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.

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.

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.

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.

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.

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.

New methods for using computer-aided detection information for the detection of lung nodules on chest radiographs.

OBJECTIVE: To investigate two new methods of using computer-aided detection (CAD) system information for the detection of lung nodules on chest radiographs. We evaluated an interactive CAD application and an independent combination of radiologists and CAD scores. METHODS: 300 posteroanterior and lateral digital chest radiographs were selected, including 111 with a solitary pulmonary nodule (average diameter, 16 mm). Both nodule and control cases were verified by CT. Six radiologists and six residents reviewed the chest radiographs without CAD and with CAD (ClearRead +Detect™ 5.2; Riverain Technologies, Miamisburg, OH) in two reading sessions. The CAD system was used in an interactive manner; CAD marks, accompanied by a score of suspicion, remained hidden unless the location was queried by the radiologist. Jackknife alternative free response receiver operating characteristics multireader multicase analysis was used to measure detection performance. Area under the curve (AUC) and partial AUC (pAUC) between a specificity of 80% and 100% served as the measure for detection performance. We also evaluated the results of a weighted combination of CAD scores and reader scores, at the location of reader findings. RESULTS: AUC for the observers without CAD was 0.824. No significant improvement was seen with interactive use of CAD (AUC = 0.834; p = 0.15). Independent combination significantly improved detection performance (AUC = 0.834; p = 0.006). pAUCs without and with interactive CAD were similar (0.128), but improved with independent combination (0.137). CONCLUSION: Interactive CAD did not improve reader performance for the detection of lung nodules on chest radiographs. Independent combination of reader and CAD scores improved the detection performance of lung nodules. ADVANCES IN KNOWLEDGE: (1) Interactive use of currently available CAD software did not improve the radiologists' detection performance of lung nodules on chest radiographs. (2) Independently combining the interpretations of the radiologist and the CAD system improved detection of lung nodules on chest radiographs.

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.

Learning from unbalanced data: a cascade-based approach for detecting clustered microcalcifications.

Finding abnormalities in diagnostic images is a difficult task even for expert radiologists because the normal tissue locations largely outnumber those with suspicious signs which may thus be missed or incorrectly interpreted. For the same reason the design of a Computer-Aided Detection (CADe) system is very complex because the large predominance of normal samples in the training data may hamper the ability of the classifier to recognize the abnormalities on the images. In this paper we present a novel approach for computer-aided detection which faces the class imbalance with a cascade of boosting classifiers where each node is trained by a learning algorithm based on ranking instead of classification error. Such approach is used to design a system (CasCADe) for the automated detection of clustered microcalcifications (µCs), which is a severely unbalanced classification problem because of the vast majority of image locations where no µC is present. The proposed approach was evaluated with a dataset of 1599 full-field digital mammograms from 560 cases and compared favorably with the Hologic R2CAD ImageChecker, one of the most widespread commercial CADe systems. In particular, at the same lesion sensitivity of R2CAD (90%) on biopsy proven malignant cases, CasCADe and R2CAD detected 0.13 and 0.21 false positives per image (FPpi), respectively (p-value=0.09), whereas at the same FPpi of R2CAD (0.21), CasCADe and R2CAD detected 93% and 90% of true lesions respectively (p-value=0.11) thus showing that CasCADe can compete with high-end CADe commercial systems.

Comparative evaluation of support vector machine classification for computer aided detection of breast masses in mammography.

False positive (FP) marks represent an obstacle for effective use of computer-aided detection (CADe) of breast masses in mammography. Typically, the problem can be approached either by developing more discriminative features or by employing different classifier designs. In this paper, the usage of support vector machine (SVM) classification for FP reduction in CADe is investigated, presenting a systematic quantitative evaluation against neural networks, k-nearest neighbor classification, linear discriminant analysis and random forests. A large database of 2516 film mammography examinations and 73 input features was used to train the classifiers and evaluate for their performance on correctly diagnosed exams as well as false negatives. Further, classifier robustness was investigated using varying training data and feature sets as input. The evaluation was based on the mean exam sensitivity in 0.05-1 FPs on normals on the free-response receiver operating characteristic curve (FROC), incorporated into a tenfold cross validation framework. It was found that SVM classification using a Gaussian kernel offered significantly increased detection performance (P = 0.0002) compared to the reference methods. Varying training data and input features, SVMs showed improved exploitation of large feature sets. It is concluded that with the SVM-based CADe a significant reduction of FPs is possible outperforming other state-of-the-art approaches for breast mass CADe.

Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis.

In this paper, a fully automatic computer-aided detection (CAD) method is proposed for the detection of prostate cancer. The CAD method consists of multiple sequential steps in order to detect locations that are suspicious for prostate cancer. In the initial stage, a voxel classification is performed using a Hessian-based blob detection algorithm at multiple scales on an apparent diffusion coefficient map. Next, a parametric multi-object segmentation method is applied and the resulting segmentation is used as a mask to restrict the candidate detection to the prostate. The remaining candidates are characterized by performing histogram analysis on multiparametric MR images. The resulting feature set is summarized into a malignancy likelihood by a supervised classifier in a two-stage classification approach. The detection performance for prostate cancer was tested on a screening population of 200 consecutive patients and evaluated using the free response operating characteristic methodology. The results show that the CAD method obtained sensitivities of 0.41, 0.65 and 0.74 at false positive (FP) levels of 1, 3 and 5 per patient, respectively. In conclusion, this study showed that it is feasible to automatically detect prostate cancer at a FP rate lower than systematic biopsy. The CAD method may assist the radiologist to detect prostate cancer locations and could potentially guide biopsy towards the most aggressive part of the tumour.

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.

A novel and automatic mammographic texture resemblance marker is an independent risk factor for breast cancer.

OBJECTIVE: We investigated whether breast cancer is predicted by a breast cancer risk mammographic texture resemblance (MTR) marker. METHODS: A previously published case-control study included 495 women of which 245 were diagnosed with breast cancer. In baseline mammograms, 2-4 years prior to diagnosis, the following mammographic parameters were analysed for relation to breast cancer risk: (C) categorical parenchymal pattern scores; (R) radiologist's percentage density, (P) computer-based percentage density; (H) computer-based breast cancer risk MTR marker; (E) computer-based hormone replacement treatment MTR marker; and (A) an aggregate of P and H. RESULTS: Density scores, C, R, and P correlated (tau=0.3-0.6); no other pair of scores showed large (tau>0.2) correlation. For the parameters, the odds ratios of future incidence of breast cancer comparing highest to lowest categories (146 and 106 subject respectively) were C: 2.4(1.4-4.2), R: 2.4(1.4-4.1), P: 2.5(1.5-4.2), E: non-significant, H: 4.2(2.4-7.2), and A: 5.6(3.2-9.8). The AUC analysis showed a similarly increasing pattern (C: 0.58±0.02, R: 0.57±0.03, P: 0.60±0.03, H: 0.63±0.02, A: 0.66±0.02). The AUC of the aggregate marker (A) surpasses others significantly except H. HRT-MTR (E) did not significantly identify future cancers or correlate with any other marker. CONCLUSIONS: Breast cancer risk MTR marker was independent of density scores and more predictive of risk. The hormone replacement treatment MTR marker did not identify patients at risk.

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.

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.

Computer aided detection of masses in mammograms as decision support.

Performance of a computer aided detection (CAD) system for masses in mammograms was investigated. Using data collected in an observer study, in which experienced screening radiologists read a series of 500 screening mammograms without CAD, performance of radiologists was compared to the standalone performance of the CAD system. Due to a larger number of FPs (false positives), the performance of CAD was lower than that of the readers. However, when analysis was restricted to mammographic regions identified by the radiologists, it was found that the CAD system was comparable to the readers in discriminating these regions in cancer and non-cancer. In a retrospective analysis, the effect of independent combination of reader scores with CAD was compared to independent combination of scores of two radiologists. No significant difference was found between the results of these two methods. Both methods improved single reading results significantly.

Accuracy of rigid CT-FDG-PET image registration of the liver.

Diagnostic and surgical strategies could benefit from accurate localization of liver malignancies via CT-FDG-PET image registration. However, registration uncertainty occurs due to protocol differences in data-acquisition, the limited spatial resolution of positron emission tomography (PET) and the low uptake of 18F-fluorodeoxyglucose (FDG) in normal liver tissue. To assess this uncertainty, methods were presented to estimate registration precision and systematic bias. A semi-automatic, organ-focused method was investigated to minimize the uncertainty well beyond the typical uncertainty of 5-10 mm obtained by commonly available methods. By restricting registration to the liver region and by isolating the liver on computed tomography (CT) from surrounding structures using a thresholding technique, registration was achieved using the mutual information-based method as implemented in insight toolkit (ITK). CT and FDG-PET images of 10 patients with liver metastases were registered rigidly a number of times. Results of the organ-focused method were compared to results of three commonly available methods (a manual, a landmark-based and a 'standard' mutual information-based method) where no dedicated image processing was performed. The proposed method outperformed the other methods with a precision (mean+/-s.d.) of 2.5+/-1.3 mm and a bias of 1.9 mm with a 95% CI of [1.0, 2.8] mm. Unlike the commonly available methods, our approach allows for robust CT-FDG-PET registration of the liver, with an accuracy better than the spatial resolution of the PET scanner that was used.