Minimally invasive breast cancer excision using the breast lesion excision system under ultrasound guidance.

PURPOSE: To assess the feasibility of completely excising small breast cancers using the automated, image-guided, single-pass radiofrequency-based breast lesion excision system (BLES) under ultrasound (US) guidance. METHODS: From February 2018 to July 2019, 22 patients diagnosed with invasive carcinomas ≤ 15 mm at US and mammography were enrolled in this prospective, multi-center, ethics board-approved study. Patients underwent breast MRI to verify lesion size. BLES-based excision and surgery were performed during the same procedure. Histopathology findings from the BLES procedure and surgery were compared, and total excision findings were assessed. RESULTS: Of the 22 patients, ten were excluded due to the lesion being > 15 mm and/or being multifocal at MRI, and one due to scheduling issues. The remaining 11 patients underwent BLES excision. Mean diameter of excised lesions at MRI was 11.8 mm (range 8.0-13.9 mm). BLES revealed ten (90.9%) invasive carcinomas of no special type, and one (9.1%) invasive lobular carcinoma. Histopathological results were identical for the needle biopsy, BLES, and surgical specimens for all lesions. None of the BLES excisions were adequate. Margins were usually compromised on both sides of the specimen, indicating that the excised volume was too small. Margin assessment was good for all BLES specimens. One technical complication occurred (retrieval of an empty BLES basket, specimen retrieved during subsequent surgery). CONCLUSIONS: BLES allows accurate diagnosis of small invasive breast carcinomas. However, BLES cannot be considered as a therapeutic device for small invasive breast carcinomas due to not achieving adequate excision.

The frequency of missed breast cancers in women participating in a high-risk MRI screening program.

PURPOSE: To evaluate the frequency of missed cancers on breast MRI in women participating in a high-risk screening program. METHODS: Patient files from women who participated in an increased risk mammography and MRI screening program (2003-2014) were coupled to the Dutch National Cancer Registry. For each cancer detected, we determined whether an MRI scan was available (0-24 months before cancer detection), which was reported to be negative. These negative MRI scans were in consensus re-evaluated by two dedicated breast radiologists, with knowledge of the cancer location. Cancers were scored as invisible, minimal sign, or visible. Additionally, BI-RADS scores, background parenchymal enhancement, and image quality (IQ; perfect, sufficient, bad) were determined. Results were stratified by detection mode (mammography, MRI, interval cancers, or cancers in prophylactic mastectomies) and patient characteristics (presence of BRCA mutation, age, menopausal state). RESULTS: Negative prior MRI scans were available for 131 breast cancers. Overall 31% of cancers were visible at the initially negative MRI scan and 34% of cancers showed a minimal sign. The presence of a BRCA mutation strongly reduced the likelihood of visible findings in the last negative MRI (19 vs. 46%, P < 0.001). Less than perfect IQ increased the likelihood of visible findings and minimal signs in the negative MRI (P = 0.021). CONCLUSION: This study shows that almost one-third of cancers detected in a high-risk screening program are already visible at the last negative MRI scan, and even more in women without BRCA mutations. Regular auditing and double reading for breast MRI screening is warranted.

The importance of early detection of calcifications associated with breast cancer in screening.

PURPOSE: The aim of this study was to assess how often women with undetected calcifications in prior screening mammograms are subsequently diagnosed with invasive cancer. METHODS: From a screening cohort of 63,895 women, exams were collected from 59,690 women without any abnormalities, 744 women with a screen-detected cancer and a prior negative exam, 781 women with a false positive exam based on calcifications, and 413 women with an interval cancer. A radiologist identified cancer-related calcifications, selected by a computer-aided detection system, on mammograms taken prior to screen-detected or interval cancer diagnoses. Using this ground truth and the pathology reports, the sensitivity for calcification detection and the proportion of lesions with visible calcifications that developed into invasive cancer were determined. RESULTS: The screening sensitivity for calcifications was 45.5%, at a specificity of 99.5%. A total of 68.4% (n = 177) of cancer-related calcifications that could have been detected earlier were associated with invasive cancer when diagnosed. CONCLUSIONS: Screening sensitivity for detection of malignant calcifications is low. Improving the detection of these early signs of cancer is important, because the majority of lesions with detectable calcifications that are not recalled immediately but detected as interval cancer or in the next screening round are invasive at the time of diagnosis.

Reasons for (non)participation in supplemental population-based MRI breast screening for women with extremely dense breasts.

AIM: To determine the willingness of women with extremely dense breasts to undergo breast cancer screening with magnetic resonance imaging (MRI) in a research setting, and to examine reasons for women to participate or not. MATERIALS AND METHODS: Between 2011 and 2015, 8,061 women (50-75 years) were invited for supplemental MRI as part of the Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial (ClinicalTrials.gov Identifier: NCT01315015), after a negative screening mammography in the national population-based mammography screening programme. Demographics of participants and non-participants were compared. All invitees were asked to report reasons for (non)participation. Ethical approval was obtained. Participants provided written informed consent. RESULTS: Of the 8,061 invitees, 66% answered that they were interested, and 59% eventually participated. Participants were on average 54-years old (interquartile range: 51-59 years), comparable to women with extremely dense breasts in the population-based screening programme (55 years). Women with higher socio-economic status (SES) were more often interested in participation than women with lower SES (68% versus 59%, p<0.001). The most frequently stated reasons for non-participation were "MRI-related inconveniences and/or self-reported contraindications to MRI" (27%) and "anxiety regarding the result of supplemental screening" (21%). "Expected personal health benefit" (68%) and "contribution to science" (43%) were the most frequent reasons for participation. CONCLUSION: Of women invited for MRI because of extremely dense breasts, 59% participated. Common reasons for non-participation were "MRI-related inconveniences" and "anxiety regarding the result of supplemental screening". In case of future implementation, availability of precise evidence on benefits and harms might reduce this anxiety.

Chest radiography: new technological developments and their applications.

Digital chest radiography is still the most common radiological examination. With the upcoming three-dimensional (3D) acquisition techniques the value of radiography seems to diminish. But because radiography is inexpensive, readily available, and requires very little dose, it is still being used for the first-line detection of many cardiothoracic diseases. In the last decades major technical developments of this 2D technique are being achieved. First, hardware developments of digital radiography have improved the contrast to noise, dose efficacy, throughput, and workflow. Dual energy acquisition techniques reduce anatomical noise by splitting a chest radiograph into a soft tissue image and a bone image. Second, advanced processing methods are developed to enable and improve detection of many kinds of disease. Digital bone subtraction by a software algorithm mimics the soft tissue image normally acquired with dedicated hardware. Temporal subtraction aims to rule out anatomical structures clotting the image, by subtracting a current radiograph with a previous radiograph. Finally, computer-aided detection systems help radiologists for the detection of various kinds of disease such as pulmonary nodules or tuberculosis.

Automated 3-dimensional segmentation of pelvic lymph nodes in magnetic resonance images.

PURPOSE: Computer aided diagnosis (CAD) of lymph node metastases may help reduce reading time and improve interpretation of the large amount of image data in a 3-D pelvic MRI exam. The purpose of this study was to develop an algorithm for automated segmentation of pelvic lymph nodes from a single seed point, as part of a CAD system for the classification of normal vs metastatic lymph nodes, and to evaluate its performance compared to other algorithms. METHODS: The authors' database consisted of pelvic MR images of 146 consecutive patients, acquired between January 2008 and April 2010. Each dataset included four different MR sequences, acquired after infusion of a lymph node specific contrast medium based on ultrasmall superparamagnetic particles of iron oxide. All data sets were analyzed by two expert readers who, reading in consensus, annotated and manually segmented the lymph nodes. The authors compared four segmentation algorithms: confidence connected region growing (CCRG), extended CCRG (ECC), graph cut segmentation (GCS), and a segmentation method based on a parametric shape and appearance model (PSAM). The methods were ranked based on spatial overlap with the manual segmentations, and based on diagnostic accuracy in a CAD system, with the experts' annotations as reference standard. RESULTS: A total of 2347 manually annotated lymph nodes were included in the analysis, of which 566 contained a metastasis. The mean spatial overlap (Dice similarity coefficient) was: 0.35 (CCRG), 0.57 (ECC), 0.44 (GCS), and 0.46 (PSAM). When combined with the classification system, the area under the ROC curve was: 0.805 (CCRG), 0.890 (ECC), 0.807 (GCS), 0.891 (PSAM), and 0.935 (manual segmentation). CONCLUSIONS: We identified two segmentation methods, ECC and PSAM, that achieve a high diagnostic accuracy when used in conjunction with a CAD system for classification of normal vs metastatic lymph nodes. The manual segmentations still achieve the highest diagnostic accuracy.

Less mammographic density after nasal versus oral administration of postmenopausal hormone therapy.

OBJECTIVE: Nasal administration gives a more acute but shorter rise in serum hormone levels than oral administration and may therefore have less effect on the fibroglandular tissue in the breasts. We studied the change in mammographic breast density after nasal vs. oral administration of postmenopausal hormone therapy (PHT). METHODS: We studied participants in a randomized, controlled trial on the impact of nasal vs. oral administration of PHT (combined 17ß-estradiol plus norethisterone) for 1 year. Two radiologists classified mammographic density at baseline and after 1 year into four categories. Also, the percentage density was calculated by a computer-based method. The main outcome measure was the difference in the proportion of women with an increase in mammographic density category after 1 year between the nasal and oral groups. Also, the change in the percentage density was calculated. RESULTS: The study group comprised 112 healthy postmenopausal women (mean age 56 years), of whom 53 received oral and 59 intranasal PHT. An increase in mammographic density category after 1 year was seen in 20% of the women in the nasal group and in 34% of the oral group. This resulted in a non-significant difference in the proportion of women in whom mammographic breast density had increased by 214% (95% confidence interval (CI) 230% to 2.7%). The mean change in percentage density was 21.2% in the nasal group and + 1.2% in the oral group, yielding a 22.4% differential effect (95% CI 27.3% to 2.5%). CONCLUSIONS: One year of nasal PHT gave a smaller, although not statistically significant, increase in mammographic density than oral PHT. Remaining issues are the relation between the route of administration of PHT and breast complaints and breast cancer risk.

Reliability of MRI tumor size measurements for minimal invasive treatment selection in small breast cancers.

INTRODUCTION: Due to the shift towards minimal invasive treatment, accurate tumor size estimation is essential for small breast cancers. The purpose of this study was to determine the reliability of MRI-based tumor size measurements with respect to clinical, histological and radiomics characteristics in small invasive or in situ carcinomas of the breast to select patients for minimal invasive therapy. MATERIALS AND METHODS: All consecutive cases of cT1 invasive breast carcinomas that underwent pre-operative MRI, treated in two hospitals between 2005 and 2016, were identified retrospectively from the Dutch cancer registry and cross-correlated with local databases. Concordance between MRI-based measurements and final pathological size was analyzed. The influence of clinical, histological and radiomics characteristics on the accuracy of MRI size measurements were analyzed. RESULTS: Analysis included 343 cT1 breast carcinomas in 336 patients (mean age, 55 years; range, 25-81 years). Overall correlation of MRI measurements with pathology was moderately strong (ρ = 0.530, P < 0.001), in 42 cases (12.2%) MRI underestimated the size with more than 5 mm. Underestimation occurs more often in grade 2 and grade 3 disease than in low grade invasive cancers. In DCIS the frequency of underestimation is higher than in invasive breast cancer. Unfortunately, none of the patient, imaging or biopsy characteristics appeared predictive for underestimation. CONCLUSION: Size measurements of small breast cancers on breast MRI are within 5 mm of pathological size in 88% of patients. Nevertheless, underestimation cannot be adequately predicted, particularly for grade 2 and grade 3 tumors, which may hinder patient selection for minimal invasive therapy.

Can a channelized Hotelling observer assess image quality in acquired mammographic images of an anthropomorphic breast phantom including image processing?

PURPOSE: To study the feasibility of a channelized Hotelling observer (CHO) to predict human observer performance in detecting calcification-like signals in mammography images of an anthropomorphic breast phantom, as part of a quality control (QC) framework. METHODS: A prototype anthropomorphic breast phantom with inserted gold disks of 0.25 mm diameter was imaged with two different digital mammography x-ray systems at four different dose levels. Regions of interest (ROIs) were extracted from the acquired processed and unprocessed images, signal-present and signal-absent. The ROIs were evaluated by a CHO using four different formulations of the difference of Gaussian (DoG) channel sets. Three human observers scored the ROIs in a two-alternative forced-choice experiment. We compared the human and the CHO performance on the simple task to detect calcification-like disks in ROIs with and without postprocessing. The proportion of correct responses of the human reader (PCH ) and the CHO (PCCHO ) was calculated and the correlation between the two was analyzed using a mixed-effect regression model. To address the signal location uncertainty, the impact of shifting the DoG channel sets in all directions up to two pixels was evaluated. Correlation results including the goodness of fit (r2 ) of PCH and PCCHO for all different parameters were evaluated. RESULTS: Subanalysis by system yielded strong correlations between PCH and PCCHO , with r2 between PCH and PCCHO was found to be between 0.926 and 0.958 for the unshifted and between 0.759 and 0.938 for the shifted channel sets, respectively. However, the linear fit suggested a slight system dependence. PCCHO with shifted channel sets increased CHO performance but the correlation with humans was decreased. These correlations were not considerably affected by of the DoG channel set used. CONCLUSIONS: There is potential for the CHO to be used in QC for the evaluation of detectability of calcification-like signals. The CHO can predict the PC of humans in images of calcification-like signals of two different systems. However, a global model to be used for all systems requires further investigation.

Differential impact of conventional and low-dose oral hormone therapy, tibolone and raloxifene on mammographic breast density, assessed by an automated quantitative method.

OBJECTIVE: To evaluate impact of different postmenopausal hormone therapy (HT) regimens and raloxifene on mammographic breast density. DESIGN: Open, randomised, comparative clinical trial. SETTING: Women were recruited through local newspapers and posters. They were examined at the Departments of Haematology, Gynaecology, and Radiology in a University Hospital. POPULATION: A total of 202 healthy postmenopausal women between the age of 45 and 65 years. METHODS: Women were randomly assigned to receive daily treatment for 12 weeks with tablets containing low-dose HT containing 1 mg 17 beta-estradiol + 0.5 mg norethisterone acetate (NETA) (n = 50), conventional-dose HT containing 2 mg 17 beta-estradiol and 1 mg NETA (n = 50), 2.5 mg tibolone (n = 51), or 60 mg raloxifene (n = 51). Mammographic density was determined at baseline and after 12 weeks by an automated technique in full-field digital mammograms. MAIN OUTCOME MEASURES: Mammographic density was expressed as volumetric breast density estimations. RESULTS: Mammographic breast density increased significantly and to a similar degree in both the conventional- and low-dose HT groups. A small reduction in mammographic breast density was seen in the raloxifene group, whereas those allocated to tibolone treatment only showed minor changes. CONCLUSIONS: Our findings demonstrated a significant difference in impact on mammographic breast density between the regimens. Although these results indicate a differential effect of these regimens on breast tissue, the relation to breast cancer risk remains unresolved.

Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection.

OBJECTIVE: To investigate the effect of dedicated Computer Aided Detection (CAD) software for automated breast ultrasound (ABUS) on the performance of radiologists screening for breast cancer. METHODS: 90 ABUS views of 90 patients were randomly selected from a multi-institutional archive of cases collected between 2010 and 2013. This dataset included normal cases (n=40) with >1year of follow up, benign (n=30) lesions that were either biopsied or remained stable, and malignant lesions (n=20). Six readers evaluated all cases with and without CAD in two sessions. CAD-software included conventional CAD-marks and an intelligent minimum intensity projection of the breast tissue. Readers reported using a likelihood-of-malignancy scale from 0 to 100. Alternative free-response ROC analysis was used to measure the performance. RESULTS: Without CAD, the average area-under-the-curve (AUC) of the readers was 0.77 and significantly improved with CAD to 0.84 (p=0.001). Sensitivity of all readers improved (range 5.2-10.6%) by using CAD but specificity decreased in four out of six readers (range 1.4-5.7%). No significant difference was observed in the AUC between experienced radiologists and residents both with and without CAD. CONCLUSIONS: Dedicated CAD-software for ABUS has the potential to improve the cancer detection rates of radiologists screening for breast cancer.

Use of border information in the classification of mammographic masses.

We are developing a new method to characterize the margin of a mammographic mass lesion to improve the classification of benign and malignant masses. Towards this goal, we designed features that measure the degree of sharpness and microlobulation of mass margins. We calculated these features in a border region of the mass defined as a thin band along the mass contour. The importance of these features in the classification of benign and malignant masses was studied in relation to existing features used for mammographic mass detection. Features were divided into three groups, each representing a different mass segment: the interior region of a mass, the border and the outer area. The interior and the outer area of a mass were characterized using contrast and spiculation measures. Classification was done in two steps. First, features representing each of the three mass segments were merged into a neural network classifier resulting in a single regional classification score for each segment. Secondly, a classifier combined the three single scores into a final output to discriminate between benign and malignant lesions. We compared the classification performance of each regional classifier and the combined classifier on a data set of 1076 biopsy proved masses (590 malignant and 486 benign) from 481 women included in the Digital Database for Screening Mammography. Receiver operating characteristic (ROC) analysis was used to evaluate the accuracy of the classifiers. The area under the ROC curve (A(z)) was 0.69 for the interior mass segment, 0.76 for the border segment and 0.75 for the outer mass segment. The performance of the combined classifier was 0.81 for image-based and 0.83 for case-based evaluation. These results show that the combination of information from different mass segments is an effective approach for computer-aided characterization of mammographic masses. An advantage of this approach is that it allows the assessment of the contribution of regions rather than individual features. Results suggest that the border and the outer areas contained the most valuable information for discrimination between benign and malignant masses.

The potential use of ultra-low radiation dose images in digital mammography--a clinical proof-of-concept study in craniocaudal views.

OBJECTIVE: To estimate the potential of low-dose images in digital mammography by analysing the effect of substantial dose reduction in craniocaudal (CC) views on clinical performance. METHODS: At routine mammography, additional CC views were obtained with about 10% of the standard dose. Five radiologists retrospectively read the standard [mediolateral oblique (MLO) + CC] and combination low-dose mammograms (standard MLO + low-dose CC). If present, lesion type, conspicuity and suggested work-up were recorded. Final diagnoses were made by histology or follow up. A t-test or χ(2) test was used to compare results. RESULTS: 421 cases were included, presenting 5 malignancies, 66 benign lesions and multiple non-specific radiologic features. Using MLO with low-dose CC, all lesions were detected by at least one reader, but altogether less often than with standard mammography (sensitivity, 73.9% vs 81.5%). Missed lesions concerned all types. Lesions detected with both protocols were described similarly (p = 0.084) with comparable work-up recommendations (p = 0.658). CONCLUSION: Mammography with ultra-low-dose CC images particularly influences detection. While sensitivity decreased, specificity was unaffected. In this proof-of-concept study a lower limit was to be determined that is not intended nor applicable for clinical practice. This should facilitate further research in optimization of a low-dose approach, which has potential in a relatively young and largely asymptomatic population. ADVANCES IN KNOWLEDGE: Tungsten/silver-acquired mammography images might facilitate substantial dose reduction. Ultra-low-dose CC images reduce sensitivity, but not specificity. Low-dose images have potential in a largely young and asymptomatic population; a baseline is set for further research in optimization of a low-dose approach.

Spatial resolution in digital mammography.

RATIONAL AND OBJECTIVES: Digital acquisition systems currently available limit spatial resolution in digital mammography to roughly 0.1 mm/pixel. The objective of this study is to determine if high-quality mammography is possible at this resolution. METHODS: The influence of spatial resolution on diagnostic quality was investigated by comparing observer performance on film to that on digitized film. A 0.1-mm sampling distance was used for digitization. Detection of mammographic details was studied by measuring threshold contrast as a function of detail size for small circular objects in the range of 0.12 to 2.5 mm. Characterization of microcalcifications was investigated in a receiver operating characteristic (ROC) study, in which 10 radiologists read 72 mammographic details with microcalcifications, both digitally and on film. RESULTS: Digitization improved the detectability of the larger, low contrast objects, whereas for small objects the detectability did not change. The authors found that even under the most optimal circumstances, isolated spherical calcifications with diameters smaller than 0.13 mm are not detectable with film-screen mammography, despite its resolution limit of 15 line patterns per mm (lp/mm). The ability to characterize microcalcification clusters did not change significantly with digitization. However, the results suggest that differentiation of benign from malignant cases decreases slightly, and that characterization of different types of malignancies somewhat improves by digitization. Mean differences between the two modalities were considerably smaller than the interobserver variability. CONCLUSION: A relatively low spatial resolution of 0.1 mm/pixel does not prohibit high-quality diagnostic performance in digital mammography.

Changes in mammographic breast density and concomitant changes in breast cancer risk.

Among participants of the biennial Nijmegen breast cancer screening programme, we examined whether diminution of mammographic breast density lowered breast cancer risk. Post-menopausal breast cancer cases (n = 108), who had to have participated in all the five screening rounds prior to their diagnosis, were matched to 400 controls on year of birth and screening history. Controls had to be free of breast cancer at the time of the case's diagnosis. Changes in breast density were measured over a 10-year period, by a fully computerized method. Women in whom 5-25% or >25% of the breast was composed of fibro-glandular density showed a threefold increased 10-year risk compared to women with <5% density. In women with 5-25% density initially, we observed a trend of decreasing risk with diminishing density: when women with <5% density throughout the whole period formed the reference category, the odds ratio (OR) for those who decreased from 5-25% to <5% density was 1.9 [95% confidence interval (CI) = 0.6-6.1] in contrast to the OR of 5.7 (95% CI = 2.2-15.2) for those with persisting 5-25% density. In women who increased from 5-25% density to >25% density the OR was 6.9 (95% CI = 2.1-22.9). In women with >25% density initially, diminishing density was not clearly associated with lowering risk, which may be partly explained by the low number of women who decreased to <5% (n = 12). Due to the limited size of the study these results have to be interpreted with caution. Although the results are not conclusive, they could indicate a trend of decreasing risk with diminishing breast density. Should this effect be real, it may have great implications for the primary prevention of breast cancer or for the identification of high-risk groups who would benefit by more frequent screening. Therefore, large-scale, long-term follow-up studies on the effects of changes in breast density are needed.

Accurate segmentation and contrast measurement of microcalcifications in mammograms: a phantom study.

The authors are developing a computer-aided diagnostic method to assist radiologists in differentiating between malignant and benign clustered microcalcifications in mammograms. In earlier studies we investigated shape and contrast features of microcalcifications for classification. It was found that segmentation strongly influences classification results. For this reason a phantom study has been carried out. The CDMAM phantom, consisting of a pattern of dots with known size and object contrast is used for evaluation of contrast measurement and segmentation. Dots in the range of 0.2-0.8 mm are taken as a model for microcalcifications. In this article performances of different methods for segmentation of microcalcifications are compared. An iterative method based on a Markov random field and a signal dependent criterion give satisfying results. The segmentation performances of both methods are comparable. Also the influence of the modulation transfer function on contrast estimates is determined and effect of exposure level on segmentation is analyzed.

Segmentation of suspicious densities in digital mammograms.

State-of-the-art algorithms for detection of masses in mammograms are very sensitive but they also detect many normal regions with slightly suspicious features. Based on segmentations of detected regions, shape and intensity features can be computed that discriminate between normal and abnormal regions. These features can be used to discard false positive detections and hence improve the specificity of the detection method. In this work two different methods to segment suspect regions were examined. A number of different implementations of a region growing method were compared to a discrete dynamic contour method. Both methods were applied to a consecutive data set of 132 mammograms containing masses and architectural distortions, taken from the Dutch screening program. Evaluation of the performance of the methods was done in two different ways. In the first experiment, the segmentations of masses were compared to annotations made by the radiologist. In the second experiment, a number of features were computed for all segmented areas, normal and abnormal, based on which regions were classified with a neural network. The most sophisticated region growing method and the method using the dynamic contour model had a similar performance when evaluation was based on the overlap of the annotations. The second experiment showed that the contours generated by the discrete dynamic contour model were more suited for computation of discriminating features. Contrast features were especially useful to improve the performance of the detection method.

Automated classification of clustered microcalcifications into malignant and benign types.

The objectives in this study were to design and test a fully automated method for classification of microcalcification clusters into malignant and benign types, and to compare the method's performance with that of radiologists. A novel aspect of the approach is that the relative location and orientation of clusters inside the breast was taken into account for feature calculation. Furthermore, correspondence of location of clusters in mediolateral oblique (MLO) and cranio-caudal (CC) views, was used in feature calculation and in final classification. Initially, microcalcifications were automatically detected by using a statistical method based on Bayesian techniques and a Markov random field model. To determine malignancy or benignancy of a cluster, a method based on two classification steps was developed. In the first step, classification of clusters was performed and in the second step a patient based classification was done. A total of 16 features was used in the study. To identify meaningful features, a feature selection was applied, using the area under the receiver operating characteristic (ROC) curve (Az value) as a criterion. For classification the k-nearest-neighbor method was used in a leave-one-patient-out procedure. A database of 192 mammograms with 280 true positive detected microcalcification clusters was used for evaluation of the method. The set consisted of cases that were selected for diagnostic work up during a 4 year period of screening in the Nijmegen region (The Netherlands). Because of the high positive predictive value in the screening program (50%), this set did not contain obvious benign cases. The method's best patient-based performance on this set corresponded with Az = 0.83, using nine features. A subset of the data set, containing mammograms from 90 patients, was used for comparing the computer results to radiologists' performance. Ten radiologists read these cases on a light-box and assessed the probability of malignancy for each patient. All participants had experience in clinical mammography and participated in our observer study during the last 2 days of a 2-week training session leading to screening mammography certification. Results on the subset showed that the method's performance (Az = 0.83) was considerably higher than that of the radiologists (Az = 0.63).

Detection of stellate distortions in mammograms.

Malignant densities in mammograms have an irregular appearance and frequently are surrounded by a radiating pattern of linear spicules. In this paper a method is described to detect such stellate patterns. This method is based on statistical analysis of a map of pixel orientations. If an increase of pixels pointing to a region is found, this region is marked as suspicious, especially if such an increase is found in many directions. Orientations of the image intensity map are determined at each pixel using a multiscale approach. At a given scale, accurate line-based orientation estimates are obtained from the output of three-directional, second-order, Gaussian derivative operators. The orientation at the scale at which these operators have maximum response is selected. If a line-like structure is present at a given site, this method provides an estimate of the orientation of this structure, whereas in other cases the image noise will generate a random orientation. The pixel orientation map is used to construct two operators which are sensitive to radial patterns of straight lines. Combination of the output of these operators using a classifier allows for detection of stellate patterns. Different classification methods have been compared and results obtained on a common database are presented. Around 90% of the malignant cases were detected at rate of one false positive (FP) per image.

Normalization of local contrast in mammograms.

Equalizing image noise has been shown to be an important step in automatic detection of microcalcifications in digital mammograms. In this study, an accurate adaptive approach for noise equalization is presented and investigated. No additional information obtained from phantom recordings is involved in the method, which makes the approach robust and independent of film type and film development characteristics. Furthermore, it is possible to apply the method on direct digital mammograms as well. In this study, the adaptive approach is optimized by investigating a number of alternative approaches to estimate the image noise. The estimation of high-frequency noise as a function of the grayscale is improved by a new technique for dividing the grayscale in sample intervals and by using a model for additive high-frequency noise. It is shown that the adaptive noise equalization gives substantially better detection results than does a fixed noise equalization. A large database of 245 digitized mammograms with 341 clusters was used for evaluation of the method.