Impact of computer-aided detection in a regional screening mammography program

Examining external control arms in oncology: A scoping review of applications to date

OBJECTIVES

Randomized controlled trials (RCTs) are the gold standard for evaluating the comparative efficacy and safety of new cancer therapies. However, enrolling patients in control arms of clinical trials can be challenging for rare cancers, particularly in the context of precision oncology and targeted therapies. External Control Arms (ECAs) are a potential solution to address these challenges in clinical research design. We conducted a scoping review to explore the use of ECAs in oncology.

METHODS

We systematically searched four databases, namely MEDLINE, EMBASE, Web of Science, and Scopus. We screened titles, abstracts, and full texts for eligible articles focusing on patients undergoing therapy for cancer, employing ECAs, and reporting clinical outcomes.

RESULTS

Of the 629 articles screened, 23 were included in this review. The earliest included studies were published in 1996, while most studies were published in the past 5 years. 44% (10/23) of ECAs were employed in blood-related cancer studies. Geographically, 30% (7/23) of studies were conducted in the United States, 22% (5/23) in Japan, and 9% (2/23) in South Korea. The primary data sources used to construct the ECAs involved pooled data from previous trials (35%, 8/23), administrative health databases (17%, 4/23) and electronic medical records (17%, 4/23). While 52% (12/23) of the studies employed methods to align treatment and ECAs characteristics, 48% (11/23) lacked explicit strategies.

CONCLUSION

ECAs offer a valuable approach in oncology research, particularly when alternative designs are not feasible. However, careful methodological planning and detailed reporting are essential for meaningful and reliable results.

Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial Intelligence-Based CAD

The aim was to determine whether an artificial intelligence (AI)-based, computer-aided detection (CAD) software can be used to reduce false positive per image (FPPI) on mammograms as compared to an FDA-approved conventional CAD. A retrospective study was performed on a set of 250 full-field digital mammograms between January 1, 2013, and March 31, 2013, and the number of marked regions of interest of two different systems was compared for sensitivity and specificity in cancer detection. The count of false-positive marks per image (FPPI) of the two systems was also evaluated as well as the number of cases that were completely mark-free. All results showed statistically significant reductions in false marks with the use of AI-CAD vs CAD (confidence interval = 95%) with no reduction in sensitivity. There is an overall 69% reduction in FPPI using the AI-based CAD as compared to CAD, consisting of 83% reduction in FPPI for calcifications and 56% reduction for masses. Almost half (48%) of cases showed no AI-CAD markings while only 17% show no conventional CAD marks. There was a significant reduction in FPPI with AI-CAD as compared to CAD for both masses and calcifications at all tissue densities. A 69% decrease in FPPI could result in a 17% decrease in radiologist reading time per case based on prior literature of CAD reading times. Additionally, decreasing false-positive recalls in screening mammography has many direct social and economic benefits.

A deep learning-based automated diagnostic system for classifying mammographic lesions

BACKGROUND

Screening mammography has led to reduced breast cancer-specific mortality and is recommended worldwide. However, the resultant doctors' workload of reading mammographic scans needs to be addressed. Although computer-aided detection (CAD) systems have been developed to support readers, the findings are conflicting regarding whether traditional CAD systems improve reading performance. Rapid progress in the artificial intelligence (AI) field has led to the advent of newer CAD systems using deep learning-based algorithms which have the potential to reach human performance levels. Those systems, however, have been developed using mammography images mainly from women in western countries. Because Asian women characteristically have higher-density breasts, it is uncertain whether those AI systems can apply to Japanese women. In this study, we will construct a deep learning-based CAD system trained using mammography images from a large number of Japanese women with high quality reading.

METHODS

We will collect digital mammography images taken for screening or diagnostic purposes at multiple institutions in Japan. A total of 15,000 images, consisting of 5000 images with breast cancer and 10,000 images with benign lesions, will be collected. At least 1000 images of normal breasts will also be collected for use as reference data. With these data, we will construct a deep learning-based AI system to detect breast cancer on mammograms. The primary endpoint will be the sensitivity and specificity of the AI system with the test image set.

DISCUSSION

When the ability of AI reading is shown to be on a par with that of human reading, images of normal breasts or benign lesions that do not have to be read by a human can be selected by AI beforehand. Our AI might work well in Asian women who have similar breast density, size, and shape to those of Japanese women.

TRIAL REGISTRATION

UMIN, trial number UMIN000039009. Registered 26 December 2019, https://www.umin.ac.jp/ctr/.

A review of computer aided detection in mammography

Breast screening with mammography is widely recognized as the most effective method of detecting early breast cancer and has consistently demonstrated a 20-40% decrease in mortality among screened women. Despite this, the sensitivity of mammography ranges between 70 and 90%. Computer aided detection (CAD) is an artificial intelligence (AI) technique that utilizes pattern recognition to highlight suspicious features on imaging and marks them for the radiologist to review and interpret. It aims to decrease oversights made by interpreting radiologists. Here we review the efficacy of CAD and potential future directions.

Artificial intelligence and deep learning - Radiology's next frontier?

Tracing the use of computers in the radiology department from administrative functions through image acquisition, storage, and reporting, to early attempts at improved diagnosis, we begin to imagine possible new frontiers for their use in exam interpretation. Given their initially slow but ultimately substantial progress in the noninterpretive areas, we are left desiring and even expecting more in the interpretation realm. New technological advances may provide the next wave of progress and radiologists should be early adopters. Several potential applications are discussed and hopefully will serve to inspire future progress.

Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study

PURPOSE

To assess the effect of computer-aided detection (CAD) of brain metastasis (BM) on radiologists' diagnostic performance in interpreting three-dimensional brain magnetic resonance (MR) imaging using follow-up imaging and consensus as the reference standard.

MATERIALS AND METHODS

The institutional review board approved this retrospective study. The study cohort consisted of 110 consecutive patients with BM and 30 patients without BM. The training data set included MR images of 80 patients with 450 BM nodules. The test set included MR images of 30 patients with 134 BM nodules and 30 patients without BM. We developed a CAD system for BM detection using template-matching and K-means clustering algorithms for candidate detection and an artificial neural network for false-positive reduction. Four reviewers (two neuroradiologists and two radiology residents) interpreted the test set images before and after the use of CAD in a sequential manner. The sensitivity, false positive (FP) per case, and reading time were analyzed. A jackknife free-response receiver operating characteristic (JAFROC) method was used to determine the improvement in the diagnostic accuracy.

RESULTS

The sensitivity of CAD was 87.3% with an FP per case of 302.4. CAD significantly improved the diagnostic performance of the four reviewers with a figure-of-merit (FOM) of 0.874 (without CAD) vs. 0.898 (with CAD) according to JAFROC analysis (p < 0.01). Statistically significant improvement was noted only for less-experienced reviewers (FOM without vs. with CAD, 0.834 vs. 0.877, p < 0.01). The additional time required to review the CAD results was approximately 72 sec (40% of the total review time).

CONCLUSION

CAD as a second reader helps radiologists improve their diagnostic performance in the detection of BM on MR imaging, particularly for less-experienced reviewers.

Breast masses in mammography classification with local contour features

Background

Mammography is one of the most popular tools for early detection of breast cancer. Contour of breast mass in mammography is very important information to distinguish benign and malignant mass. Contour of benign mass is smooth and round or oval, while malignant mass has irregular shape and spiculated contour. Several studies have shown that 1D signature translated from 2D contour can describe the contour features well.

Methods

In this paper, we propose a new method to translate 2D contour of breast mass in mammography into 1D signature. The method can describe not only the contour features but also the regularity of breast mass. Then we segment the whole 1D signature into different subsections. We extract four local features including a new contour descriptor from the subsections. The new contour descriptor is root mean square (RMS) slope. It can describe the roughness of the contour. KNN, SVM and ANN classifier are used to classify benign breast mass and malignant mass.

Results

The proposed method is tested on a set with 323 contours including 143 benign masses and 180 malignant ones from digital database of screening mammography (DDSM). The best accuracy of classification is 99.66% using the feature of root mean square slope with SVM classifier.

Conclusion

The performance of the proposed method is better than traditional method. In addition, RMS slope is an effective feature comparable to most of the existing features.

Electronic supplementary material

The online version of this article (doi:10.1186/s12938-017-0332-0) contains supplementary material, which is available to authorized users.

Reducing false positives of microcalcification detection systems by removal of breast arterial calcifications

PURPOSE

In the past decades, computer-aided detection (CADe) systems have been developed to aid screening radiologists in the detection of malignant microcalcifications. These systems are useful to avoid perceptual oversights and can increase the radiologists' detection rate. However, due to the high number of false positives marked by these CADe systems, they are not yet suitable as an independent reader. Breast arterial calcifications (BACs) are one of the most frequent false positives marked by CADe systems. In this study, a method is proposed for the elimination of BACs as positive findings. Removal of these false positives will increase the performance of the CADe system in finding malignant microcalcifications.

METHODS

A multistage method is proposed for the removal of BAC findings. The first stage consists of a microcalcification candidate selection, segmentation and grouping of the microcalcifications, and classification to remove obvious false positives. In the second stage, a case-based selection is applied where cases are selected which contain BACs. In the final stage, BACs are removed from the selected cases. The BACs removal stage consists of a GentleBoost classifier trained on microcalcification features describing their shape, topology, and texture. Additionally, novel features are introduced to discriminate BACs from other positive findings.

RESULTS

The CADe system was evaluated with and without BACs removal. Here, both systems were applied on a validation set containing 1088 cases of which 95 cases contained malignant microcalcifications. After bootstrapping, free-response receiver operating characteristics and receiver operating characteristics analyses were carried out. Performance between the two systems was compared at 0.98 and 0.95 specificity. At a specificity of 0.98, the sensitivity increased from 37% to 52% and the sensitivity increased from 62% up to 76% at a specificity of 0.95. Partial areas under the curve in the specificity range of 0.8-1.0 were significantly different between the system without BACs removal and the system with BACs removal, 0.129 ± 0.009 versus 0.144 ± 0.008 (p<0.05), respectively. Additionally, the sensitivity at one false positive per 50 cases and one false positive per 25 cases increased as well, 37% versus 51% (p<0.05) and 58% versus 67% (p<0.05) sensitivity, respectively. Additionally, the CADe system with BACs removal reduces the number of false positives per case by 29% on average. The same sensitivity at one false positive per 50 cases in the CADe system without BACs removal can be achieved at one false positive per 80 cases in the CADe system with BACs removal.

CONCLUSIONS

By using dedicated algorithms to detect and remove breast arterial calcifications, the performance of CADe systems can be improved, in particular, at false positive rates representative for operating points used in screening.

The Hybrid Feature Selection Algorithm Based on Maximum Minimum Backward Selection Search Strategy for Liver Tissue Pathological Image Classification

We propose a novel feature selection algorithm for liver tissue pathological image classification. To improve the efficiency of feature selection, the same feature values of positive and negative samples are removed in rough selection. To obtain the optimal feature subset, a new heuristic search algorithm, which is called Maximum Minimum Backward Selection (MMBS), is proposed in precise selection. MMBS search strategy has the following advantages. (1) For the deficiency of Discernibility of Feature Subsets (DFS) evaluation criteria, which makes the class of small samples invalid for unbalanced samples, the Weighted Discernibility of Feature Subsets (WDFS) evaluation criteria are proposed as the evaluation strategy of MMBS, which is also available for unbalanced samples. (2) For the deficiency of Sequential Forward Selection (SFS) and Sequential Backward Selection (SBS), which can only add or only delete feature, MMBS decides whether to add the feature to feature subset according to WDFS criteria for each feature firstly; then it decides whether to remove the feature from feature subset according to SBS algorithm. In this way, the better feature subset can be obtained. The experiment results show that the proposed hybrid feature selection algorithm has good classification performance for liver tissue pathological image.

Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis

We propose computer-aided detection (CADe) algorithm for microcalcification (MC) clusters in reconstructed digital breast tomosynthesis (DBT) images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP) reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D) objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR) enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.

Influences of Radiology Trainees on Screening Mammography Interpretation

PURPOSE

Participation of radiology trainees in screening mammographic interpretation is a critical component of radiology residency and fellowship training. The aim of this study was to investigate and quantify the effects of trainee involvement on screening mammographic interpretation and diagnostic outcomes.

METHODS

Screening mammograms interpreted at an academic medical center by six dedicated breast imagers over a three-year period were identified, with cases interpreted by an attending radiologist alone or in conjunction with a trainee. Trainees included radiology residents, breast imaging fellows, and fellows from other radiology subspecialties during breast imaging rotations. Trainee participation, patient variables, results of diagnostic evaluations, and pathology were recorded.

RESULTS

A total of 47,914 mammograms from 34,867 patients were included, with an overall recall rate for attending radiologists reading alone of 14.7% compared with 18.0% when involving a trainee (P < .0001). Overall cancer detection rate for attending radiologists reading alone was 5.7 per 1,000 compared with 5.2 per 1,000 when reading with a trainee (P = .517). When reading with a trainee, dense breasts represented a greater portion of recalls (P = .0001), and more frequently, greater than one abnormality was described in the breast (P = .013). Detection of ductal carcinoma in situ versus invasive carcinoma or invasive cancer type was not significantly different. The mean size of cancers in patients recalled by attending radiologists alone was smaller, and nodal involvement was less frequent, though not statistically significantly.

CONCLUSIONS

These results demonstrate a significant overall increase in recall rate when interpreting screening mammograms with radiology trainees, with no change in cancer detection rate. Radiology faculty members should be aware of this potentiality and mitigate tendencies toward greater false positives.

Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection

IMPORTANCE

After the US Food and Drug Administration (FDA) approved computer-aided detection (CAD) for mammography in 1998, and the Centers for Medicare and Medicaid Services (CMS) provided increased payment in 2002, CAD technology disseminated rapidly. Despite sparse evidence that CAD improves accuracy of mammographic interpretations and costs over $400 million a year, CAD is currently used for most screening mammograms in the United States.

OBJECTIVE

To measure performance of digital screening mammography with and without CAD in US community practice.

DESIGN, SETTING, AND PARTICIPANTS

We compared the accuracy of digital screening mammography interpreted with (n = 495 818) vs without (n = 129 807) CAD from 2003 through 2009 in 323 973 women. Mammograms were interpreted by 271 radiologists from 66 facilities in the Breast Cancer Surveillance Consortium. Linkage with tumor registries identified 3159 breast cancers in 323 973 women within 1 year of the screening.

MAIN OUTCOMES AND MEASURES

Mammography performance (sensitivity, specificity, and screen-detected and interval cancers per 1000 women) was modeled using logistic regression with radiologist-specific random effects to account for correlation among examinations interpreted by the same radiologist, adjusting for patient age, race/ethnicity, time since prior mammogram, examination year, and registry. Conditional logistic regression was used to compare performance among 107 radiologists who interpreted mammograms both with and without CAD.

RESULTS

Screening performance was not improved with CAD on any metric assessed. Mammography sensitivity was 85.3% (95% CI, 83.6%-86.9%) with and 87.3% (95% CI, 84.5%-89.7%) without CAD. Specificity was 91.6% (95% CI, 91.0%-92.2%) with and 91.4% (95% CI, 90.6%-92.0%) without CAD. There was no difference in cancer detection rate (4.1 in 1000 women screened with and without CAD). Computer-aided detection did not improve intraradiologist performance. Sensitivity was significantly decreased for mammograms interpreted with vs without CAD in the subset of radiologists who interpreted both with and without CAD (odds ratio, 0.53; 95% CI, 0.29-0.97).

CONCLUSIONS AND RELEVANCE

Computer-aided detection does not improve diagnostic accuracy of mammography. These results suggest that insurers pay more for CAD with no established benefit to women.

Automated breast image classification using features from its discrete cosine transform

Purpose

This work aimed to improve breast screening program accuracy using automated classification. The goal was to determine if whole image features represented in the discrete cosine transform would provide a basis for classification. Priority was placed on avoiding false negative findings.

Methods

Online datasets were used for this work. No informed consent was required. Programs were developed in Mathematica and, where necessary to improve computational performance ported to C++. The use of a discrete cosine transform to separate normal from cancerous breast tissue was tested. Features (moments of the mean) were calculated in square sections of the transform centered on the origin. K-nearest neighbor and naive Bayesian classifiers were tested.

Results

Forty-one features were generated and tested singly, and in combination of two or three. Using a k-nearest neighbor classifier, sensitivities as high as 98% with a specificity of 66% were achieved. With a naive Bayesian classifier, sensitivities as high as 100% were achieved with a specificity of 64%.

Conclusion

Whole image classification based on discrete cosine transform (DCT) features was effectively implemented with a high level of sensitivity and specificity achieved. The high sensitivity attained using the DCT generated feature set implied that these classifiers could be used in series with other methods to increase specificity. Using a classifier with near 100% sensitivity, such as the one developed in this project, before applying a second classifier could only boost the accuracy of that classifier.

Investigation of computer-aided diagnosis system for bone scans: a retrospective analysis in 406 patients

OBJECTIVE

The aim of this study was to investigate the diagnostic ability of a completely automated computer-assisted diagnosis (CAD) system to detect metastases in bone scans by two patterns: one was per region, and the other was per patient.

MATERIALS AND METHODS

This study included 406 patients with suspected metastatic bone tumors who underwent whole-body bone scans that were analyzed by the automated CAD system. The patients were divided into four groups: a group with prostatic cancer (N = 71), breast cancer (N = 109), males with other cancers (N = 153), and females with other cancers (N = 73). We investigated the bone scan index and artificial neural network (ANN), which are parameters that can be used to classify bone scans to determine whether there are metastases. The sensitivities, specificities, positive predictive value (PPV), negative predictive value (NPV), and accuracies for the four groups were compared. Receiver operating characteristic (ROC) analyses of region-based ANN were performed to compare the diagnostic performance of the automated CAD system.

RESULTS

There were no significant differences in the sensitivity, specificity, or NPV between the four groups. The PPVs of the group with prostatic cancer (51.0 %) were significantly higher than those of the other groups (P < 0.01). The accuracy of the group with prostatic cancer (81.5 %) was significantly higher than that of the group with breast cancer (68.6 %) and the females with other cancers (65.9 %) (P < 0.01). For the evaluation of the ROC analysis of region-based ANN, the highest Az values for the groups with prostatic cancer, breast cancer, males with other cancers, and females with other cancers were 0.82 (ANN = 0.4, 0.5, 0.6, 0.7, and 0.8), 0.83 (ANN = 0.7), 0.81 (ANN = 0.5), and 0.81 (ANN = 0.6), respectively.

CONCLUSION

The special CAD system "BONENAVI" trained with a Japanese database appears to have significant potential in assisting physicians in their clinical routine. However, an improved CAD system depending on the primary lesion of the cancer is required to decrease the proportion of false-positive findings.

Short-term outcomes of screening mammography using computer-aided detection: a population-based study of medicare enrollees

BACKGROUND

Computer-aided detection (CAD) has rapidly diffused into screening mammography practice despite limited and conflicting data on its clinical effect.

OBJECTIVE

To determine associations between CAD use during screening mammography and the incidence of ductal carcinoma in situ (DCIS) and invasive breast cancer, invasive cancer stage, and diagnostic testing.

DESIGN

Retrospective cohort study.

SETTING

Medicare program.

PARTICIPANTS

Women aged 67 to 89 years having screening mammography between 2001 and 2006 in U.S. SEER (Surveillance, Epidemiology and End Results) regions (409 459 mammograms from 163 099 women).

MEASUREMENTS

Incident DCIS and invasive breast cancer within 1 year after mammography, invasive cancer stage, and diagnostic testing within 90 days after screening among women without breast cancer.

RESULTS

From 2001 to 2006, CAD prevalence increased from 3.6% to 60.5%. Use of CAD was associated with greater DCIS incidence (adjusted odds ratio [OR], 1.17 [95% CI, 1.11 to 1.23]) but no difference in invasive breast cancer incidence (adjusted OR, 1.00 [CI, 0.97 to 1.03]). Among women with invasive cancer, CAD was associated with greater likelihood of stage I to II versus III to IV cancer (adjusted OR, 1.27 [CI, 1.14 to 1.41]). In women without breast cancer, CAD was associated with increased odds of diagnostic mammography (adjusted OR, 1.28 [CI, 1.27 to 1.29]), breast ultrasonography (adjusted OR, 1.07 [CI, 1.06 to 1.09]), and breast biopsy (adjusted OR, 1.10 [CI, 1.08 to 1.12]).

LIMITATION

Short follow-up for cancer stage, potential unmeasured confounding, and uncertain generalizability to younger women.

CONCLUSION

Use of CAD during screening mammography among Medicare enrollees is associated with increased DCIS incidence, the diagnosis of invasive breast cancer at earlier stages, and increased diagnostic testing among women without breast cancer.

PRIMARY FUNDING SOURCE

Center for Healthcare Policy and Research, University of California, Davis.

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.

An engineering view on megatrends in radiology: digitization to quantitative tools of medicine

Within six months of the discovery of X-ray in 1895, the technology was used to scan the interior of the human body, paving the way for many innovations in the field of medicine, including an ultrasound device in 1950, a CT scanner in 1972, and MRI in 1980. More recent decades have witnessed developments such as digital imaging using a picture archiving and communication system, computer-aided detection/diagnosis, organ-specific workstations, and molecular, functional, and quantitative imaging. One of the latest technical breakthrough in the field of radiology has been imaging genomics and robotic interventions for biopsy and theragnosis. This review provides an engineering perspective on these developments and several other megatrends in radiology.

Usefulness of presentation of similar images in the diagnosis of breast masses on mammograms: comparison of observer performances in Japan and the USA

Computer-aided diagnosis has potential in improving radiologists' diagnosis, and presentation of similar images as a reference may provide additional useful information for distinction between benign and malignant lesions. In this study, we evaluated the usefulness of presentation of reference images in observer performance studies and compared the results obtained by groups of observers practicing in the United States and Japan. The results showed that the presentation of the reference images was generally effective for both groups, as the areas under the receiver operating characteristic curves improved from 0.915 to 0.924 for the group in the US and from 0.913 to 0.925 for the group in Japan, although the differences were marginally (p = 0.047) and not (p = 0.13) statistically significant, respectively. There was a slight difference between the two groups in the way that the observers reacted to some benign cases, which might be due to differences in the population of screenees and in the socio-clinical environment. In the future, it may be worthwhile to investigate the development of a customized system for physicians in different socio-clinical environments.

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.

Prediction of survival benefit using an automated bone scan index in patients with castration-resistant prostate cancer

What's known on the subject? and What does the study add? A bone scan index (BSI) can quantify the extent of bone involvement and response to treatment, but it has not been widely accepted, because of its time-consuming nature. The study is the first to demonstrate that automated BSI calculated with a computer-assisted diagnosis system is effective in judging the chemotherapeutic response of bone metastatic lesions in patients with castration-resistant prostate cancer.

OBJECTIVE

• To evaluate the value of an automated bone scan index (aBSI), calculated using a computer-assisted diagnosis system, to indicate chemotherapy response and to predict prognosis in patients with castration-resistant prostate cancer (CRPC) with bone metastasis.

PATIENTS AND METHODS

• Forty-two consecutive CRPC patients underwent taxane-based chemotherapy between November 2004 and March 2011 at our institution. • The aBSIs were retrospectively calculated at the diagnosis of CRPC and 16 weeks after starting chemotherapy. • Cox proportional hazards regression models were applied to multivariate analyses with and without aBSI response in addition to the basic model. • Based on the difference in the concordance index (c-index) between each model, the prognostic relevance of adding the aBSI response was determined.

RESULTS

• A decrease in aBSI was found in 28 patients (66.7%), whereas a response was shown by bone scan in only 23.8% of patients. • Patients with a reduction in aBSI had longer overall survival (OS) in comparison with the other patients (P= 0.0157). • Multivariate analysis without aBSI response showed that performance status (P= 0.0182) and PSA response (P= 0.0375) were significant prognosticators. • By adding the aBSI response to this basic model, the prognostic relevance of the model was improved with an increase in the c-index from 0.621 to 0.660.

CONCLUSIONS

• The aBSI reflected the chemotherapy response in bone metastasis. • The index detected small changes of bone metastasis response as quantified values and was a strong prognostic indicator for patients with CRPC.

Breast cancer: missed interval and screening-detected cancer at full-field digital mammography and screen-film mammography-- results from a retrospective review

PURPOSE

To compare the percentages and mammographic features of cancers missed at full-field digital mammography (FFDM) and screen-film mammography (SFM) in women who participated in the Norwegian Breast Cancer Screening Program in 2002-2008.

MATERIALS AND METHODS

Social Science Data Services approval was obtained; the requirement for informed consent was waived. Cases were all the interval and screening-detected cancers from 35&emsp14;127 FFDM and 52&emsp14;444 SFM examinations in two Norwegian counties. Prior and diagnostic FFDM examinations of 49 interval and 86 screening-detected breast cancers were reviewed by four breast radiologists and compared with a review of SFM examinations of 81 interval and 123 screening-detected cancers. Cancers were classified as missed or true, mammographic features were described, percentages were compared by using the χ(2) or Fisher exact test, and 95% confidence intervals (CIs) were calculated.

RESULTS

The percentages of interval and screening-detected cancers missed at FFDM and SFM did not differ significantly. (interval cancers missed: 33% [16 of 49] at FFDM vs 30% [24 of 81] at SFM [P = .868]; screening-detected cancers missed: 20% [17 of 86] at FFDM vs 21% [26 of 123] at SFM [P = .946]). Asymmetry was present in 27% (95% CI: 13.3%, 45.5%) of prior mammograms of cancers missed at FFDM and 10% (95% CI: 3.3%, 21.8%) of those missed at SFM (P = .070). Calcifications were observed in 18% (95% CI: 7.0%, 35.5%) of the cancers missed at FFDM and 34% (95% CI: 21.2%, 48.8%) of those missed at SFM (P = .185). Average mammographic tumor size of missed cancers manifesting as masses was 10.4 mm at FFDM and 13.6 mm at SFM (P = .036).

CONCLUSION

The use of FFDM has not reduced the challenge of missed cancers. Cancers missed at FFDM tend to have different mammographic features than those missed at SFM.

Computer-aided detection system performance on current and previous digital mammograms in patients with contralateral metachronous breast cancer

BACKGROUND

The computer-aided detection (CAD) system is widely used for screening mammography. The performance of the CAD system for contralateral breast cancer has not been reported for women with a history of breast cancer.

PURPOSE

To retrospectively evaluate the performance of a CAD system on current and previous mammograms in patients with contralateral metachronous breast cancer.

MATERIAL AND METHODS

During a 3-year period, 4945 postoperative patients had follow-up examinations, from whom we selected 55 women with contralateral breast cancers. Among them, 38 had visible malignant signs on the current mammograms. We analyzed the sensitivity and false-positive marks of the system on the current and previous mammograms according to lesion type and breast density.

RESULTS

The total visible lesion components on the current mammograms included 27 masses and 14 calcifications in 38 patients. The case-based sensitivity for all lesion types was 63.2% (24/38) with false-positive marks of 0.71 per patient. The lesion-based sensitivity for masses and calcifications was 59.3% (16/27) and 71.4% (10/14), respectively. The lesion-based sensitivity for masses in fatty and dense breasts was 68.8% (11/16) and 45.5% (5/11), respectively. The lesion-based sensitivity for calcifications in fatty and dense breasts was 100.0% (3/3) and 63.6% (7/11), respectively. The total visible lesion components on the previous mammograms included 13 masses and three calcifications in 16 patients, and the sensitivity for all lesion types was 31.3% (5/16) with false-positive marks of 0.81 per patient. On these mammograms, the sensitivity for masses and calcifications was 30.8% (4/13) and 33.3% (1/3), respectively. The sensitivity in fatty and dense breasts was 28.6% (2/7) and 33.3% (3/9), respectively.

CONCLUSION

In the women with a history of breast cancer, the sensitivity of the CAD system in visible contralateral breast cancer was lower than in most previous reports using the same CAD system probably due to the relatively small size, subtlety of the lesion findings, and dense parenchymal pattern.

Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: a 3D approach

PURPOSE

To design a computer-aided detection (CADe) system for clustered microcalcifications in reconstructed digital breast tomosynthesis (DBT) volumes and to perform a preliminary evaluation of the CADe system.

METHODS

IRB approval and informed consent were obtained in this study. A data set of two-view DBT of 72 breasts containing microcalcification clusters was collected from 72 subjects who were scheduled to undergo breast biopsy. Based on tissue sampling results, 17 cases had breast cancer and 55 were benign. A separate data set of two-view DBT of 38 breasts free of clustered microcalcifications from 38 subjects was collected to independently estimate the number of false-positives (FPs) generated by the CADe system. A radiologist experienced in breast imaging marked the biopsied cluster of microcalcifications with a 3D bounding box using all available clinical and imaging information. A CADe system was designed to detect microcalcification clusters in the reconstructed volume. The system consisted of prescreening, clustering, and false-positive reduction stages. In the prescreening stage, the conspicuity of microcalcification-like objects was increased by an enhancement-modulated 3D calcification response function. An iterative thresholding and 3D object growing method was used to detect cluster seed objects, which were used as potential centers of microcalcification clusters. In the cluster detection stage, microcalcification candidates were identified using a second iterative thresholding procedure, which was applied to the signal-to-noise ratio (SNR) enhanced image voxels with a positive calcification response. Starting with each cluster seed object as the initial cluster center, a dynamic clustering algorithm formed a cluster candidate by including microcalcification candidates within a 3D neighborhood of the cluster seed object that satisfied the clustering criteria. The number, size, and SNR of the microcalcifications in a cluster candidate and the cluster shape were used to reduce the number of FPs.

RESULTS

The prescreening stage detected a cluster seed object in 94% of the biopsied microcalcification clusters at a threshold of 100 cluster seed objects per DBT volume. After clustering, the detection sensitivity was 90% at 15 marks per DBT volume. After FP reduction, at 85% sensitivity, the average number of FPs estimated using the data set containing microcalcification clusters was 3.8 per DBT volume, and that estimated using the data set free of microcalcification clusters was 3.4. The detection performance for malignant microcalcification clusters was superior to that for benign clusters.

CONCLUSIONS

Our study indicates the feasibility of the 3D approach to the detection of clustered microcalcifications in DBT and that the newly designed enhancement-modulated 3D calcification response function is promising for prescreening. Further work is needed to assess the generalizability of our approach and to improve its performance.

Clinically missed cancer: how effectively can radiologists use computer-aided detection?

OBJECTIVE

The purpose of this study was to determine the effectiveness with which radiologists can use computer-aided detection (CADe) to detect cancer missed at screening.

MATERIALS AND METHODS

An observer study was performed to measure the ability of radiologists to detect breast cancer on mammograms with and without CADe. The images in the study were from 300 analog mammographic examinations. In 234 cases the mammograms were read clinically as normal and free of cancer for at least 2 subsequent years. In the other 66 cases, cancers were missed clinically. In 256 cases, current and previous mammograms were available. Eight radiologists read the dataset and recorded a BI-RADS assessment, the location of the lesion, and their level of confidence that the patient should be recalled for diagnostic workup for each suspicious lesion. Jackknife alternative free-response receiver operating characteristic analysis was used.

RESULTS

The jackknife alternative free-response receiver operating characteristic figure of merit was 0.641 without aid and 0.659 with aid (p = 0.06; 95% CI, -0.001 to 0.036). The sensitivity increased 9.9% (95% CI, 3.4-19%) and the callback rate 12.1% (95% CI, 7.3-20%) with CADe. Both increases were statistically significant (p < 0.001). Radiologists on average ignored 71% of correct computer prompts.

CONCLUSION

Use of CADe can increase radiologist sensitivity 10% with a comparable increase in recall rate. There is potential for CADe to have a bigger clinical impact because radiologists failed to recognize a correct computer prompt in 71% of missed cancer cases [corrected].

CAD e mamografia: por que usar esta ferramenta?

A mamografia representa o melhor método de detecção precoce do câncer de mama, porém cerca de 10% a 30% das lesões mamárias são perdidas no rastreamento, devido a limitações próprias dos observadores humanos. A detecção auxiliada por computador (computer-aided detection - CAD) é uma tecnologia relativamente nova que tem sido implementada em alguns serviços de mamografia, com o intuito de prover uma dupla leitura. Estudos clínicos têm demonstrado que o CAD aumenta a sensibilidade de detecção do câncer da mama, por radiologistas, em até 21%. Um sistema CAD é útil em situações em que exista alta variabilidade interobservador, falta de observadores treinados, ou na impossibilidade de se realizar a dupla leitura com dois ou mais radiologistas. O objetivo desta revisão está baseado na necessidade de atualizar a comunidade médica acerca desta ferramenta, como um método auxiliar, quantitativo, não operador-dependente, e que visa a melhorar a qualidade do diagnóstico do câncer de mama.

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.

Evaluation of computer-aided detection of lesions in mammograms obtained with a digital phase-contrast mammography system

A computer-aided detection (CAD) system was evaluated for its ability to detect microcalcifications and masses on images obtained with a digital phase-contrast mammography (PCM) system, a system characterised by the sharp images provided by phase contrast and by the high resolution of 25-μm-pixel mammograms. Fifty abnormal and 50 normal mammograms were collected from about 3,500 mammograms and printed on film for reading on a light box. Seven qualified radiologists participated in an observer study based on receiver operating characteristic (ROC) analysis. The average of the areas under ROC curve (AUC) values for the ROC analysis with and without CAD were 0.927 and 0.897 respectively (P = 0.015). The AUC values improved from 0.840 to 0.888 for microcalcifications (P = 0.034) and from 0.947 to 0.962 for masses (P = 0.025) respectively. The application of CAD to the PCM system is a promising approach for the detection of breast cancer in its early stages.

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.

How widely is computer-aided detection used in screening and diagnostic mammography?

PURPOSE

The aim of this study was to determine how widely computer-aided detection (CAD) is used in screening and diagnostic mammography and to see if there are differences between hospital facilities and private offices.

METHODS

The nationwide Medicare Part B fee-for-service databases for 2004 to 2008 were used. The Current Procedural Terminology(®) codes for screening and diagnostic mammography (both digital and screen film) and the CAD add-on codes were selected. Procedure volume was compared for screening vs diagnostic mammography and for hospital facilities vs private offices.

RESULTS

From 2004 to 2008, Medicare screening mammography volume increased slightly from 5,728,419 to 5,827,326 (+2%), but the use of screening CAD increased from 2,257,434 to 4,305,595 (+91%). By 2008, CAD was used in 74% of all screening mammographic studies. During this same time period, the Medicare volume of diagnostic mammography declined slightly from 1,835,700 to 1,682,026 (-8%), but the use of diagnostic CAD increased from 360,483 to 845,461 (+135%). By 2008, CAD was used in 50% of all diagnostic mammographic studies. In hospital facilities in 2008, CAD was used in 70% of all screening mammographic studies, compared with 81% in private offices. For diagnostic mammography in 2008, CAD was used in 48% in hospitals, compared with 55% in private offices.

CONCLUSION

Despite some operational drawbacks to using CAD, radiologists have embraced it in an effort to improve cancer detection. Its use has grown rapidly, and in 2008, it was used in three-quarters of all screening mammographic studies and half of all diagnostic mammographic studies. Women undergoing either screening or diagnostic mammography are more likely to receive CAD if they go to a private office than if they go to a hospital facility, although the differences are not great.

Features of prospectively overlooked computer-aided detection marks on prior screening digital mammograms in women with breast cancer

OBJECTIVE

The purpose of this article is to describe the features of prospectively overlooked computer-aided detection (CAD) marks on prior screening digital mammograms for women with breast cancer.

SUBJECTS AND METHODS

A CAD system embedded in a digital mammography system was prospectively applied to 50,100 screening mammograms between December 2003 and December 2006. Each mammogram was originally interpreted by one of five radiologists using the CAD information. Seventy-five mammogram pairs of prior negative screening mammograms and subsequent mammograms of developed cancers were collected. Visible findings and their actionability were determined by three blinded radiologists. All CAD marks, both true-positive and false-positive, and the number of marked views for the visible findings on prior mammograms were analyzed.

RESULTS

Of the 75 areas where cancer later developed, 61% (46/75) of mammograms had visible findings (21 masses, 17 microcalcifications, and eight masses with microcalcifications). Of these visible findings, 46% (21/46) were determined to be actionable, and 54% (25/46) were underthreshold. The CAD system had correctly depicted 74% (34/46) of the visible findings-52% (11/21) of masses, 94% (16/17) of microcalcifications, and 88% (7/8) of masses with microcalcifications. Actionable findings showed higher CAD sensitivity than did underthreshold findings (90% [19/21] vs 60% [15/25]; p = 0.04) and were more often marked on both views (58% [11/19] vs 27% [4/15]; p = 0.09). The average number of false-positive marks per case was 1.61.

CONCLUSION

On prior screening digital mammograms, the CAD system had correctly marked 74% (34/46) of visible findings and 90% (19/21) of actionable findings. The actionable findings showed significantly higher CAD sensitivity and were marked on both mammographic views more often than the underthreshold findings were.

Impact of a CAD system in a screen-film mammography screening program: a prospective study

OBJECTIVE

The purpose of our study was to perform a prospective assessment of the impact of a CAD system in a screen-film mammography screening program during a period of 3 years.

MATERIALS AND METHODS

Our study was carried out on a population of 21,855 asymptomatic women (45-65 years). Mammograms were processed in a CAD system and independently interpreted by one of six radiologists. We analyzed the following parameters: sensitivity of radiologist's interpretation (without and with CAD), detection increase, recall rate and positive predictive value of biopsy, CAD's marks, radiologist's false negatives and comparative analysis of carcinomas detected and non-detected by CAD.

RESULTS

Detection rate was 4.3‰. CAD supposed an increase of 0.1‰ in detection rate and 1% in the total number of cases (p<0.005). The impact on recall rate was not significant (0.4%) and PPV of percutaneous biopsy was unchanged by CAD (20.23%). CAD's marks were 2.7 per case and 0.7 per view. Radiologist's false negatives were 13 lesions which were initially considered as CAD's false positives.

CONCLUSIONS

CAD supposed a significant increase in detection, without modifications in recall rates and PPV of biopsy. However, better results could have been achieved if radiologists had considered actionable those cases marked by CAD but initially misinterpreted.

Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration

BACKGROUND

Discriminating malignant breast lesions from benign ones and accurately predicting the risk of breast cancer for individual patients are crucial to successful clinical decisions. In the past, several artificial neural network (ANN) models have been developed for breast cancer-risk prediction. All studies have reported discrimination performance, but not one has assessed calibration, which is an equivalently important measure for accurate risk prediction. In this study, the authors have evaluated whether an artificial neural network (ANN) trained on a large prospectively collected dataset of consecutive mammography findings can discriminate between benign and malignant disease and accurately predict the probability of breast cancer for individual patients.

METHODS

Our dataset consisted of 62,219 consecutively collected mammography findings matched with the Wisconsin State Cancer Reporting System. The authors built a 3-layer feedforward ANN with 1000 hidden-layer nodes. The authors trained and tested their ANN by using 10-fold cross-validation to predict the risk of breast cancer. The authors used area the under the receiver-operating characteristic curve (AUC), sensitivity, and specificity to evaluate discriminative performance of the radiologists and their ANN. The authors assessed the accuracy of risk prediction (ie, calibration) of their ANN by using the Hosmer-Lemeshow (H-L) goodness-of-fit test.

RESULTS

Their ANN demonstrated superior discrimination (AUC, 0.965) compared with the radiologists (AUC, 0.939; P<.001). The authors' ANN was also well calibrated as shown by an H-L goodness of fit P-value of .13.

CONCLUSIONS

The authors' ANN can effectively discriminate malignant abnormalities from benign ones and accurately predict the risk of breast cancer for individual abnormalities.

Computer-aided diagnostic models in breast cancer screening

Mammography is the most common modality for breast cancer detection and diagnosis and is often complemented by ultrasound and MRI. However, similarities between early signs of breast cancer and normal structures in these images make detection and diagnosis of breast cancer a difficult task. To aid physicians in detection and diagnosis, computer-aided detection and computer-aided diagnostic (CADx) models have been proposed. A large number of studies have been published for both computer-aided detection and CADx models in the last 20 years. The purpose of this article is to provide a comprehensive survey of the CADx models that have been proposed to aid in mammography, ultrasound and MRI interpretation. We summarize the noteworthy studies according to the screening modality they consider and describe the type of computer model, input data size, feature selection method, input feature type, reference standard and performance measures for each study. We also list the limitations of the existing CADx models and provide several possible future research directions.

What's the control in studies measuring the effect of computer-aided detection (CAD) on observer performance?

RATIONALE AND OBJECTIVES

The goal of many multiple-observer computer-aided detection (CADe) studies is to estimate the change in observers' diagnostic performance with CADe from their unaided performance. A key issue in these studies is the method for estimating the observers' unaided performance. The crossover design is considered the most valid. The sequential design takes less time and is less expensive but may be biased. We conducted a study to investigate the differences between these two designs.

MATERIALS AND METHODS

Data from two large CADe studies using both types of unaided reads were analyzed. The first study involved three radiologists examining the chest x-rays of 200 patients for lung nodules. The second study involved 19 observers interpreting the computed tomography colonography images of 100 patients for polyps. Observers' sensitivity, specificity, and receiver operating characteristic areas were estimated while unaided in both designs and compared to their accuracy with CADe. Bias, inter-observer variability, and correlations between unaided and aided results were assessed.

RESULTS

Observers tend to perform better while unaided in the sequential design than while unaided in the crossover design, but the differences are small. The inter-observer variability is larger in the sequential design. The correlations between unaided and aided results are larger in the sequential design. 95% CIs for the change with CADe are narrower with the sequential design.

CONCLUSION

The estimated effect of CADe on observer performance is similar regardless of the study design. Use of the sequential design may save investigators time and resources.

The brain MR Image segmentation techniques and use of diagnostic packages

RATIONALE AND OBJECTIVES

This article provides a survey of segmentation methods for medical images. Usually, classification of segmentation methods is done based on the approaches adopted and the domain of application.

MATERIALS AND METHODS

This survey is conducted on the recent segmentation methods used in biomedical image processing and explores the methods useful for better segmentation. A critical appraisal of the current status of semiautomated and automated methods is made for the segmentation of anatomical medical images emphasizing the advantages and disadvantages. Computer-aided diagnosis (CAD) used by radiologists as a second opinion has become one of the major research areas in medical imaging and diagnostic radiology. A picture archiving communication system (PACS) is an integrated workflow system for managing images and related data that is designed to streamline operations throughout the whole patient care delivery process.

RESULTS

By using PACS, the medical image interpretation may be changed from conventional hard-copy images to soft-copy studies viewed on the systems workstations.

CONCLUSION

The automatic segmentations assist the doctors in making quick diagnosis. The CAD need not be comparable to that of physicians, but is surely complementary.

"CADEAT": considerations on the use of CAD (computer-aided diagnosis) in mammography

Computer-aided diagnosis (CAD) has been extensively reported to increase sensitivity by about 10% when added to a single reading while increasing recall rate by 12%, and its current use can be safely recommended in clinical practice. CAD has been suggested as a possible alternative to conventional double reading in screening. Uncontrolled comparison is consistent and suggests that CAD is comparable to double reading in incremental cancer detection rate (CAD +10.6%, double reading +9.1%) and possibly better in recall rate (CAD +12.5%, double reading +28.8%). However, controlled studies comparing single reading + CAD to conventional double reading are not consistent and on average suggest a lower cancer detection rate (-5.1%) and a lower recall rate (-9.8%) for CAD. Scientific evidence is not sufficient for a safe recommendation of single reading + CAD as a current alternative to conventional double reading.

Invasive breast cancers detected by screening mammography: a detailed comparison of computer-aided detection-assisted single reading and double reading

To compare double reading plus arbitration for discordance, (currently best practice, (BP)) with computer-aided-detection (CAD)-assisted single reading (CAD-R) for detection of invasive cancers detected within BreastScreen Australia. Secondarily, to examine characteristics of cancers detected/rejected using each method. Mammograms of 157 randomly selected double-read invasive cancers were mixed 1:9 with normal cancers (total 1569), all detected in a BreastScreen service. Cancers were detected by two readers or one reader (C2 and C1 cancers, ratio 70:30%) in the program. The 1569 film-screen mammograms were read by two radiologists (reader A (RA) and reader B(RB)), with findings recorded before and after CAD. Discordant findings with BP were resolved by arbitration. We compared CAD-assisted reading (CAD-RA, CAD-RB) with BP, and CAD and arbitration contribution to findings. We correlated cancer size, sensitivity and mammographic density with detection methods. BP sensitivity 90.4% compared with CAD-RA sensitivity 86.6% (P = 0.12) and CAD-RB 94.3% (P = 0.14). CAD-RB specificity was less than BP (P = 0.01). CAD sensitivity was 93%, but readers rejected most positive CAD prompts. After CAD, reader's sensitivity increased 1.9% and specificity dropped 0.2% and 0.8%. Arbitration decreased specificity 4.7%. Receiving operator curves analysis demonstrated BP accuracy better than CAD-RA, borderline significance (P = 0.07), but not CAD-RB. Secondarily, cancer size was similar for BP and CAD-R. Cancers recalled after arbitration (P = 0.01) and CAD-R (P = 0.10) were smaller. No difference in cancer size or sensitivity between reading methods was found with increasing breast density. CAD-R and BP sensitivity and cancer detection size were not significantly different. CAD-R specificity was significantly lower for one reader.

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.