Effect of Computer-Aided Detection on Independent Double Reading of Paired Screen-Film and Full-Field Digital Screening Mammograms

[Measurements errors in screening mammogram interpretation by radiologists]

The timely detection of breast cancer is achieved through mammography; however, the quality of the procedure should be addressed for proper performance and interpretation. Despite recent improvements in quality assurance in mammography, interpretation still depends on each reader; therefore, errors can be made when interpreting screening mammograms, leading to unnecessary biopsies and/or overdiagnosis, with sustained physical, economic and psychological consequences. Since interpretation is related to the perceptive and cognitive ability of the radiologist, it is necessary to have extensive knowledge about the possible errors that may occur during interpretation, as well as of the way how they can be reduced, prevented and/or corrected to provide the patient with the highest possible level of safety.

The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review

BACKGROUND

Early detection of breast cancer (BC) is crucial in lowering the mortality.

PURPOSE

To present an overview of studies concerning computer-aided detection (CAD) in screening mammography for early detection of BC and compare diagnostic accuracy and recall rates (RR) of single reading (SR) with SR + CAD and double reading (DR) with SR + CAD.

MATERIAL AND METHODS

PRISMA guidelines were used as a review protocol. Articles on clinical trials concerning CAD for detection of BC in a screening population were included. The literature search resulted in 1522 records. A total of 1491 records were excluded by abstract and 18 were excluded by full text reading. A total of 13 articles were included.

RESULTS

All but two studies from the SR vs. SR + CAD group showed an increased sensitivity and/or cancer detection rate (CDR) when adding CAD. The DR vs. SR + CAD group showed no significant differences in sensitivity and CDR. Adding CAD to SR increased the RR and decreased the specificity in all but one study. For the DR vs. SR + CAD group only one study reported a significant difference in RR.

CONCLUSION

All but two studies showed an increase in RR, sensitivity and CDR when adding CAD to SR. Compared to DR no statistically significant differences in sensitivity or CDR were reported. Additional studies based on organized population-based screening programs, with longer follow-up time, high-volume readers, and digital mammography are needed to evaluate the efficacy of CAD.

Errors in Mammography Cannot be Solved Through Technology Alone

Mammography has been the frontline screening tool for breast cancer for decades. However, high error rates in the form of false negatives (FNs) and false positives (FPs) have persisted despite technological improvements. Radiologists still miss between 10% and 30% of cancers while 80% of woman recalled for additional views have normal outcomes, with 40% of biopsied lesions being benign. Research show that the majority of cancers missed is actually visible and looked at, but either go unnoticed or are deemed to be benign. Causal agents for these errors include human related characteristics resulting in contributory search, perception and decision-making behaviours. Technical, patient and lesion factors are also important relating to positioning, compression, patient size, breast density and presence of breast implants as well as the nature and subtype of the cancer itself, where features such as architectural distortion and triple-negative cancers remain challenging to detect on screening. A better understanding of these causal agents as well as the adoption of technological and educational interventions, which audits reader performance and provide immediate perceptual feedback, should help. This paper reviews the current status of our knowledge around error rates in mammography and explores the factors impacting it. It also presents potential solutions for maximizing diagnostic efficacy thus benefiting the millions of women who undergo this procedure each year.

Evolution of breast cancer screening in the Medicare population: clinical and economic implications

BACKGROUND

Newer approaches to mammography, including digital image acquisition and computer-aided detection (CAD), and adjunct imaging (e.g., magnetic resonance imaging [MRI]) have diffused into clinical practice. The impact of these technologies on screening-related cost and outcomes remains undefined, particularly among older women.

METHODS

Using the Surveillance, Epidemiology, and End Results-Medicare linked database, we constructed two cohorts of women without a history of breast cancer and followed each cohort for 2 years. We compared the use and cost of screening mammography including digital mammography and CAD, adjunct procedures including breast ultrasound, MRI, and biopsy between the period of 2001 and 2002 and the period of 2008 and 2009 using χ(2) and t test. We also assessed the change in breast cancer stage and incidence rates using χ(2) and Poisson regression. All statistical tests were two-sided.

RESULTS

There were 137150 women (mean age = 76.0 years) in the early cohort (2001-2002) and 133097 women (mean age = 77.3 years) in the later cohort (2008-2009). The use of digital image acquisition for screening mammography increased from 2.0% in 2001 and 2002 to 29.8% in 2008 and 2009 (P < .001). CAD use increased from 3.2% to 33.1% (P < .001). Average screening-related cost per capita increased from $76 to $112 (P < .001), with annual national fee-for-service Medicare spending increasing from $666 million to $962 million. There was no statistically significant change in detection rates of early-stage tumors (2.45 vs 2.57 per 1000 person-years; P = .41).

CONCLUSIONS

Although breast cancer screening-related costs increased substantially from 2001 through 2009 among Medicare beneficiaries, a clinically significant change in stage at diagnosis was not observed.

Detection of breast cancer with a computer-aided detection applied to full-field digital mammography

A study was conducted to evaluate the sensitivity of computer-aided detection (CAD) with full-field digital mammography in detection of breast cancer, based on mammographic appearance and histopathology. Retrospectively, CAD sensitivity was assessed in total group of 152 cases for subgroups based on breast density, mammographic presentation, lesion size, and results of histopathological examination. The overall sensitivity of CAD was 91 % (139 of 152 cases). CAD detected 100 % (47/47) of cancers manifested as microcalcifications; 98 % (62/63) of those manifested as non-calcified masses; 100 % (15/15) of those manifested as mixed masses and microcalcifications; 75 % (12/16) of those manifested as architectural distortions, and 69 % (18/26) of those manifested as focal asymmetry. CAD sensitivity was 83 % (10/12) for cancers measuring 1-10 mm, 92 % (37/40) for those measuring 11-20 mm, and 92 % (92/100) for those measuring >20 mm. There was no significant difference in CAD detection efficiency between cancers in dense breasts (88 %; 69/78) and those in non-dense breasts (95 %; 70/74). CAD showed a high sensitivity of 91 % (139/152) for the mammographic appearance of cancer and 100 % sensitivity for identifying cancers manifested as microcalcifications. Sensitivity was not influenced by breast density or lesion size. CAD should be effective for helping radiologists detect breast cancer at an earlier stage.

Role of computer-aided detection in very small screening detected invasive breast cancers

This study aims to assess computer-aided detection (CAD) performance with full-field digital mammography (FFDM) in very small (equal to or less than 1 cm) invasive breast cancers. Sixty-eight invasive breast cancers less than or equal to 1 cm were retrospectively studied. All cases were detected with FFDM in women aged 49-69 years from our breast cancer screening program. Radiological characteristics of lesions following BI-RADS descriptors were recorded and compared with CAD sensitivity. Age, size, BI-RADS classification, breast density type, histological type of the neoplasm, and role of the CAD were also assessed. Per-study specificity and mass false-positive rate were determined by using 100 normal consecutive studies. Thirty-seven (54.4 %) masses, 17 (25 %) calcifications, 6 (8.8 %) masses with calcifications, 7 (10.3 %) architectural distortions, and 1 asymmetry (1.5 %) were found. CAD showed an overall sensitivity of 86.7 % (masses, 86.5 %; calcifications, 100 %; masses with calcifications, 100 %; and architectural distortion, 57.14 %), CAD failed to detect 9 out of 68 cases: 5 of 37 masses, 3 of 7 architectural distortions, and 1 of 1 asymmetry. Fifteen out of 37 masses were hyperdense, and all of them were detected by CAD. No association was seen among mass morphology or margins and detectability. Per-study specificity and CAD false-positive rate was 26 % and 1.76 false marks per study. In conclusion, CAD shows a high sensitivity and a low specificity. Lesion size, histology, and breast density do not influence sensitivity. Mammographic features, mass density, and thickness of the spicules in architectural distortions do influence.

[Population-based breast cancer screening: certainties, controversies, and future perspectives]

Population-based breast cancer screening programs based on mammography must maintain a high level of quality, so the results must be constantly monitored. Although most authors consider that these programs have decreased the mortality due to breast cancer by about 30%, others claim that the mortality has decreased by only about 12% due to errors in the randomization of patients, because the rate of advanced tumors has hardly decreased and because adjuvant treatment also improves survival. Other criticisms focus on overdiagnosis and overtreatment. We believe that despite the unquestionable value of mammographic screening, we should be open to certain changes such as the stratification of patients by their level of risk and the introduction of complementary techniques like tomosynthesis, ultrasonography, and magnetic resonance imaging in selected cases.

Perceptual training for visual search

People are better at visual search than the best fully automated methods. Despite this, visual search remains a difficult perceptual task. The goal of this investigation was to experimentally test the ways in which visual search performance could be improved through two categories of training interventions: perceptual training and conceptual training. To determine the effects of each training on a later performance task, the two types of trainings were manipulated using a between-subjects design (conceptual vs. perceptual × training present vs. training absent). Perceptual training led to speed and accuracy improvements in visual search. Issues with the design and administration of the conceptual training limited conclusions on its effectiveness but provided useful lessons for conceptual training design. The results suggest that when the visual search task involves detecting heterogeneous or otherwise unpredictable stimuli, perceptual training can improve visual search performance. Similarly, careful consideration of the performance task and training design is required to evaluate the effectiveness of conceptual training.

PRACTITIONER SUMMARY

Visual search is a difficult, yet critical, task in industries such as baggage screening and radiology. This study investigated the effectiveness of perceptual training for visual search. The results suggest that when visual search involves detecting heterogeneous or otherwise unpredictable stimuli, perceptual training may improve the speed and accuracy of visual search.

Assessing the stand-alone sensitivity of computer-aided detection with cancer cases from the Digital Mammographic Imaging Screening Trial

OBJECTIVE

The purpose of this study was to assess the sensitivities and false-detection rates of two computer-aided detection (CADe) systems when applied to digital or film-screen mammograms in detecting the known breast cancer cases from the Digital Mammographic Imaging Screening Trial (DMIST) breast cancer screening population.

MATERIALS AND METHODS

Available film-screen and digital mammograms of 161 breast cancer cases from DMIST were analyzed by two CADe systems, iCAD Second-Look and R2 ImageChecker. Three experienced breast-imaging radiologists reviewed the CADe marks generated for each available cancer case, recording the number and locations of CADe marks and whether each CADe mark location corresponded with the known location of the cancer.

RESULTS

For the 161 cancer cases included in this study, the sensitivities of the DMIST reader without CAD were 0.43 (69/161, 95% CI 0.35-0.51) for digital and 0.41 (66/161, 0.33-0.49) for film-screen mammography. The sensitivities of iCAD were 0.74 (119/161, 0.66-0.81) for digital and 0.69 (111/161, 0.61-0.76) for film-screen mammography, both significantly higher than the DMIST study sensitivities (p < 0.0001 for both). The average number of false CADe marks per case of iCAD was 2.57 (SD, 1.92) for digital and 3.06(1.72) for film-screen mammography. The sensitivity of R2 was 0.74 (119/161, 0.66-0.81) for digital, and 0.60 (97/161, 0.52-0.68) for film-screen mammography, both significantly higher than the DMIST study sensitivities (p < 0.0001 for both). The average number of false CADe marks per case of R2 was 2.07 (1.57) for digital and 1.52 (1.45) for film-screen mammography.

CONCLUSION

Our results suggest the use of CADe in interpretation of digital and film-screen mammograms could lead to improvements in cancer detection.

CAD May Not be Necessary for Microcalcifications in the Digital era, CAD May Benefit Radiologists for Masses

Objective:

The aim of this study was to evaluate the effectiveness of computer-aided detection (CAD) to mark the cancer on digital mammograms at the time of breast cancer diagnosis and also review retrospectively whether CAD marked the cancer if visible on any available prior mammograms, thus potentially identifying breast cancer at an earlier stage. We sought to determine why breast lesions may or may not be marked by CAD. In particular, we analyzed factors such as breast density, mammographic views, and lesion characteristics.

Materials and Methods:

Retrospective review from 2004 to 2008 revealed 3445 diagnosed breast cancers in both symptomatic and asymptomatic patients; 1293 of these were imaged with full field digital mammography (FFDM). After cancer diagnosis, in a retrospective review held by the radiologist staff, 43 of these cancers were found to be visible on prior-year mammograms (false-negative cases); these breast cancer cases are the basis of this analysis. All cases had CAD evaluation available at the time of cancer diagnosis and on prior mammography studies. Data collected included patient demographics, breast density, palpability, lesion type, mammographic size, CAD marks on current- and prior-year mammograms, needle biopsy method, pathology results (core needle and/or surgical), surgery type, and lesion size.

Results:

On retrospective review of the mammograms by the staff radiologists, 43 cancers were discovered to be visible on prior-year mammograms. All 43 cancers were masses (mass classification included mass, mass with calcification, and mass with architectural distortion); no pure microcalcifications were identified in this cohort. Mammograms with CAD applied at the time of breast cancer diagnosis were able to detect 79% (34/43) of the cases and 56% (24/43) from mammograms with CAD applied during prior year(s). In heterogeneously dense/extremely dense tissue, CAD marked 79% (27/34) on mammograms taken at the time of diagnosis and 56% (19/34) on mammograms with CAD applied during the prior year(s). At time of diagnosis, CAD marked lesions in 32% (11/34) on the craniocaudal (CC) view, 21% (7/34) on the mediolateral oblique (MLO) view. Lesion size of those marked by CAD or not marked were similar, the average being 15 and 12 mm, respectively.

Conclusion:

CAD marked cancers on mammograms at the time of diagnosis in 79% of the cases and in 56% of the cases from the mammograms with CAD applied in the prior year(s). Our review demonstrated that CAD can mark invasive breast carcinomas in even dense breast tissue. CAD marked a significant portion on the CC view only, which may be an indicator to radiologists to be especially vigilant when a lesion is marked on this view.

Is single reading with computer-aided detection (CAD) as good as double reading in mammography screening? A systematic review

BACKGROUND

In accordance with European guidelines, mammography screening comprises independent readings by two breast radiologists (double reading). CAD (computer-aided detection) has been suggested to complement or replace one of the two readers (single reading + CAD).The aim of this systematic review is to address the following question: Is the reading of mammographic x-ray images by a single breast radiologist together with CAD at least as accurate as double reading?

METHODS

The electronic literature search included the databases Pub Med, EMBASE and The Cochrane Library. Two independent reviewers assessed abstracts and full-text articles.

RESULTS

1049 abstracts were identified, of which 996 were excluded with reference to inclusion and exclusion criteria; 53 full-text articles were assessed for eligibility. Finally, four articles were included in the qualitative analysis, and one in a GRADE synthesis.

CONCLUSIONS

The scientific evidence is insufficient to determine whether the accuracy of single reading + CAD is at least equivalent to that obtained in standard practice, i.e. double reading where two breast radiologists independently read the mammographic images.

Mammography screening using independent double reading with consensus: is there a potential benefit for computer-aided detection?

BACKGROUND

Double reading improves the cancer detection rate in mammography screening. Single reading with computer-aided detection (CAD) has been considered to be an alternative to double reading. Little is known about the potential benefit of CAD in breast cancer screening with double reading.

PURPOSE

To compare prospective independent double reading of screen-film (SFM) and full-field digital (FFDM) mammography in population-based screening with retrospective standalone CAD performance on the baseline mammograms of the screen-detected cancers and subsequent cancers diagnosed during the follow-up period.

MATERIAL AND METHODS

The study had ethics committee approval. A 5-point rating scale for probability of cancer was used for 23,923 (SFM = 16,983; FFDM = 6940) screening mammograms. Of 208 evaluable cancers, 104 were screen-detected and 104 were subsequent (44 interval and 60 next screening round) cancers. Baseline mammograms of subsequent cancers were retrospectively classified in consensus without information about cancer location, histology, or CAD prompting as normal, non-specific minimal signs, significant minimal signs, and false-negatives. The baseline mammograms of the screen-detected cancers and subsequent cancers were evaluated by CAD. Significant minimal signs and false-negatives were considered 'actionable' and potentially diagnosable if correctly prompted by CAD.

RESULTS

CAD correctly marked 94% (98/104) of the baseline mammograms of the screen-detected cancers (SFM = 95% [61/64]; FFDM = 93% [37/40]), including 96% (23/24) of those with discordant interpretations. Considering only those baseline examinations of subsequent cancers prospectively interpreted as normal and retrospectively categorized as 'actionable', CAD input at baseline screening had the potential to increase the cancer detection rate from 0.43% to 0.51% (P = 0.13); and to increase cancer detection by 16% ([104 + 17]/104) and decrease interval cancers by 20% (from 44 to 35).

CONCLUSION

CAD may have the potential to increase cancer detection by up to 16%, and to reduce the number of interval cancers by up to 20% in SFM and FFDM screening programs using independent double reading with consensus review. The influence of true- and false-positive CAD marks on decision-making can, however, only be evaluated in a prospective clinical study.

False positive marks on unsuspicious screening mammography with computer-aided detection

The contribution of computer-aided detection (CAD) systems as an interpretive aid in screening mammography can be hampered by a high rate of false positive detections. Specificity, false positive rate, and ease of dismissing false positive marks from two CAD systems are retrospectively evaluated. One hundred screening mammographic studies with a BI-RADS assessment code of 1 or 2 and at least 2-year normal mammographic follow-up were retrospectively reviewed using two CAD systems. Breast density, CAD marks, and radiologist's ease of dismissing false positive marks were recorded. Specificities from the two CAD versions considering all marks were 23% and 15% (p value = 0.07); mass marks, 35% and 17% (p value < 0.01); and calcification marks 62% and 75% (p value = 0.01). The two CAD versions did not differ regarding mean and median marks per case for all marks (2.3, 2.0 and 2.3, 2.0, p value = 0.65) or mass marks (1.6, 1.0 and 1.8, 2.0, p value = 0.15), but differed for calcification marks (0.8, 0 and 0.5, 0, p value < 0.01). Slightly higher specificity and fewer marks per case observed in dense breasts did not reach statistical significance. The reviewing radiologist classified most marks from both CAD systems (84% and 88%) as very easy/easy to dismiss. The two CAD versions had small differences in specificity and false positive marks. Differences, although not statistically significant, in specificities and false positive rates between dense and non-dense breasts warrant further research. Most false positive marks are easily dismissed and should not affect clinical performance.

Quantification of the UK 5-point breast imaging classification and mapping to BI-RADS to facilitate comparison with international literature

OBJECTIVE

The UK 5-point breast imaging scoring system, recently formalised by the Royal College of Radiologists Breast Group, does not specify the likelihood of malignancy in each category. The breast imaging and reporting data system (BI-RADS) is widely used throughout North America and much of Europe. The main purpose of this study is to quantify the cancer likelihood of each of the UK 5-point categories and map them to comparable BI-RADS categories to facilitate comparison with North American and European literature and publication of UK research abroad.

METHODS

During the 8 year study period, mammogram and ultrasound results were UK scored and the percentage of cancer outcomes within each group calculated. These were then compared with the percentage incidence of the BI-RADS categories.

RESULTS

Of 23 741 separate assessment episodes, 15 288 mammograms and 10 642 ultrasound examinations were evaluated. There was a direct correlation between UK scoring and BI-RADS for categories 1 and 5. UK Score 2 lipomas and simple cysts correlated with BI-RADS 2, with the remaining UK Score 2 lesions (mostly fibroadenomas) assigned to BI-RADS 3. BI-RADS 4 incorporates a wide range of cancer risk (2-95%) with subdivisions a, b and c indicating increasing, but unspecified, likelihood of malignancy. UK Score 3 correlated with BI-RADS 4 a/b and UK Score 4 corresponded with BI-RADS 4c.

CONCLUSION

This study quantifies the cancer likelihood of the UK scoring and maps them to parallel BI-RADS categories, with equivalent cancer risks. This facilitates the ability to share UK research data and clinical practice on an international scale.

Effectiveness of computer-aided detection in community mammography practice

BACKGROUND

Computer-aided detection (CAD) is applied during screening mammography for millions of US women annually, although it is uncertain whether CAD improves breast cancer detection when used by community radiologists.

METHODS

We investigated the association between CAD use during film-screen screening mammography and specificity, sensitivity, positive predictive value, cancer detection rates, and prognostic characteristics of breast cancers (stage, size, and node involvement). Records from 684 956 women who received more than 1.6 million film-screen mammograms at Breast Cancer Surveillance Consortium facilities in seven states in the United States from 1998 to 2006 were analyzed. We used random-effects logistic regression to estimate associations between CAD and specificity (true-negative examinations among women without breast cancer), sensitivity (true-positive examinations among women with breast cancer diagnosed within 1 year of mammography), and positive predictive value (breast cancer diagnosed after positive mammograms) while adjusting for mammography registry, patient age, time since previous mammography, breast density, use of hormone replacement therapy, and year of examination (1998-2002 vs 2003-2006). All statistical tests were two-sided.

RESULTS

Of 90 total facilities, 25 (27.8%) adopted CAD and used it for an average of 27.5 study months. In adjusted analyses, CAD use was associated with statistically significantly lower specificity (OR = 0.87, 95% confidence interval [CI] = 0.85 to 0.89, P < .001) and positive predictive value (OR = 0.89, 95% CI = 0.80 to 0.99, P = .03). A non-statistically significant increase in overall sensitivity with CAD (OR = 1.06, 95% CI = 0.84 to 1.33, P = .62) was attributed to increased sensitivity for ductal carcinoma in situ (OR = 1.55, 95% CI = 0.83 to 2.91; P = .17), although sensitivity for invasive cancer was similar with or without CAD (OR = 0.96, 95% CI = 0.75 to 1.24; P = .77). CAD was not associated with higher breast cancer detection rates or more favorable stage, size, or lymph node status of invasive breast cancer.

CONCLUSION

CAD use during film-screen screening mammography in the United States is associated with decreased specificity but not with improvement in the detection rate or prognostic characteristics of invasive breast cancer.

Missed and true interval and screen-detected breast cancers in a population based screening program

RATIONALE AND OBJECTIVES

To increase radiologic knowledge, the distribution of mammographic features on prior screening mammograms of missed interval and screen-detected cancers was compared to the distribution on diagnostic mammograms of screen-detected cancers. The same variables were compared on mammograms of discordant and concordant screen-detected cancers.

MATERIALS AND METHODS

The study was performed in Møre og Romsdal County, Norway, as a part of the quality assurance of the Norwegian Breast Cancer Screening Program. Women were screened using analog techniques and diagnosed from 2002 to 2008. Prior and diagnostic mammograms of 81 interval and 123 screen-detected breast cancers in women aged 50 to 71 years were retrospectively reviewed and classified as either missed or true by four experienced breast radiologists. Mammographic features were classified according to a modified Breast Imaging Reporting and Data System.

RESULTS

Thirty percent (24 of 81) of the interval cancers and 21% (26 of 123) of the screen-detected cancers were classified as missed. Calcifications, alone or in association with mass or asymmetry, tended to be more common on prior mammograms of missed cancers compared to diagnostic mammograms of screen-detected cancers (34% [17 of 50] vs 21% [26 of 123], P = .114), whereas an opposite trend was seen for mass (54% [27 of 50] vs 68% [84 of 123], P = .109). Similar results were seen when comparing discordant and concordant cancers.

CONCLUSIONS

Calcifications represent a challenge in the interpretation of screening mammograms. For educational purposes, the importance of reviewing both interval and screen-detected cancers is obvious. Knowledge gained from systematic reviews might reduce the number of missed cancers on mammographic screening. Performing reviews according to established guidelines would make it possible to compare results across screening programs.

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 1-10mm was 84% (38/45); 11-20mm 93% (55/59); and >20mm 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.

[Breast cancer screening imaging: what do we do]

The purpose of this review is to evaluate the value of different breast imaging technics and their place for individual and mass screening of breast cancer according to the randomized studies on digital mammography and ultrasound screening. Analogic and numerical mammograms are validated for screening of women aged from 50 and 74 years. The additional value of ultrasound is therefore proven when the increased risk is moderate. When risk is higher (genetic or familial), MRI is the method of choice associated with conventional imaging. Individual screening is recommended before 50 for women aged from 45 and 50 and for those over 74 using the same procedures as organized screening.

Computer-aided detection of breast carcinoma in standard mammographic projections with digital mammography

PURPOSE

A retrospective evaluation of the ability of computer-aided detection (CAD) ability to identify breast carcinoma in standard mammographic projections.

MATERIALS AND METHODS

Forty-five biopsy proven lesions in 44 patients imaged digitally with CAD applied at examination were reviewed. Forty-four screening BIRADS category 1 digital mammography examinations were randomly identified to serve as a comparative normal/control population. Data included patient age; BIRADS breast density; lesion type, size, and visibility; number, type, and location of CAD marks per image; CAD ability to mark lesions; needle core and surgical pathologic correlation.

RESULTS

The CAD lesion/case sensitivity of 87% (n = 39), image sensitivity of 69% (n = 31) for mediolateral oblique view and 78% (n = 35) for the craniocaudal view was found. The average false positive rate in 44 normal screening cases was 2.0 (range 1-8). The 2.0 figure is based on 88 reported false positive CAD marks in 44 normal screening exams: 98% (n = 44) lesions proceeded to excision; initial pathology upgraded at surgical excision from in situ to invasive disease in 24% (n = 9) lesions.

CONCLUSION

CAD demonstrated potential to detect mammographically visible cancers in standard projections for all lesion types.

Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists' goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists-as opposed to a completely automatic computer interpretation-focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous-from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects-collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more-from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.

Mammographic features and histopathological findings of interval breast cancers

BACKGROUND

Interval cancers are considered a shortcoming in screening mammography due to less favorable prognostic tumor characteristics compared to screening-detected cancers and consequently a lower chance of survival from the disease.

PURPOSE

To describe the mammographic features and prognostic histopathological tumor characteristics of interval breast cancers.

MATERIAL AND METHODS

A total of 231 interval breast cancer cases diagnosed in prevalently screened women aged 50-69 years old were examined. Thirty-five percent of the cases were retrospectively classified as missed cancers, 23% as minimal sign, and 42% as true negative (including occult cancers) in a definitive classification performed by six experienced breast radiologists. The retrospective classification described the mammographic features of the baseline screening mammograms in missed and minimal-sign interval cancers, while histopathological reports were used to describe the tumor characteristics in all the subgroups of interval cancers.

RESULTS

Fifty percent of the missed and minimal-sign interval cancers combined presented poorly defined mass or asymmetric density, and 26% calcifications with or without associated density or mass at baseline screening. Twenty-seven percent of invasive tumors were <15 mm for missed and 47% for true interval cancers (P<0.001). Lymph node involvement was more common in missed (49%) compared with the true cases (33%, P<0.05).

CONCLUSION

Missed interval cancers have less prognostically favorable histopathological tumor characteristics compared with true interval cancers. Improving the radiologist's perception and interpretation by establishing systematic collection of features and implementation of organized reviews may decrease the number of interval cancers in a screening program.

Dynamic infrared imaging in identification of breast cancer tissue with combined image processing and frequency analysis

Five combinations of image-processing algorithms were applied to dynamic infrared (IR) images of six breast cancer patients preoperatively to establish optimal enhancement of cancer tissue before frequency analysis. mid-wave photovoltaic (PV) IR cameras with 320x254 and 640x512 pixels were used. The signal-to-noise ratio and the specificity for breast cancer were evaluated with the image-processing combinations from the image series of each patient. Before image processing and frequency analysis the effect of patient movement was minimized with a stabilization program developed and tested in the study by stabilizing image slices using surface markers set as measurement points on the skin of the imaged breast. A mathematical equation for superiority value was developed for comparison of the key ratios of the image-processing combinations. The ability of each combination to locate the mammography finding of breast cancer in each patient was compared. Our results show that data collected with a 640x512-pixel mid-wave PV camera applying image-processing methods optimizing signal-to-noise ratio, morphological image processing and linear image restoration before frequency analysis possess the greatest superiority value, showing the cancer area most clearly also in the match centre of the mammography estimation.

Comparing screening mammography for early breast cancer detection in Vermont and Norway

BACKGROUND

Most screening mammography in the United States differs from that in countries with formal screening programs by having a shorter screening interval and interpretation by a single reader vs independent double reading. We examined how these differences affect early detection of breast cancer by comparing performance measures and histopathologic outcomes in women undergoing opportunistic screening in Vermont and organized screening in Norway.

METHODS

We evaluated recall, screen detection, and interval cancer rates and prognostic tumor characteristics for women aged 50-69 years who underwent screening mammography in Vermont (n = 45 050) and in Norway (n = 194 430) from 1997 through 2003. Rates were directly adjusted for age by weighting the rates within 5-year age intervals to reflect the age distribution in the combined data and were compared using two-sided Z tests.

RESULTS

The age-adjusted recall rate was 9.8% in Vermont and 2.7% in Norway (P < .001). The age-adjusted screen detection rate per 1000 woman-years after 2 years of follow-up was 2.77 in Vermont and 2.57 in Norway (P = .12), whereas the interval cancer rate per 1000 woman-years was 1.24 and 0.86, respectively (P < .001). Larger proportions of invasive interval cancers in Vermont than in Norway were 15 mm or smaller (55.9% vs 38.2%, P < .001) and had no lymph node involvement (67.5% vs 57%, P = .01). The prognostic characteristics of all invasive cancers (screen-detected and interval cancer) were similar in Vermont and Norway.

CONCLUSION

Screening mammography detected cancer at about the same rate and at the same prognostic stage in Norway and Vermont, with a statistically significantly lower recall rate in Norway. The interval cancer rate was higher in Vermont than in Norway, but tumors that were diagnosed in the Vermont women tended to be at an earlier stage than those diagnosed in the Norwegian women.

[Current situation and future perspectives of digital mammography]

Digital mammography has extensively replaced conventional film screen mammography and is now the standard in combination with soft copy reading in clinical as well as screening mammography. Large international multicenter studies demonstrate an equivalent or superior detection rate of breast cancers by digital in comparison to conventional mammography especially in dense breasts, premenopausal and perimenopausal women and women less than 50 years old. Computer-aided detection (CAD) is important for the experienced investigator (increased specificity). Digital mammography also offers further options, such as tomosynthesis, digital contrast-enhanced mammography and the combination of digital mammography and ultrasound. The future in breast diagnosis will be the fusion of images from different digital systems.

Article Commentary: Computer-Aided Detection of Breast Cancer — Have All Bases Been Covered?

The use of computer-aided detection (CAD) systems in mammography has been the subject of intense research for many years. These systems have been developed with the aim of helping radiologists to detect signs of breast cancer. However, the effectiveness of CAD systems in practice has sparked recent debate. In this commentary, we argue that computer-aided detection will become an increasingly important tool for radiologists in the early detection of breast cancer, but there are some important issues that need to be given greater focus in designing CAD systems if they are to reach their full potential.