Malignant Microcalcifications Research Articles

The impact of simulated motion blur on lesion detection performance in full-field digital mammography.

Motion blur is a known phenomenon in full-field digital mammography, but the impact on lesion detection is unknown. This is the first study to investigate detection performance with varying magnitudes of simulated motion blur. 7 observers (15 ± 5 years' reporting experience) evaluated 248 cases (62 containing malignant masses, 62 containing malignant microcalcifications and 124 normal cases) for 3 conditions: no blurring (0 mm) and 2 magnitudes of simulated blurring (0.7 and 1.5 mm). Abnormal cases were biopsy proven. Mathematical simulation was used to provide a pixel shift in order to simulate motion blur. A free-response observer study was conducted to compare lesion detection performance for the three conditions. The equally weighted jackknife alternative free-response receiver operating characteristic was used as the figure of merit. Test alpha was set at 0.05 to control probability of Type I error. The equally weighted jackknife alternative free-response receiver operating characteristic analysis found a statistically significant difference in lesion detection performance for both masses [F(2,22) = 6.01, p = 0.0084] and microcalcifications [F(2,49) = 23.14, p < 0.0001]. The figures of merit reduced as the magnitude of simulated blurring increased. Statistical differences were found between some of the pairs investigated for the detection of masses (0.0 vs 0.7 and 0.0 vs 1.5 mm) and all pairs for microcalcifications (0.0 vs 0.7, 0.0 vs 1.5 and 0.7 vs 1.5 mm). No difference was detected between 0.7 and 1.5 mm for masses. The mathematical simulation of motion blur caused a statistically significant reduction in lesion detection performance. These false-negative decisions could have implications for clinical practice. Advances in knowledge: This research demonstrates for the first time that motion blur has a negative and statistically significant impact on lesion detection performance in digital mammography.

Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography.

Mammograms acquired with full-field digital mammography (FFDM) systems are provided in both "for-processing'' and "for-presentation'' image formats. For-presentation images are traditionally intended for visual assessment by the radiologists. In this study, we investigate the feasibility of using for-presentation images in computerized analysis and diagnosis of microcalcification (MC) lesions. We make use of a set of 188 matched mammogram image pairs of MC lesions from 95 cases (biopsy proven), in which both for-presentation and for-processing images are provided for each lesion. We then analyze and characterize the MC lesions from for-presentation images and compare them with their counterparts in for-processing images. Specifically, we consider three important aspects in computer-aided diagnosis (CAD) of MC lesions. First, we quantify each MC lesion with a set of 10 image features of clustered MCs and 12 textural features of the lesion area. Second, we assess the detectability of individual MCs in each lesion from the for-presentation images by a commonly used difference-of-Gaussians (DoG) detector. Finally, we study the diagnostic accuracy in discriminating between benign and malignant MC lesions from the for-presentation images by a pretrained support vector machine (SVM) classifier. To accommodate the underlying background suppression and image enhancement in for-presentation images, a normalization procedure is applied. The quantitative image features of MC lesions from for-presentation images are highly consistent with that from for-processing images. The values of Pearson's correlation coefficient between features from the two formats range from 0.824 to 0.961 for the 10 MC image features, and from 0.871 to 0.963 for the 12 textural features. In detection of individual MCs, the FROC curve from for-presentation is similar to that from for-processing. In particular, at sensitivity level of 80%, the average number of false-positives (FPs) per image region is 9.55 for both for-presentation and for-processing images. Finally, for classifying MC lesions as malignant or benign, the area under the ROC curve is 0.769 in for-presentation, compared to 0.761 in for-processing (P = 0.436). The quantitative results demonstrate that MC lesions in for-presentation images are highly consistent with that in for-processing images in terms of image features, detectability of individual MCs, and classification accuracy between malignant and benign lesions. These results indicate that for-presentation images can be compatible with for-processing images for use in CAD algorithms for MC lesions.

Microcalcification diagnosis in digital mammography using extreme learning machine based on hidden Markov tree model of dual-tree complex wavelet transform

Abstract Diagnosis of benign and malignant microcalcifications in digital mammography using Computer-aided Diagnosis (CAD) system is critical for the early diagnosis of breast cancer. Wavelet transform based diagnosis methods are effective to accomplish this task, but limited by representing the correlation within each wavelet scale, these methods neglect the correlation between wavelet scales. In this paper, we apply the hidden Markov tree model of dual-tree complex wavelet transform (DTCWT-HMT) for microcalcification diagnosis in digital mammography. DTCWT-HMT can effectively capture the correlation between different wavelet coefficients and model the statistical dependencies and non-Gaussian statistics of real signals, is used to characterize microcalcifications for the diagnosis of benign and malignant cases. The combined features which consist of the DTCWT-HMT features and the DTCWT features are optimized by genetic algorithm (GA). Extreme learning machine (ELM), an efficient learning theory is employed as the classifier to diagnose the benign and malignant microcalcifications. The validity of the proposed method is evaluated on the Nijmegen, MIAS and DDSM datasets using area under curve (AUC) of receiver operating characteristic (ROC). The AUC values of 0.9856, 0.9941 and 0.9168 of the proposed method are achieved on Nijmegen, MIAS and DDSM, respectively. We compare the proposed method with state-of-the-art diagnosis methods, and the experimental results show the effectiveness of the proposed method for the diagnosis of the benign and malignant microcalcifications in mammograms in terms of the accuracy and stability.

Tree-based modelling for the classification of mammographic benign and malignant micro-calcification clusters

Computer Aided Detection (CAD) systems are being developed to assist radiologists in diagnosis. For breast cancer the emphasis is shifting from detection to classification of abnormalities. The presented work concentrates on the benign versus malignant classification of micro-calcification clusters, which are a specific type of mammographic abnormality associated with the early development of breast cancer. After segmentation (automatic or manual), tree-based representations were used to distinguish between benign and malignant clusters, which takes into account clinical criteria such as the number of micro-calcifications in the clusters and their distribution and is based on the topology of the trees and the connectivity of the micro-calcifications. The idea of using tree structure based on the distance of individual calcifications for the classification of benign and malignant micro-calcification clusters is novel and closely related to clinical perception. Tree structures used in this study are distinct from decision trees classifiers being used in many machine learning approaches. Initial evaluation on the Digital Database for Screening Mammography (DDSM) data shows promising results, with an accuracy equal to 91 %, which is comparable to state of the art CAD systems and is in line with clinical perception of the morphology and appearance of benign and malignant micro-calcification clusters.

Dedicated Breast CT: Screening Technique of the Future

Mammography has been the mainstay of breast imaging for over four decades and is the only screening modality demonstrated to reduce mortality from breast cancer. The known limitations of mammography have prompted the development of newer imaging techniques with three-dimensional capabilities such as dedicated breast computed tomography (bCT). Several studies have shown the superiority of bCT in detection of breast masses, when compared to 2-D mammography. Malignant micro-calcification lesions can be detected and characterized by bCT. With further development of higher resolution detectors, bCT should become a modality for large population screening. Contrast-enhanced bCT (CE-bCT) adds improved specificity over mammography and may be utilized as an imaging biomarker in the emerging era of precision medicine.

Computational growth model of breast microcalcification clusters in simulated mammographic environments

BackgroundWhen screening for breast cancer, the radiological interpretation of mammograms is a difficult task, particularly when classifying precancerous growth such as microcalcifications (MCs). Biophysical modeling of benign vs. malignant growth of MCs in simulated mammographic backgrounds may improve characterization of these structures MethodsA mathematical model based on crystal growth rules for calcium oxide (benign) and hydroxyapatite (malignant) was used in conjunction with simulated mammographic backgrounds, which were generated by fractional Brownian motion of varying roughness and quantified by the Hurst exponent to mimic tissue of varying density. Simulated MC clusters were compared by fractal dimension, average circularity of individual MCs, average number of MCs per cluster, and average cluster area. ResultsBenign and malignant clusters were distinguishable by average circularity, average number of MCs per cluster, and average cluster area with p<0.01 across all Hurst exponent values considered. Clusters were distinguishable by fractal dimension with p<0.05 in low Hurst exponent environments. As the Hurst exponent increased (tissue density increased) benign and malignant MCs became indistinguishable by fractal dimension. ConclusionsThe fractal dimension of MCs changes with breast tissue density, which suggests tissue environment plays a role in regulating MC growth. Benign and malignant MCs are distinguishable in all types of tissue by shape, size, and area, which is consistent with findings in the literature. These results may help to better understand the effects of the tissue environment on tumor progression, and improve classification of MCs in mammograms via computer-aided diagnosis.

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

In the past decades, computer-aided detection (CADe) systems have been developed to aid screening radiologists in the detection of malignant microcalcifications. These systems are useful to avoid perceptual oversights and can increase the radiologists' detection rate. However, due to the high number of false positives marked by these CADe systems, they are not yet suitable as an independent reader. Breast arterial calcifications (BACs) are one of the most frequent false positives marked by CADe systems. In this study, a method is proposed for the elimination of BACs as positive findings. Removal of these false positives will increase the performance of the CADe system in finding malignant microcalcifications. A multistage method is proposed for the removal of BAC findings. The first stage consists of a microcalcification candidate selection, segmentation and grouping of the microcalcifications, and classification to remove obvious false positives. In the second stage, a case-based selection is applied where cases are selected which contain BACs. In the final stage, BACs are removed from the selected cases. The BACs removal stage consists of a GentleBoost classifier trained on microcalcification features describing their shape, topology, and texture. Additionally, novel features are introduced to discriminate BACs from other positive findings. The CADe system was evaluated with and without BACs removal. Here, both systems were applied on a validation set containing 1088 cases of which 95 cases contained malignant microcalcifications. After bootstrapping, free-response receiver operating characteristics and receiver operating characteristics analyses were carried out. Performance between the two systems was compared at 0.98 and 0.95 specificity. At a specificity of 0.98, the sensitivity increased from 37% to 52% and the sensitivity increased from 62% up to 76% at a specificity of 0.95. Partial areas under the curve in the specificity range of 0.8-1.0 were significantly different between the system without BACs removal and the system with BACs removal, 0.129 ± 0.009 versus 0.144 ± 0.008 (p<0.05), respectively. Additionally, the sensitivity at one false positive per 50 cases and one false positive per 25 cases increased as well, 37% versus 51% (p<0.05) and 58% versus 67% (p<0.05) sensitivity, respectively. Additionally, the CADe system with BACs removal reduces the number of false positives per case by 29% on average. The same sensitivity at one false positive per 50 cases in the CADe system without BACs removal can be achieved at one false positive per 80 cases in the CADe system with BACs removal. By using dedicated algorithms to detect and remove breast arterial calcifications, the performance of CADe systems can be improved, in particular, at false positive rates representative for operating points used in screening.

Classification of Mammograms into Normal, Benign and Malignant based on Fractal Features

Modern life style of women has made them more vulnerable to breast cancer and it is considered as the largest cause of mortality among women. This paper presents a novel method to classify mammograms into normal ones, with benign and malignant microcalcifications, and with malignant and benign tumors using fractal features derived from fractal dimension. Here, three fractal dimension estimation methods such as Differential Box Counting (DBC), Triangular Prism Surface Area (TPSA) and Blanket methods are used for computing the six fractal features utilized for the classification. The new fractal feature f6 obtained using TPSA method is found to be the best with 100% classification accuracy. The average value of f6 is found to be 0.1110, 0.2875, 0.4743, 0.5271 and 0.8558, for normal, benign masses, benign and malignant microcalcifications and malignant masses respectively. The classification performance of the different features was analyzed using the Receiver Operating Characteristics (ROC).

Robust automatic classification of benign and malignant microcalcification and mass in digital mammography

Breast cancer is the most dangerous cancer among women and second mortality among them. Mammography is the efficient methodology used in early finding of breast cancer. However, mammograms requires high amount of skill and there is a possibility of radiologist to misunderstand it. Hence, computer aided diagnosis are used for finding the abnormalities in mammograms. Automated classification of mass and microcalcification system is proposed in this work using NSCT and SVM. The classification of abnormalities is achieved by extracting the microcalcification and mass features from the contourlet coefficients of the image and the results are used as an input to the SVM. The proposed automated system classifies the mammogram as normal or abnormal and result is abnormal, then it classifies the abnormal severity as benign or malignant. The evaluation of the proposed system is conceded on MIAS database. The experimentation result shows that the proposed system contributes improved classification rate.

Robust Automatic Classification of Benign and Malignant Microcalcification and Mass in Digital Mammography

Robust Automatic Classification of Benign and Malignant Microcalcification and Mass in Digital Mammography

Detectability and Usefulness of Automated Whole Breast Ultrasound in Patients with Suspicious Microcalcifications on Mammography: Comparison with Handheld Breast Ultrasound.

PurposeThe purpose of this study was to prospectively evaluate the detectability and usefulness of automated whole breast ultrasound (AWUS) and to compare it with handheld breast ultrasound (HHUS) in cases with suspicious microcalcifications identified by mammography.MethodsForty-two patients with 43 suspicious microcalcifications (25 malignant and 18 benign) detected by mammography underwent AWUS, HHUS, and histol-ogic examination. With knowledge of the mammographic findings, HHUS was performed to assess the visibility of the microcalcifications and the presence of associated masses or ductal changes. Two radiologists reviewed the AWUS images in consensus using the same methods employed for HHUS. Detectability of AWUS was compared with that of HHUS and was correlated with histologic and mammographic findings.ResultsOf the 43 lesions, 32 (74.4%) were detectable by AWUS and 31 (72.1%) by HHUS. No significant differences in sensitivity were found between the two methods (p=0.998). AWUS detected 96% (24/25) of malignant microcalcifications and 44.4% (8/18) of benign microcalcifications. AWUS was more successful in the detection of malignant vs. benign lesions (96.0% vs. 44.4%, p=0.002), lesions >10 mm vs. ≤10 mm in size (86.7% [26/30] vs. 46.2% [6/13], p=0.009), lesions with a fine pleomorphic or linear shape vs. a round or amorphous or coarse heterogeneous shape (94.7% [18/19] vs. 58.3% [14/24], p=0.021), and lesions associated with a mass or architectural distortion vs. without obvious changes on mammography (100% [19/19] vs. 54.2% [13/24], p=0.022).ConclusionDetectability of AWUS was comparable to that of HHUS in cases where suspicious microcalcifications were identified on mammography. Therefore, AWUS might be helpful in the performance of ultrasound-guided percutaneous procedures for highly suspicious microcalcifications.

Early Detection of Breast Cancer using Mammogram and Wavelet Transform for Woman Care

Mammography is the vital screening testfor early diagnosis of Breast cancer and thereby the death rate caused by this disease is greatly reduced. It is visually tough to recognize malignant micro-calcifications as it is a highly complicated issue. Current research in this area is facing challenges to afford accurate and best solution for the prediction of calcifications. The proposed system is developed for efficient classification of the images as normal and abnormal using wavelet transform and Neural Network. The noise present in the test images are removed by Oriented Rician Noise-Reducing Anisotropic Diffusion (ORNRAD) filter, which makes use of anisotropic diffusion for filtering images. Then the images are decomposed into sub-bands using Symlet filter and the crucial sub band features are extracted by employing statistical metrics such as Mean, Standard Deviation and Variance to make the classification results precise. Levenberg Marquardt is exploited as a training algorithm to train the neural networks and to classify the images.

Clinical utility of dual-energy contrast-enhanced spectral mammography for breast microcalcifications without associated mass: a preliminary analysis.

To assess the utility of dual-energy contrast-enhanced spectral mammography (DE-CESM) for evaluation of suspicious malignant microcalcifications. Two hundred and fifty-six DE-CESMs were reviewed from 2012-2013, 59 cases fulfilled the following criteria and were enrolled for analysis: (1) suspicious malignant microcalcifications (BI-RADS 4) on mammogram, (2) no related mass, (3) with pathological diagnoses. The microcalcification morphology and associated enhancement were reviewed to analyse the accuracy of the diagnosis and cancer size measurements versus the results of pathology. Of the 59 microcalcifications, 22 were diagnosed as cancers, 19 were atypical lesions and 18 were benign lesions. Twenty (76.9 %) cancers, three (11.55 %) atypia and three (11.55 %) benign lesions revealed enhancement. The true-positive rate of intermediate- and high-concern microcalcifications was significantly higher than that of low-concern lesions (93.75 % vs. 50 %). Overall, the diagnostic sensitivity of enhancement was 90.9 %, with 83.78 % specificity, 76.92 % positive predictive value, 93.94 % negative predictive value and 86.4 % accuracy. Performance was good (AUC = 0.87) according to a ROC curve and cancer size correlation with a mean difference of 0.05 cm on a Bland-Altman plot. DE-CESM provides additional enhancement information for diagnosing breast microcalcifications and measuring cancer sizes with high correlation to surgicohistology. • DE-CESM provides additional enhancement information for diagnosing suspicious breast microcalcifications. • The enhanced cancer size closely correlates to microscopy by Bland-Altman plot. • DE-CESM could be considered for evaluation of suspicious malignant microcalcifications.

Neuroendocrine carcinoma of the breast: mammographic features correlated with sonography and histopathological findings

The purpose of this study was to describe the X-ray features of the neuroendocrine carcinoma (NEC) of breast to raise the awareness for the disease. The mammography, sonography and clinicopathologic features in a total of 16 cases of pathologically proven breast neuroendocrine carcinoma in Fudan University Cancer Hospital were analyzed retrospectively. All the 16 patients were women with a mean age of 67.3 years old (ranged from 45 to 75 years old). Twelve patients had palpable masses and the other four patients had outflow from the nipple. Pathological diagnosis included endocrine ductal carcinoma in situ (E-DCIS) in 4 cases, E-DCIS with microinvasion in 5, and invasive solid neuroendocrine carcinoma in 7. On mammography, 9 of 16 cases exhibited round or slightly lobulated masses. Five of the 16 cases exhibited irregular or asymmetric opacities. Two of the 16 cases had negative findings. The borders of the 14 masses detected on mammography were vague in 7, partly unclear in 3 and clear-cut in 4 cases. Malignant microcalcification was not found in all cases. The accuracy rates of preoperative qualitative diagnosis for NEC with mammography and sonography were 68.8% (11/16) and 81.3% (13/16), respectively. Given the application of combined mammography and sonography, the accuracy rates could be improved to 87.5% (14/16). A round mass and irregular or asymmetric opacities without malignant calcification are the most frequent appearances of mammography in neuroendocrine carcinoma of the breast. Combination of mammography and sonography may be useful to improve the accuracy of diagnosis and early detection of neuroendocrine carcinoma of the breast.

Contour analysis of mammographic images in benign and malignant breast diseases

Aim. Development and evaluation of the diagnostic value of techniques for quantitative analysis of masses on mammographic images satisfying the requirements of categories 2-4 in BIRADS (Breast Imaging Reporting And Data System - the system for classification and description of breast imaging results of the American College of Radiology).Methods. Images of 375 focal lesions revealed on mammography (211 malignant and 164 benign, all were verified morphologically), satisfying the criteria 2-4 according to BIRADS classification, were analyzed. Mathematical processing was performed using the MammCheck 1.15 software package developed by authors, which uploads the obtained images. Contour, segments of area, peripheral outlines, homogeneity and inner outlines indexes were calculated.Results.The statistical analysis of foci at standard mammography images of BIRADS 2-4 categories revealed no statistically significant difference in all four estimated parameters. When using tomosynthesis images, statistically significant difference was discovered in the outline index in the groups of benign and malignant foci (mean ± standard deviation: 1.73±1.11 vs. 3.11±2.02, respectively, pConclusion. Outline analysis of mammograms, including those obtained using tomosynthesis, does not provide the necessary level of specificity for the differential diagnosis of benign and malignant microcalcifications and clusters.

Radiological technologists' performance for the detection of malignant microcalcifications in digital mammograms without and with a computer-aided detection system.

The aim of this study was to investigate the diagnostic performance of radiological technologists (RTs) in the detection of malignant microcalcifications and to evaluate how much computer-aided detection (CADe) improved their performances compared with those by expert breast radiologists (BRs). Six board-certified breast RTs and four board-certified BRs participated in a free-response receiver operating characteristic observer study. The dataset consisted of 75 cases (25 malignant, 25 benign, and 25 normal cases) of digital mammograms, selected from the digital database for screening mammography provided by the University of South Florida. Average figure of merit (FOM) of the RTs' performances was statistically analyzed using jack-knife free-response receiver operating characteristic and compared with that of expert BRs. The detection performance of RTs was significantly improved by using CADe; average sensitivity was increased from 46.7% to 56.7%, with a decrease in the average number of false positives per case from 0.19 to 0.13. Detection accuracy of an average FOM was improved from 0.680 to 0.816 ([Formula: see text]) and the difference in FOMs between RTs and radiologists failed to reach statistical significance. RTs' performances for the identification of malignant microcalcifications on digital mammography were sufficiently high and comparable to those of radiologists by using CADe.

Abstract P2-02-01: Randomised controlled trial of stereotactic 11g vacuum-assisted or core biopsy for diagnosis and management of malignant microcalcification

Abstract Background Vaccum-assisted biopsy (VAB) is claimed to improve diagnosis of malignant microcalcification (MM) and prevent underestimation of invasive component compared to stereotactic core biopsy (SCNB) 14g biopsies. VAB is also reported to reduce requirement for further surgical procedures versus SCNB. Methods In a randomised controlled trial, we compared both techniques in the firstline diagnosis of MM and subsequent surgical management in two UK Breast Screening Units. Patients with MM were identified and gave written informed consent to the study before randomisation 1:1 to SCNB or VAB. Exclusions included bleeding diathesis or significant comorbidity preventing surgery. SCNB was performed by advanced radiography practitioners and VAB by experienced radiologists under local anaesthesia. Quality of life was assessed using EORTC QLQ-BR23 questionnaire: Pre-biopsy, 2, 6, and 12 months post-randomisation. The primary objective compared the final pathological diagnosis with the initial biopsy (VAB or SCNB) and required 110 patients to be randomised to show a 25% improvement in diagnostic accuracy with VAB compared to SCNB with a 90% power. Results Three quarters of MM investigated was less than 15mm in size. Overall 787 women were investigated for eligibility. 476 did not meet the inclusion criteria, 82 declined to participate and 100 met exclusion criteria. In total, 129 women were randomised. Both groups were evenly matched for age 57.2 years (range 47-75) and lesion size but VAB took longer (p&amp;lt;0.001) and produced on average, 4 more cores (12 versus 8) (p&amp;lt;0.01). Three (4.7%) VAB developed bleeding causing procedure abandonment. The PPV for biopsy of MM was 30.2% and 1 patient withdrew before the procedure. There was an 84% diagnostic accuracy of SCNB which precluded any effect on surgical outcome. AnalysisVABCore SCNBPn6364 Accurate diagnosis54 (86%)54 (84%)1.0Subsequent therapeutic porcedures - 19 (56%)17 (71%)0.50Subsequent therapeutic procedures - 27 (44%)7 (29%)0.50DCIS upgrade to invasive12.5%8.7%0.86 Both groups had significant Quality of Life (QOL) falls in Global Health (p&amp;lt;0.001) and Social Functioning over time (p&amp;lt;0.04) but no overall difference between groups was seen. At 12 months, body image scores fell and fatigue increased in patients undergoing surgery. Conclusion SCNB has equal accuracy as VAB in the firstline diagnosis of malignant microcalcification. Devicor provided the equipment to Dr S.B. and Prof. N.B., to do this study. Citation Format: Sara M Bundred, Jin Zhou, Anthony Maxwell, Sigrid Whiteside, Julie Morris, Janicke Harake, Nigel J Bundred. Randomised controlled trial of stereotactic 11g vacuum-assisted or core biopsy for diagnosis and management of malignant microcalcification [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P2-02-01.

Using breast radiographers' reports as a second opinion for radiologists' readings of microcalcifications in digital mammography.

The aim of this study was to investigate a practical method for incorporating radiographers' reports with radiologists' readings of digital mammograms. This simulation study was conducted using data from a free-response receiver operating characteristic observer study obtained with 75 cases (25 malignant, 25 benign and 25 normal cases) of digital mammograms. Each of the rating scores obtained by six breast radiographers was utilized as a second opinion for four radiologists' readings with the radiographers' reports. A logical "OR" operation with various criteria settings was simulated for deciding an appropriate method to select a radiographer's report in all combinations of radiologists and radiographers. The average figure of merit (FOM) of the radiologists' performances was statistically analysed using a jackknife procedure (JAFROC) to verify the clinical utility of using radiographers' reports. Potential improvement of the average FOM of the radiologists' performances for identifying malignant microcalcifications could be expected when using radiographers' reports as a second opinion. When the threshold value of 2.6 in Breast Imaging-Reporting and Data System (BI-RADS®) assessment was applied to adopt/reject a radiographer's report, FOMs of radiologists' performances were further improved. When using breast radiographers' reports as a second opinion, radiologists' performances potentially improved when reading digital mammograms. It could be anticipated that radiologists' performances were improved further by setting a threshold value on the BI-RADS assessment provided by the radiographers. For the effective use of a radiographer's report as a second opinion, radiographers' rating scores and its criteria setting for adoption/rejection would be necessary.

PB.9. Randomised controlled trial of stereotactic 11G vacuum-assisted core biopsy for diagnosis and management of malignant microcalcification

Vacuum-assisted biopsy (VAB) may improve accuracy of diagnosis of malignant microcalcification (MM) compared with 14G core biopsy (SCNB), thus reducing requirements for further surgical procedures.

Wavelet-Based 3D Reconstruction of Microcalcification Clusters from Two Mammographic Views: New Evidence That Fractal Tumors Are Malignant and Euclidean Tumors Are Benign

The 2D Wavelet-Transform Modulus Maxima (WTMM) method was used to detect microcalcifications (MC) in human breast tissue seen in mammograms and to characterize the fractal geometry of benign and malignant MC clusters. This was done in the context of a preliminary analysis of a small dataset, via a novel way to partition the wavelet-transform space-scale skeleton. For the first time, the estimated 3D fractal structure of a breast lesion was inferred by pairing the information from two separate 2D projected mammographic views of the same breast, i.e. the cranial-caudal (CC) and mediolateral-oblique (MLO) views. As a novelty, we define the “CC-MLO fractal dimension plot”, where a “fractal zone” and “Euclidean zones” (non-fractal) are defined. 118 images (59 cases, 25 malignant and 34 benign) obtained from a digital databank of mammograms with known radiologist diagnostics were analyzed to determine which cases would be plotted in the fractal zone and which cases would fall in the Euclidean zones. 92% of malignant breast lesions studied (23 out of 25 cases) were in the fractal zone while 88% of the benign lesions were in the Euclidean zones (30 out of 34 cases). Furthermore, a Bayesian statistical analysis shows that, with 95% credibility, the probability that fractal breast lesions are malignant is between 74% and 98%. Alternatively, with 95% credibility, the probability that Euclidean breast lesions are benign is between 76% and 96%. These results support the notion that the fractal structure of malignant tumors is more likely to be associated with an invasive behavior into the surrounding tissue compared to the less invasive, Euclidean structure of benign tumors. Finally, based on indirect 3D reconstructions from the 2D views, we conjecture that all breast tumors considered in this study, benign and malignant, fractal or Euclidean, restrict their growth to 2-dimensional manifolds within the breast tissue.