammographic findings were categorized as BI-RADS 3 (n=5), BI-RADS 4 (n=17) and BI-RADS 5 (n=40). The sensitivity for malignant lesions and the rate of false-positive marks per image were calculated. The sensitivity and its 95% confidence interval (CI) were estimated. The sensitivity of the CAD R2 system in breast cancer seen on FFDM was 89% for microcalcifications [CI_95%=(70%; 98%)] and 81% for masses [CI_95%=(67%; 91%)]. As expected, the detection rate was higher in lesions categorized as BI-RADS 5 (37 of 40) compared with lesions categorized as BI-RADS 4 (11 of 17). In the group categorized as BI-RADS 3 the detection rate was 4 of 5 lesions; however, this group was very small. The rate of false-positive marks was 0.35 microcalcification marks/image and 0.26 mass marks/image. The overall rate of false-positive marks was 0.61 per image. CAD based on FFDM provides an optimized work flow. Results are equivalent to the results reported for CAD analysis of secondarily digitized image data. Sensitivity for microcalcifications is acceptable and for masses is low. The number of false-positive marks per image should be reduced. Electronic Publication     

The performance of computer-aided detection when analyzing prior mammograms of newly detected breast cancers with special focus on the time interval from initial imaging to detection

Purpose

The clinical role of CAD systems to detect breast cancer, which have not been on cancer containing mammograms not detected by the radiologist was proven retrospectively.

Methods

All patients from 1992 to 2005 with a histologically verified malignant breast lesion and a mammogram at our department, were analyzed in retrospect focussing on the time of detection of the malignant lesion. All prior mammograms were analyzed by CAD (CADx, USA). The resulting CAD printout was matched with the cancer containing images yielding to the radiological diagnosis of breast cancer. CAD performance, sensitivity as well as the association of CAD and radiological features were analyzed.

Results

278 mammograms fulfilled the inclusion criteria. 111 cases showed a retrospectively visible lesion (71 masses, 23 single microcalcification clusters, 16 masses with microcalcifications, in one case two microcalcification clusters). 54/87 masses and 34/41 microcalcifications were detected by CAD.Detection rates varied from 9/20 (ACR 1) to 5/7 (ACR 4) (45% vs. 71%). The detection of microcalcifications was not influenced by breast tissue density.

Conclusion

CAD might be useful in an earlier detection of subtle breast cancer cases, which might remain otherwise undetected.     

Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection

PURPOSE: To retrospectively determine the mammographic characteristics of cancers missed at screening mammography and assess the ability of computer-aided detection (CAD) to mark the missed cancers. MATERIALS AND METHODS: A multicenter retrospective study accrued 1,083 consecutive cases of breast cancer detected at screening mammography. Prior mammograms were available in 427 cases. Of these, 286 had lesions visible in retrospect. The 286 cases underwent blinded review by panels of radiologists; a majority recommended recall for 112 cases. Two experienced radiologists compared prior mammograms in 110 of these cases with the subsequent screening mammograms (when cancer was detected), noting mammographic characteristics of breast density, lesion type, size, morphology, and subjective reasons for possible miss. The prior mammograms were then analyzed with a CAD program. RESULTS: There were 110 patients with 115 cancers. On the prior mammograms with missed cancers, 35 (30%) of the 115 lesions were calcifications, with 17 of 35 (49%) clustered or pleomorphic. Eighty of the 115 (70%) were mass lesions, with 32 of 80 (40%) spiculated or irregular. For calcifications and masses, the most frequently suggested reasons for possible miss were dense breasts (12 of 35; 34%) and distracting lesions (35 of 80; 44%), respectively. CAD marked 30 (86%) of 35 missed calcifications and 58 (73%) of 80 missed masses. CONCLUSION: Detection errors affected cases with calcifications and masses. CAD marked most (77%; 88 of 115) cancers missed at screening mammography that radiologists retrospectively judged to merit recall.     

Screening US in Patients with Mammographically Dense Breasts: Initial Experience with Connecticut Public Act 09-41

Purpose: To determine performance and utilization of screening breast ultrasonography (US) in women with dense breast tissue who underwent additional screening breast US in the 1st year since implementation of Connecticut Public Act 09-41 requiring radiologists to inform patients with heterogeneous or extremely dense breasts at mammography that they may benefit from such examination. Materials and Methods: Informed consent was waived for this institutional review board-approved, HIPAA-compliant retrospective review of 935 women with dense breasts at mammography who subsequently underwent handheld screening and whole-breast US from October 1, 2009, through September 30, 2010. Results: Of 935 women, 614 (65.7%) were at low risk, 149 (15.9%) were at intermediate risk, and 87 (9.3%) were at high risk for breast cancer. Of the screening breast US examinations, in 701 (75.0%), results were classified as Breast Imaging Reporting and Data System (BI-RADS) category 1 or 2; in 187 (20.0%), results were classified as BI-RADS category 3; and in 47 (5.0%), results were classified as BI-RADS category 4. Of 63 aspirations or biopsies recommended and performed in 53 patients, in nine, lesions were BI-RADS category 3, and in 54, lesions were BI-RADS category 4. Among 63 biopsies and aspirations, three lesions were malignant (all BI-RADS category 4, diagnosed with biopsy). All three cancers were smaller than 1 cm, were found in postmenopausal patients, and were solid masses. One cancer was found in each risk group. In 44 of 935 (4.7%) patients, examination results were false-positive. Overall positive predictive value (PPV) for biopsy or aspirations performed in patients with BI-RADS category 4 masses was 6.5% (three of 46; 95% confidence interval [CI]: 1.7%, 19%). Overall cancer detection rate was 3.2 cancers per 1000 women screened (three of 935; 95% CI: 0.8 cancers per 1000 women screened, 10 cancers per 1000 women screened). Conclusion: Technologist-performed handheld screening breast US offered to women in the general population with dense breasts can aid detection of small mammographically occult breast cancers (cancer detection rate, 0.8-10 cancers per 1000 women screened), although the overall PPV is low. ? RSNA, 2012.     

Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion

OBJECTIVE: Computer-aided detection (CAD) algorithms have successfully revealed breast masses and microcalcifications on screening mammography. The purpose of our study was to evaluate the sensitivity of commercially available CAD systems for revealing architectural distortion, the third most common appearance of breast cancer. MATERIALS AND METHODS: Two commercially available CAD systems were used to evaluate screening mammograms obtained in 43 patients with 45 mammographically detected regions of architectural distortion. For each CAD system, we determined the sensitivity for revealing architectural distortion on at least one image of the two-view mammographic examination (case sensitivity) and for each individual mammogram (image sensitivity). Surgical biopsy results were available for each case of architectural distortion. RESULTS: Architectural distortion was deemed present and actionable by a panel of expert breast imagers in 80 views of the 45 cases. One CAD system detected distortion in 22 of 45 cases of distortion (case sensitivity, 49%) and in 30 of 80 mammograms (image sensitivity, 38%); it displayed 0.7 false-positive marks per image. Another CAD system identified distortion in 15 of 45 cases (case sensitivity, 33%) and 17 of 80 mammograms (image sensitivity, 21%); it displayed 1.27 false-positive marks per image. Sensitivity for malignancy-caused distortion was similar to or lower than sensitivity for all causes of distortion. CONCLUSION: Fewer than one half of the cases of architectural distortion were detected by the two most widely available CAD systems used for interpretations of screening mammograms. Considerable improvement in the sensitivity of CAD systems is needed for detecting this type of lesion. Practicing breast imagers who use CAD systems should remain vigilant for architectural distortion.     

Malignant lesions on mammography: accuracy of two different computer-aided detection systems

We retrospectively compared the accuracy of two computer-aided detection (CAD) systems for the detection of malignant breast lesions on full-field digital mammograms. Mammograms of 326 patients were analyzed (117 patients with breast cancer, 209 negative cases), and each set of cases was read by two CAD systems (Second Look versus AccuDetect Galileo). True-positive fractions per image and case for soft densities, microcalcifications, and total cancers were assessed. Study results showed better overall performance of AccuDetect Galileo (when compared to Second Look) in detecting masses, microcalcifications, and all cancer types, especially in extremely dense breast parenchyma.     

Tumour detection rate of a new commercially available computer-aided detection system

The aim of this study was to determine the tumour detection rate and false positive rate of a new mammographic computer-aided detection system (CAD) in order to assess its clinical usefulness. The craniocaudal and oblique images of 150 suspicious mammograms from 150 patients that were histologically proven to be malignant were analysed using the Second Look CAD (CADx Medical Systems, Quebec, Canada). Cases were selected randomly using the clinic's internal tumour case sampler. Correct marking of the malignant lesion in at least one view was scored as a true positive. Marks not at the location of the malignant lesion were scored as false positives. In addition, mammograms with histologically proven benign masses ( n=50) and microcalcifications ( n=50), as well as 100 non-suspicious mammograms, were scanned in order to determine the value of false-positive marks per image. The 150 mammograms included 94 lesions that were suspicious due to masses, 26 due to microcalcifications and 30 showed both signs of malignancy. The overall sensitivity was 90.0% (135 of 150). Sensitivity on subsets of the data was 88.7% (110 of 124) for suspicious masses (MA) and 98.2% (55 of 56) for microcalcifications. Eight of 14 false-negative cases were large lesions. The overall false-positive rate was observed as 0.28 and 0.97 marks per image of microcalcifications and masses, respectively. The lowest false-positive rates for microcalcifications and MA were observed in the cancer subgroup, whereas the highest false-positive rates were scored in the benign but mammographically suspicious subgroups, respectively. The new CAD system shows a high tumour detection rate, with approximately 1.3 false positive marks per image. These results suggest that this system might be clinically useful as a second reader of mammograms. The system performance was particularly useful for detecting microcalcifications.     

The incremental contribution of clinical breast examination to invasive cancer detection in a mammography screening program

OBJECTIVE: The objective of this study was to determine the potential added contribution of clinical breast examination (CBE) to invasive breast cancer detection in a mammography screening program, by categories of age and breast density. SUBJECTS AND METHODS: We prospectively followed 61,688 women aged 40 years or older who had undergone at least one screening examination with mammography and CBE between January 1, 1996, and December 31, 2000, for 1 year after their mammogram for invasive cancer. We computed the incremental sensitivity, specificity, and positive predictive value of CBE over mammography alone for combinations of age and breast density (predominantly fatty or dense). RESULTS: Mammography sensitivity was 78% and combined mammography-CBE sensitivity was 82%, thus CBE detected an additional 4% of invasive cancers. CBE detected a minority of invasive cancers compared with mammography for all age groups and all breast densities. Sensitivity increased from adding CBE to screening mammography for all ages, from 6.8% in women ages 50-59 with dense breasts to 1.8% in women ages 60-69 years with fatty breasts. CBE generally added incrementally more to sensitivity among women with dense breasts. Specificity and positive predictive value declined when CBE was used in conjunction with mammography, and this decrement was more pronounced in women with dense breasts. CONCLUSION: CBE had modest incremental benefit to invasive cancer detection over mammography alone in a screening program, but also led to greater risk of false-positive results. These risks and benefits were greater in women with dense breasts. The balance of risks and benefits must be weighed carefully when evaluating the inclusion of CBE in a screening examination.     

Sensitivity of a Direct Computer-aided Detection System in Full-field Digital Mammography for Detection of Microcalcifications Not Associated with Mass or Architectural Distortion

Purpose

The purpose of this study was to evaluate the sensitivity of a direct computer-aided detection (CAD) system (d-CAD) in full-field digital mammography (FFDM) for the detection of microcalcifications not associated with mass or architectural distortion.

Materials and Methods

A database search of 1063 consecutive stereotactic core biopsies performed between 2002 and 2005 identified 196 patients with Breast Imaging-Reporting and Data System (BI-RADS) 4 and 5 microcalcifications not associated with mass or distortion detected exclusively by bilateral FFDM. A commercially available CAD system (Second Look, version 7.2) was retrospectively applied to the craniocaudal and mediolateral oblique views in these patients (mean age, 59 years; range, 35–84 years). Breast density, location and mammographic size of the lesion, distribution, and tumour histology were recorded and analysed by using χ², Fisher exact, or McNemar tests, when applicable.

Results

When using d-CAD, 71 of 74 malignant microcalcification cases (96%) and 101 of 122 benign microcalcifications (83%) were identified. There was a significant difference (P < .05) between CAD sensitivity on the craniocaudal view, 91% (68 of 75), vs CAD sensitivity on the mediolateral oblique view, 80% (60 of 75). The d-CAD sensitivity for dense breast tissue (American College of Radiology [ACR] density 3 and 4) was higher (97%) than d-CAD sensitivity (95%) for nondense tissue (ACR density 1 and 2), but the difference was not statically significant. All 28 malignant calcifications larger than 10 mm were detected by CAD, whereas the sensitivity for lesions small than or equal to 10 mm was 94%.

Conclusions

D-CAD had a high sensitivity in the depiction of asymptomatic breast cancers, which were seen as microcalcifications on FFDM screening, with a sensitivity of d-CAD on the craniocaudal view being significantly better. All malignant microcalcifications larger than 10 mm were detected by d-CAD.     

Accuracy of assigned BI-RADS breast density category definitions

RATIONALE AND OBJECTIVES: Quantitative criteria for the Breast Imaging Reporting and Data System (BI-RADS) mammographic density categories have recently been defined as <25% dense for almost entirely fatty, 25%-50% dense for scattered fibroglandular densities, 51%-75% for heterogeneously dense, and >75% dense for the extremely dense category. The purpose of this study is to compare the range of percent mammographic densities with radiologist-assigned BI-RADS mammographic density categories and compare with the recently issued definitions. MATERIALS AND METHODS: In this study, 200 consecutive negative analog screening mammograms were assigned BI-RADS mammographic density categories independently by three radiologists blinded to the other readers' density assignment. Quantitative assessment of percent mammographic density was performed using previously validated software. RESULTS: All three readers agreed on BI-RADS mammographic density categories in 98 cases (49%), and two of three readers agreed in all 200 cases. Using two reader's consensus, median mammographic density (range) was 6.0% (0.5%-19.2%) for fatty, 14.8% (1.2%-52.7%) for scattered densities, 51.2% (15.9%-82.2%) for heterogeneously dense, and 78.4% (60.1%-87.9%) for extremely dense breasts. The percent mammographic density ranges for fatty and extremely dense breasts correlated well with BI-RADS definitions, whereas the ranges of densities in the scattered and heterogeneously dense categories were considerably broader. CONCLUSION: Fatty and extremely dense BI-RADS categories compare relatively well to defined criteria, and therefore may be helpful in breast cancer risk models. Scattered fibroglandular densities and heterogeneously dense categories have broad percent mammographic density ranges and may not function well in breast cancer risk models.     

Breast cancer: importance of spiculation in computer-aided detection

PURPOSE: To determine the prevalence of spiculation in a large series of screening-detected breast cancers appearing as masses on mammograms and to assess the sensitivity of a computer-aided detection (CAD) algorithm that uses spiculation measures in the detection of such lesions. MATERIALS AND METHODS: Six hundred seventy-seven consecutive cases of breast cancers detected as masses on mammograms were independently reviewed by three radiologists who determined if the lesions were spiculated. All cancers were then analyzed by the CAD system. RESULTS: All three radiologists interpreted 375 (55%) of the 677 masses as being spiculated on at least one view. The CAD algorithm correctly marked 322 (86%) of the 375 clearly spiculated masses, with a mean of 0.24 additional mass mark per image. With a looser definition of spiculation, 585 (86%) of the 677 masses were called spiculated by at least one radiologist on one view. The algorithm correctly marked 464 (79%) of the 585 lesions that were spiculated or possibly spiculated. CONCLUSION: Spiculation was clearly present in a majority (55%) of consecutive screening-detected breast cancer masses found on mammograms in a large clinical trial. Incorporation of spiculation measures is, therefore, an important strategy in the detection of breast cancer with CAD. A present-generation CAD algorithm correctly identified a large proportion (86%) of spiculated breast cancers.     

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%).     

Reproducibility of Computer-Aided Detection Marks in Digital Mammography

Objective

To evaluate the performance and reproducibility of a computer-aided detection (CAD) system in mediolateral oblique (MLO) digital mammograms taken serially, without release of breast compression.

Materials and Methods

A CAD system was applied preoperatively to the full-field digital mammograms of two MLO views taken without release of breast compression in 82 patients (age range: 33 - 83 years; mean age: 49 years) with previously diagnosed breast cancers. The total number of visible lesion components in 82 patients was 101: 66 masses and 35 microcalcifications. We analyzed the sensitivity and reproducibility of the CAD marks.

Results

The sensitivity of the CAD system for first MLO views was 71% (47/66) for masses and 80% (28/35) for microcalcifications. The sensitivity of the CAD system for second MLO views was 68% (45/66) for masses and 17% (6/35) for microcalcifications. In 84 ipsilateral serial MLO image sets (two patients had bilateral cancers), identical images, regardless of the existence of CAD marks, were obtained for 35% (29/84) and identical images with CAD marks were obtained for 29% (23/78). Identical images, regardless of the existence of CAD marks, for contralateral MLO images were 65% (52/80) and identical images with CAD marks were obtained for 28% (11/39). The reproducibility of CAD marks for the true positive masses in serial MLO views was 84% (42/50) and that for the true positive microcalcifications was 0% (0/34).

Conclusion

The CAD system in digital mammograms showed a high sensitivity for detecting masses and microcalcifications. However, reproducibility of microcalcification marks was very low in MLO views taken serially without release of breast compression. Minute positional change and patient movement can alter the images and result in a significant effect on the algorithm utilized by the CAD for detecting microcalcifications.     

Computer-aided detection (CAD) in mammography: Does it help the junior or the senior radiologist?

OBJECTIVES: To evaluate the impact of a computer-aided detection (CAD) system on the ability of a junior and senior radiologist to detect breast cancers on mammograms, and to determine the potential of CAD as a teaching tool in mammography. METHODS: Hundred biopsy-proven cancers and 100 normal mammograms were randomly analyzed by a CAD system. The sensitivity (Se) and specificity (Sp) of the CAD system were calculated. In the second phase, to simulate daily practice, 110 mammograms (97 normal or with benign lesions, and 13 cancers) were examined independently by a junior and a senior radiologist, with and without CAD. Interpretations were standardized according to BI-RADS classification. Sensitivity, Specificity, positive and negative predictive values (PPV, NPV) were calculated for each session. RESULTS: For the senior radiologist, Se slightly improved from 76.9 to 84.6% after CAD analysis (NS) (one case of clustered microcalcifications case overlooked by the senior radiologist was detected by CAD). Sp, PPV and PNV did not change significantly. For the junior radiologist, Se improved from 61.9 to 84.6% (significant change). Three cancers overlooked by the junior radiologist were detected by CAD. Sp was unchanged. CONCLUSION: CAD mammography proved more useful for the junior than for the senior radiologist, improving sensitivity. The CAD system may represent a useful educational tool for mammography.     

Computer-aided diagnosis in full digital mammography

RATIONALE AND OBJECTIVES: The authors clarify the detection rates for breast cancerous tumors and clustered microcalcifications with computer-aided diagnosis (CAD) based on Fuji Computed Radiography. The authors also determine whether mammographic reading with CAD contributes to the discovery of breast cancer. METHODS: Data acquired by Fuji Computed Radiography 9000, which consisted of 4148 digital mammograms including 267 cases of breast cancer, was transferred directly to an analysis workstation where an original software program determined extraction rates for breast tumors and clustered microcalcifications. Furthermore, using another 344 mammograms from 86 women, observer performance studies were conducted on five doctors for receiver operating characteristic (ROC) analysis. RESULTS: Sensitivity to breast cancerous tumors and clustered microcalcifications were 89.9% and 92.8%, respectively false-positive rates were 1.35 and 0.40 per image, respectively. The observer performance studies indicate that an average Az value for the five doctors was greater with the CAD system than with a film-only reading without CAD, and that a reading with CAD was significantly superior at P < 0.022. CONCLUSIONS: It has been shown that CAD based on Fuji Computed Radiography offers good detection rates for both breast cancerous tumors and clustered microcalcifications, and that the reading of mammograms with this CAD system would provide potential improvement in diagnostic accuracy for breast cancer.