Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography

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.

Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network

Several computer aided diagnosis (CAD) systems have been developed for mammography. They are widely used in certain countries such as the U.S. where mammography studies are conducted more frequently; however, they are not yet globally employed for clinical use due to their inconsistent performance, which can be attributed to their reliance on hand-crafted features. It is difficult to use hand-crafted features for mammogram images that vary due to factors such as the breast density of patients and differences in imaging devices. To address these problems, several studies have leveraged a deep convolutional neural network that does not require hand-crafted features. Among the recent object detectors, RetinaNet is particularly promising as it is a simpler one-stage object detector that is fast and efficient while achieving state-of-the-art performance. RetinaNet has been proven to perform conventional object detection tasks but has not been tested on detecting masses in mammograms. Thus, we propose a mass detection model based on RetinaNet. To validate its performance in diverse use cases, we construct several experimental setups using the public dataset INbreast and the in-house dataset GURO. In addition to training and testing on the same dataset (i.e., training and testing on INbreast), we evaluate our mass detection model in setups using additional training data (i.e., training on INbreast + GURO and testing on INbreast). We also evaluate our model in setups using pre-trained weights (i.e., using weights pre-trained on GURO, training and testing on INbreast). In all the experiments, our mass detection model achieves comparable or better performance than more complex state-of-the-art models including the two-stage object detector. Also, the results show that using the weights pre-trained on datasets achieves similar performance as directly using datasets in the training phase. Therefore, we make our mass detection model's weights pre-trained on both GURO and INbreast publicly available. We expect that researchers who train RetinaNet on their in-house dataset for the mass detection task can use our pre-trained weights to leverage the features extracted from the datasets.

Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks

We investigate the addition of symmetry and temporal context information to a deep convolutional neural network (CNN) with the purpose of detecting malignant soft tissue lesions in mammography. We employ a simple linear mapping that takes the location of a mass candidate and maps it to either the contralateral or prior mammogram, and regions of interest (ROIs) are extracted around each location. Two different architectures are subsequently explored: (1) a fusion model employing two datastreams where both ROIs are fed to the network during training and testing and (2) a stagewise approach where a single ROI CNN is trained on the primary image and subsequently used as a feature extractor for both primary and contralateral or prior ROIs. A "shallow" gradient boosted tree classifier is then trained on the concatenation of these features and used to classify the joint representation. The baseline yielded an AUC of 0.87 with confidence interval [0.853, 0.893]. For the analysis of symmetrical differences, the first architecture where both primary and contralateral patches are presented during training obtained an AUC of 0.895 with confidence interval [0.877, 0.913], and the second architecture where a new classifier is retrained on the concatenation an AUC of 0.88 with confidence interval [0.859, 0.9]. We found a significant difference between the first architecture and the baseline at high specificity with [Formula: see text]. When using the same architectures to analyze temporal change, we yielded an AUC of 0.884 with confidence interval [0.865, 0.902] for the first architecture and an AUC of 0.879 with confidence interval [0.858, 0.898] in the second setting. Although improvements for temporal analysis were consistent, they were not found to be significant. The results show our proposed method is promising and we suspect performance can greatly be improved when more temporal data become available.

Large scale deep learning for computer aided detection of mammographic lesions

Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object detection and speech recognition. In this paper we provide a head-to-head comparison between a state-of-the art in mammography CAD system, relying on a manually designed feature set and a Convolutional Neural Network (CNN), aiming for a system that can ultimately read mammograms independently. Both systems are trained on a large data set of around 45,000 images and results show the CNN outperforms the traditional CAD system at low sensitivity and performs comparable at high sensitivity. We subsequently investigate to what extent features such as location and patient information and commonly used manual features can still complement the network and see improvements at high specificity over the CNN especially with location and context features, which contain information not available to the CNN. Additionally, a reader study was performed, where the network was compared to certified screening radiologists on a patch level and we found no significant difference between the network and the readers.

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Background

In the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models.

Objectives

To (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis.

Design

Two cohort studies assessing breast cancer incidence.

Setting

High-risk FHC and the NHSBSP Greater Manchester, UK.

Participants

A total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080].

Interventions

Questionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted.

Main outcome measures

Breast cancer incidence.

Data sources

The NHSBSP; cancer registration.

Results

A total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful inBRCA1(breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000;p < 0.001) of high-stage cancers in women with above-average breast cancer risks. It is not possible to provide estimates of the incremental costs and benefits of risk stratified screening because of lack of data inputs for key parameters in the model-based cost-effectiveness analysis.

Conclusions

Risk precision can be improved by using DNA and MD, and can potentially be used to stratify NHSBSP screening. It may also identify those at greater risk of high-stage cancers for enhanced screening. The cost-effectiveness of risk stratified screening is currently associated with extensive uncertainty. Additional research is needed to identify data needed for key inputs into model-based cost-effectiveness analyses to identify the impact on health-care resource use and patient benefits.

Future work

A pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required.

Funding

The National Institute for Health Research Programme Grants for Applied Research programme. The DNA saliva collection for SNP analysis for PROCAS was funded by the Genesis Breast Cancer Prevention Appeal.

False-positive reduction in mammography using multiscale spatial Weber law descriptor and support vector machines

In a CAD system for the detection of masses, segmentation of mammograms yields regions of interest (ROIs), which are not only true masses but also suspicious normal tissues that result in false positives. We introduce a new method for false-positive reduction in this paper. The key idea of our approach is to exploit the textural properties of mammograms and for texture description, to use Weber law descriptor (WLD), which outperforms state-of-the-art best texture descriptors. The basic WLD is a holistic descriptor by its construction because it integrates the local information content into a single histogram, which does not take into account the spatial locality of micropatterns. We extend it into a multiscale spatial WLD (MSWLD) that better characterizes the texture micro structures of masses by incorporating the spatial locality and scale of microstructures. The dimension of the feature space generated by MSWLD becomes high; it is reduced by selecting features based on their significance. Finally, support vector machines are employed to classify ROIs as true masses or normal parenchyma. The proposed approach is evaluated using 1024 ROIs taken from digital database for screening mammography and an accuracy of Az = 0.99 ± 0.003 (area under receiver operating characteristic curve) is obtained. A comparison reveals that the proposed method has significant improvement over the state-of-the-art best methods for false-positive reduction problem.

How to discriminate between computer-aided and computer-hindered decisions: a case study in mammography

BACKGROUND

Computer aids can affect decisions in complex ways, potentially even making them worse; common assessment methods may miss these effects. We developed a method for estimating the quality of decisions, as well as how computer aids affect it, and applied it to computer-aided detection (CAD) of cancer, reanalyzing data from a published study where 50 professionals ("readers") interpreted 180 mammograms, both with and without computer support.

METHOD

We used stepwise regression to estimate how CAD affected the probability of a reader making a correct screening decision on a patient with cancer (sensitivity), thereby taking into account the effects of the difficulty of the cancer (proportion of readers who missed it) and the reader's discriminating ability (Youden's determinant). Using regression estimates, we obtained thresholds for classifying a posteriori the cases (by difficulty) and the readers (by discriminating ability).

RESULTS

Use of CAD was associated with a 0.016 increase in sensitivity (95% confidence interval [CI], 0.003-0.028) for the 44 least discriminating radiologists for 45 relatively easy, mostly CAD-detected cancers. However, for the 6 most discriminating radiologists, with CAD, sensitivity decreased by 0.145 (95% CI, 0.034-0.257) for the 15 relatively difficult cancers.

CONCLUSIONS

Our exploratory analysis method reveals unexpected effects. It indicates that, despite the original study detecting no significant average effect, CAD helped the less discriminating readers but hindered the more discriminating readers. Such differential effects, although subtle, may be clinically significant and important for improving both computer algorithms and protocols for their use. They should be assessed when evaluating CAD and similar warning systems.

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.

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].

Is computer aided detection (CAD) cost effective in screening mammography? A model based on the CADET II study

BACKGROUND

Single reading with computer aided detection (CAD) is an alternative to double reading for detecting cancer in screening mammograms. The aim of this study is to investigate whether the use of a single reader with CAD is more cost-effective than double reading.

METHODS

Based on data from the CADET II study, the cost-effectiveness of single reading with CAD versus double reading was measured in terms of cost per cancer detected. Cost (Pound (£), year 2007/08) of single reading with CAD versus double reading was estimated assuming a health and social service perspective and a 7 year time horizon. As the equipment cost varies according to the unit size a separate analysis was conducted for high, average and low volume screening units. One-way sensitivity analyses were performed by varying the reading time, equipment and assessment cost, recall rate and reader qualification.

RESULTS

CAD is cost increasing for all sizes of screening unit. The introduction of CAD is cost-increasing compared to double reading because the cost of CAD equipment, staff training and the higher assessment cost associated with CAD are greater than the saving in reading costs. The introduction of single reading with CAD, in place of double reading, would produce an additional cost of £227 and £253 per 1,000 women screened in high and average volume units respectively. In low volume screening units, the high cost of purchasing the equipment will results in an additional cost of £590 per 1,000 women screened.One-way sensitivity analysis showed that the factors having the greatest effect on the cost-effectiveness of CAD with single reading compared with double reading were the reading time and the reader's professional qualification (radiologist versus advanced practitioner).

CONCLUSIONS

Without improvements in CAD effectiveness (e.g. a decrease in the recall rate) CAD is unlikely to be a cost effective alternative to double reading for mammography screening in UK. This study provides updated estimates of CAD costs in a full-field digital system and assessment cost for women who are re-called after initial screening. However, the model is highly sensitive to various parameters e.g. reading time, reader qualification, and equipment cost.

Using computer-aided detection in mammography as a decision support

Objective:

To evaluate an interactive computer-aided detection (CAD) system for reading mammograms to improve decision making.

Methods:

A dedicated mammographic workstation has been developed in which readers can probe image locations for the presence of CAD information. If present, CAD findings are displayed with the computed malignancy rating. A reader study was conducted in which four screening radiologists and five non-radiologists participated to study the effect of this system on detection performance. The participants read 120 cases of which 40 cases had a malignant mass that was missed at the original screening. The readers read each mammogram both with and without CAD in separate sessions. Each reader reported localized findings and assigned a malignancy score per finding. Mean sensitivity was computed in an interval of false-positive fractions less than 10%.

Results:

Mean sensitivity was 25.1% in the sessions without CAD and 34.8% in the CAD-assisted sessions. The increase in detection performance was significant (p = 0.012). Average reading time was 84.7 ± 61.5 s/case in the unaided sessions and was not significantly higher when interactive CAD was used (85.9 ± 57.8 s/case).

Conclusion:

Interactive use of CAD in mammography may be more effective than traditional CAD for improving mass detection without affecting reading time.

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.

Variable size computer-aided detection prompts and mammography film reader decisions

Introduction

The purpose of the present study was to investigate the effect of computer-aided detection (CAD) prompts on reader behaviour in a large sample of breast screening mammograms by analysing the relationship of the presence and size of prompts to the recall decision.

Methods

Local research ethics committee approval was obtained; informed consent was not required. Mammograms were obtained from women attending routine mammography at two breast screening centres in 1996. Films, previously double read, were re-read by a different reader using CAD. The study material included 315 cancer cases comprising all screen-detected cancer cases, all subsequent interval cancers and 861 normal cases randomly selected from 10,267 cases. Ground truth data were used to assess the efficacy of CAD prompting. Associations between prompt attributes and tumour features or reader recall decisions were assessed by chi-squared tests.

Results

There was a highly significant relationship between prompting and a decision to recall for cancer cases and for a random sample of normal cases (P < 0.001). Sixty-four per cent of all cases contained at least one CAD prompt. In cancer cases, larger prompts were more likely to be recalled (P = 0.02) for masses but there was no such association for calcifications (P = 0.9). In a random sample of 861 normal cases, larger prompts were more likely to be recalled (P = 0.02) for both mass and calcification prompts. Significant associations were observed with prompting and breast density (p = 0.009) for cancer cases but not for normal cases (P = 0.05).

Conclusions

For both normal cases and cancer cases, prompted mammograms were more likely to be recalled and the prompt size was also associated with a recall decision.

Cost-effectiveness of implementing automated grading within the national screening programme for diabetic retinopathy in Scotland

AIMS

National screening programmes for diabetic retinopathy using digital photography and multi-level manual grading systems are currently being implemented in the UK. Here, we assess the cost-effectiveness of replacing first level manual grading in the National Screening Programme in Scotland with an automated system developed to assess image quality and detect the presence of any retinopathy.

METHODS

A decision tree model was developed and populated using sensitivity/specificity and cost data based on a study of 6722 patients in the Grampian region. Costs to the NHS, and the number of appropriate screening outcomes and true referable cases detected in 1 year were assessed.

RESULTS

For the diabetic population of Scotland (approximately 160,000), with prevalence of referable retinopathy at 4% (6400 true cases), the automated strategy would be expected to identify 5560 cases (86.9%) and the manual strategy 5610 cases (87.7%). However, the automated system led to savings in grading and quality assurance costs to the NHS of 201,600 pounds per year. The additional cost per additional referable case detected (manual vs automated) totalled 4088 pounds and the additional cost per additional appropriate screening outcome (manual vs automated) was 1990 pounds.

CONCLUSIONS

Given that automated grading is less costly and of similar effectiveness, it is likely to be considered a cost-effective alternative to manual grading.

Performance of radiographers in mammogram interpretation: a systematic review

Radiologists may be relieved from work that could be performed by radiographers. This systematic literature review focuses on the performance of radiographers (also referring to technologists and physician assistants) compared with radiologists in the interpretation of mammograms; the effect of training; and the question whether there are any studies evaluating the effects of involving radiographers in the interpretation of diagnostic mammograms in daily clinical practice on the sensitivity and specificity of cancer detection in breast imaging. Six studies met the inclusion criteria (primary aim of the study has to be the evaluation of the performance of radiographers, sensitivity and specificity have to be reported or calculable and there has to be a sufficient gold standard). The results showed that, in a screening setting, radiographers scored higher false positive rates with a similar sensitivity in the detection of malignancies, compared with radiologists. Furthermore, results suggested that training could improve their performance. No studies were reported assessing the performance of radiographers interpreting diagnostic mammograms in a consecutive patient population in a daily clinical setting. This indicates a need for a well-designed diagnostic study using an adequate gold standard, in order to evaluate the feasibility of deploying radiographers in the interpretation of diagnostic mammograms in a clinical setting.

Current status and future directions of computer-aided diagnosis in mammography

The concept of computer-aided detection (CADe) was introduced more than 50 years ago; however, only in the last 20 years there have been serious and successful attempts at developing CADe for mammography. CADe schemes have high sensitivity, but poor specificity compared to radiologists. CADe has been shown to help radiologists find more cancers both in observer studies and in clinical evaluations. Clinically, CADe increases the number of cancers detected by approximately 10%, which is comparable to double reading by two radiologists.

Cost-effectiveness of screening with contrast enhanced magnetic resonance imaging vs X-ray mammography of women at a high familial risk of breast cancer

Contrast enhanced magnetic resonance imaging (CE MRI) is the most sensitive tool for screening women who are at high familial risk of breast cancer. Our aim in this study was to assess the cost-effectiveness of X-ray mammography (XRM), CE MRI or both strategies combined. In total, 649 women were enrolled in the MARIBS study and screened with both CE MRI and mammography resulting in 1881 screens and 1–7 individual annual screening events. Women aged 35–49 years at high risk of breast cancer, either because they have a strong family history of breast cancer or are tested carriers of a BRCA1, BRCA2 or TP53 mutation or are at a 50% risk of having inherited such a mutation, were recruited from 22 centres and offered annual MRI and XRM for between 2 and 7 years. Information on the number and type of further investigations was collected and specifically calculated unit costs were used to calculate the incremental cost per cancer detected. The numbers of cancer detected was 13 for mammography, 27 for CE MRI and 33 for mammography and CE MRI combined. In the subgroup of BRCA1 (BRCA2) mutation carriers or of women having a first degree relative with a mutation in BRCA1 (BRCA2) corresponding numbers were 3 (6), 12 (7) and 12 (11), respectively. For all women, the incremental cost per cancer detected with CE MRI and mammography combined was £28 284 compared to mammography. When only BRCA1 or the BRCA2 groups were considered, this cost would be reduced to £11 731 (CE MRI vs mammography) and £15 302 (CE MRI and mammography vs mammography). Results were most sensitive to the unit cost estimate for a CE MRI screening test. Contrast-enhanced MRI might be a cost-effective screening modality for women at high risk, particularly for the BRCA1 and BRCA2 subgroups. Further work is needed to assess the impact of screening on mortality and health-related quality of life.

CAD for mammography: the technique, results, current role and further developments

CAD systems, developed to assist the radiologist in the detection of suspicious lesions on mammograms, are currently controversially discussed. The highly sensitive detection of malignant structures including priors by CAD is linked with a low specific performance and a high rate of falsely positive markings. This causes controversial results regarding the effect of CAD systems for the diagnosing radiologist. This review aims to give an overview of the current literature, to state the currently discussed controversial results of CAD and to give an outlook on the next developments, which are not limited to senology, but include many other applications of CAD systems in radiology.