Simulation of breast lesions based upon fractal Perlin noise.

PURPOSE: Steadily increasing use of computational/virtual phantoms in medical physics has motivated expanding development of new simulation methods and data representations for modelling human anatomy. This has emphasized the need for increased realism, user control, and availability. In breast cancer research, virtual phantoms have gained an important role in evaluating and optimizing imaging systems. For this paper, we have developed an algorithm to model breast abnormalities based on fractal Perlin noise. We demonstrate and characterize the extension of this approach to simulate breast lesions of various sizes, shapes, and complexity. MATERIALS AND METHOD: Recently, we developed an algorithm for simulating the 3D arrangement of breast anatomy based on Perlin noise. In this paper, we have expanded the method to also model soft tissue breast lesions. We simulated lesions within the size range of clinically representative breast lesions (masses, 5-20 mm in size). Simulated lesions were blended into simulated breast tissue backgrounds and visualized as virtual digital mammography images. The lesions were evaluated by observers following the BI-RADS assessment criteria. RESULTS: Observers categorized the lesions as round, oval or irregular, with circumscribed, microlobulated, indistinct or obscured margins. The majority of the simulated lesions were considered by the observers to have a realism score of moderate to well. The simulation method provides almost real-time lesion generation (average time and standard deviation: 1.4 ± 1.0 s). CONCLUSION: We presented a novel algorithm for computer simulation of breast lesions using Perlin noise. The algorithm enables efficient simulation of lesions, with different sizes and appearances.

Personalized breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence.

Purpose: Breast cancer screening is predominantly performed using digital mammography (DM), but digital breast tomosynthesis (DBT) has higher sensitivity. DBT demands more resources than DM, and it might be more feasible to reserve DBT for women with a clear benefit from the technique. We explore if artificial intelligence (AI) can select women who would benefit from DBT imaging. Approach: We used data from Malmö Breast Tomosynthesis Screening Trial, where all women prospectively were examined with separately double read DM and DBT. We retrospectively analyzed DM examinations (n=14768) with a breast cancer detection system and used the provided risk score (1 to 10) for risk stratification. We tested how different score thresholds for adding DBT to an initial DM affects the number of detected cancers, additional DBT examinations needed, detection rate, and false positives. Results: If using a threshold of 9.0, 25 (26%) more cancers would be detected compared to using DM alone. Of the 41 cancers only detected on DBT, 61% would be detected, with only 1797 (12%) of the women examined with both DM and DBT. The detection rate for the added DBT would be 14/1000 women, whereas the false-positive recalls would be increased with 58 (21%). Conclusion: Using DBT only for selected high gain cases could be an alternative to complete DBT screening. AI can analyze initial DM images to identify high gain cases where DBT can be added during the same visit. There might be logistical challenges, and further studies in a prospective setting are necessary.

Malmö Breast ImaginG database: objectives and development.

Purpose: We describe the design and implementation of the Malmö Breast ImaginG (M-BIG) database, which will support research projects investigating various aspects of current and future breast cancer screening programs. Specifically, M-BIG will provide clinical data to:1.investigate the effect of breast cancer screening on breast cancer prognosis and mortality;2.develop and validate the use of artificial intelligence and machine learning in breast image interpretation; and3.develop and validate image-based radiological breast cancer risk profiles. Approach: The M-BIG database is intended to include a wide range of digital mammography (DM) and digital breast tomosynthesis (DBT) examinations performed on women at the Mammography Clinic in Malmö, Sweden, from the introduction of DM in 2004 through 2020. Subjects may be included multiple times and for diverse reasons. The image data are linked to extensive clinical, diagnostic, and demographic data from several registries. Results: To date, the database contains a total of 451,054 examinations from 104,791 women. During the inclusion period, 95,258 unique women were screened. A total of 19,968 examinations were performed using DBT, whereas the rest used DM. Conclusions: We describe the design and implementation of the M-BIG database as a representative and accessible medical image database linked to various types of medical data. Work is ongoing to add features and curate the existing data.

Breast cancer screening with digital breast tomosynthesis: comparison of different reading strategies implementing artificial intelligence.

OBJECTIVES: Digital breast tomosynthesis (DBT) can detect more cancers than the current standard breast screening method, digital mammography (DM); however, it can substantially increase the reading workload and thus hinder implementation in screening. Artificial intelligence (AI) might be a solution. The aim of this study was to retrospectively test different ways of using AI in a screening workflow. METHODS: An AI system was used to analyse 14,772 double-read single-view DBT examinations from a screening trial with paired DM double reading. Three scenarios were studied: if AI can identify normal cases that can be excluded from human reading; if AI can replace the second reader; if AI can replace both readers. The number of detected cancers and false positives was compared with DM or DBT double reading. RESULTS: By excluding normal cases and only reading 50.5% (7460/14,772) of all examinations, 95% (121/127) of the DBT double reading detected cancers could be detected. Compared to DM screening, 27% (26/95) more cancers could be detected (p < 0.001) while keeping recall rates at the same level. With AI replacing the second reader, 95% (120/127) of the DBT double reading detected cancers could be detected-26% (25/95) more than DM screening (p < 0.001)-while increasing recall rates by 53%. AI alone with DBT has a sensitivity similar to DM double reading (p = 0.689). CONCLUSION: AI can open up possibilities for implementing DBT screening and detecting more cancers with the total reading workload unchanged. Considering the potential legal and psychological implications, replacing the second reader with AI would probably be most the feasible approach. KEY POINTS: • Breast cancer screening with digital breast tomosynthesis and artificial intelligence can detect more cancers than mammography screening without increasing screen-reading workload. • Artificial intelligence can either exclude low-risk cases from double reading or replace the second reader. • Retrospective study based on paired mammography and digital breast tomosynthesis screening data.

Finite element model of mechanical imaging of the breast.

Purpose: Malignant breast lesions can be distinguished from benign lesions by their mechanical properties. This has been utilized for mechanical imaging in which the stress distribution over the breast is measured. Mechanical imaging has shown the ability to identify benign or normal cases and to reduce the number of false positives from mammography screening. Our aim was to develop a model of mechanical imaging acquisition for simulation purposes. To that end, we simulated mammographic compression of a computer model of breast anatomy and lesions. Approach: The breast compression was modeled using the finite element method. Two finite element breast models of different sizes were used and solved using linear elastic material properties in open-source virtual clinical trial (VCT) software. A spherical lesion (15 mm in diameter) was inserted into the breasts, and both the location and stiffness of the lesion were varied extensively. The average stress over the breast and the average stress at the lesion location, as well as the relative mean pressure over lesion area (RMPA), were calculated. Results: The average stress varied 6.2-6.5 kPa over the breast surface and 7.8-11.4 kPa over the lesion, for different lesion locations and stiffnesses. These stresses correspond to an RMPA of 0.80 to 1.46. The average stress was 20% to 50% higher at the lesion location compared with the average stress over the entire breast surface. Conclusions: The average stress over the breast and the lesion location corresponded well to clinical measurements. The proposed model can be used in VCTs for evaluation and optimization of mechanical imaging screening strategies.

Development and evaluation of a method for tumor growth simulation in virtual clinical trials of breast cancer screening.

Purpose: Image-based analysis of breast tumor growth rate may optimize breast cancer screening and diagnosis by suggesting optimal screening intervals and guide the clinical discussion regarding personalized screening based on tumor aggressiveness. Simulation-based virtual clinical trials (VCTs) can be used to evaluate and optimize medical imaging systems and design clinical trials. This study aimed to simulate tumor growth over multiple screening rounds. Approach: This study evaluates a preliminary method for simulating tumor growth. Clinical data on tumor volume doubling time (TVDT) was used to fit a probability distribution ("clinical fit") of TVDTs. Simulated tumors with TVDTs sampled from the clinical fit were inserted into 30 virtual breasts ("simulated cohort") and used to simulate mammograms. Based on the TVDT, two successive screening rounds were simulated for each virtual breast. TVDTs from clinical and simulated mammograms were compared. Tumor sizes in the simulated mammograms were measured by a radiologist in three repeated sessions to estimate TVDT. Results: The mean TVDT was 297 days (standard deviation, SD, 169 days) in the clinical fit and 322 days (SD, 217 days) in the simulated cohort. The mean estimated TVDT was 340 days (SD, 287 days). No significant difference was found between the estimated TVDTs from simulated mammograms and clinical TVDT values ( p > 0.5 ). No significant difference ( p > 0.05 ) was observed in the reproducibility of the tumor size measurements between the two screening rounds. Conclusions: The proposed method for tumor growth simulation has demonstrated close agreement with clinical results, supporting potential use in VCTs of temporal breast imaging.

Artificial Intelligence Detection of Missed Cancers at Digital Mammography That Were Detected at Digital Breast Tomosynthesis.

PURPOSE: To investigate how an artificial intelligence (AI) system performs at digital mammography (DM) from a screening population with ground truth defined by digital breast tomosynthesis (DBT), and whether AI could detect breast cancers at DM that had originally only been detected at DBT. MATERIALS AND METHODS: In this secondary analysis of data from a prospective study, DM examinations from 14 768 women (mean age, 57 years), examined with both DM and DBT with independent double reading in the MalmÓ§ Breast Tomosynthesis Screening Trial (MBTST) (ClinicalTrials.gov: NCT01091545; data collection, 2010-2015), were analyzed with an AI system. Of 136 screening-detected cancers, 95 cancers were detected at DM and 41 cancers were detected only at DBT. The system identifies suspicious areas in the image, scored 1-100, and provides a risk score of 1 to 10 for the whole examination. A cancer was defined as AI detected if the cancer lesion was correctly localized and scored at least 62 (threshold determined by the AI system developers), therefore resulting in the highest examination risk score of 10. Data were analyzed with descriptive statistics, and detection performance was analyzed with receiver operating characteristics. RESULTS: The highest examination risk score was assigned to 10% (1493 of 14 786) of the examinations. With 90.8% specificity, the AI system detected 75% (71 of 95) of the DM-detected cancers and 44% (18 of 41) of cancers at DM that had originally been detected only at DBT. The majority were invasive cancers (17 of 18). CONCLUSION: Almost half of the additional DBT-only screening-detected cancers in the MBTST were detected at DM with AI. AI did not reach double reading performance; however, if combined with double reading, AI has the potential to achieve a substantial portion of the benefit of DBT screening.Keywords: Computer-aided Diagnosis, Mammography, Breast, Diagnosis, Classification, Application DomainClinical trial registration no. NCT01091545© RSNA, 2021.

VIRTUAL CLINICAL TRIALS IN MEDICAL IMAGING SYSTEM EVALUATION AND OPTIMISATION.

Virtual clinical trials (VCTs) can be used to evaluate and optimise medical imaging systems. VCTs are based on computer simulations of human anatomy, imaging modalities and image interpretation. OpenVCT is an open-source framework for conducting VCTs of medical imaging, with a particular focus on breast imaging. The aim of this paper was to evaluate the OpenVCT framework in two tasks involving digital breast tomosynthesis (DBT). First, VCTs were used to perform a detailed comparison of virtual and clinical reading studies for the detection of lesions in digital mammography and DBT. Then, the framework was expanded to include mechanical imaging (MI) and was used to optimise the novel combination of simultaneous DBT and MI. The first experiments showed close agreement between the clinical and the virtual study, confirming that VCTs can predict changes in performance of DBT accurately. Work in simultaneous DBT and MI system has demonstrated that the system can be optimised in terms of the DBT image quality. We are currently working to expand the OpenVCT software to simulate MI acquisition more accurately and to include models of tumour growth. Based on our experience to date, we envision a future in which VCTs have an important role in medical imaging, including support for more imaging modalities, use with rare diseases and a role in training and testing artificial intelligence (AI) systems.

Identifying normal mammograms in a large screening population using artificial intelligence.

OBJECTIVES: To evaluate the potential of artificial intelligence (AI) to identify normal mammograms in a screening population. METHODS: In this retrospective study, 9581 double-read mammography screening exams including 68 screen-detected cancers and 187 false positives, a subcohort of the prospective population-based Malmö Breast Tomosynthesis Screening Trial, were analysed with a deep learning-based AI system. The AI system categorises mammograms with a cancer risk score increasing from 1 to 10. The effect on cancer detection and false positives of excluding mammograms below different AI risk thresholds from reading by radiologists was investigated. A panel of three breast radiologists assessed the radiographic appearance, type, and visibility of screen-detected cancers assigned low-risk scores (≤ 5). The reduction of normal exams, cancers, and false positives for the different thresholds was presented with 95% confidence intervals (CI). RESULTS: If mammograms scored 1 and 2 were excluded from screen-reading, 1829 (19.1%; 95% CI 18.3-19.9) exams could be removed, including 10 (5.3%; 95% CI 2.1-8.6) false positives but no cancers. In total, 5082 (53.0%; 95% CI 52.0-54.0) exams, including 7 (10.3%; 95% CI 3.1-17.5) cancers and 52 (27.8%; 95% CI 21.4-34.2) false positives, had low-risk scores. All, except one, of the seven screen-detected cancers with low-risk scores were judged to be clearly visible. CONCLUSIONS: The evaluated AI system can correctly identify a proportion of a screening population as cancer-free and also reduce false positives. Thus, AI has the potential to improve mammography screening efficiency. KEY POINTS: • Retrospective study showed that AI can identify a proportion of mammograms as normal in a screening population. • Excluding normal exams from screening using AI can reduce false positives.

Intra- and inter-rater reliability of compressed breast thickness, applied force, and pressure distribution in screening mammography.

BACKGROUND: Ensuring equivalent and reproducible breast compression between mammographic screening rounds is important for the diagnostic performance of mammography, yet the extent to which screening mammography positioning and compression is reproducible for the individual woman is unknown. PURPOSE: To investigate the intra- and inter-rater reliability of breast compression in screening mammography. MATERIALS AND METHODS: Eleven breast-healthy women participated in the study. Two experienced radiographers independently positioned and compressed the breasts of each participant in two projections-right craniocaudal and left mediolateral oblique-and at two time points. The spatial pressure distribution on the compressed breast was measured using a pressure sensor matrix. Applied force, compressed breast thickness, force in field of view, contact area, mean pressure, and center of mass (anterio-posterior and mediolateral axes) were measured. The reliabilities of the measures between the time points for each radiographer (intra-rater reliability) and between the radiographers (inter-rater reliability) were analyzed using the intraclass correlation coefficient (ICC). RESULTS: Intra- and inter-rater reliabilities from both projections demonstrated good to excellent ICCs (≥0.82) for compressed breast thickness, contact area, and anterio-posterior center of mass. The other measures produced ICCs that varied from poor (≤0.42) to excellent (≥0.93) between time points and between radiographers. CONCLUSION: Intra- and inter-rater reliability of breast compression was consistently high for compressed breast thickness, contact area, and anterio-posterior center of mass but low for mediolateral center of mass and applied force. Further research is needed to establish objective and clinically useful parameters for the standardization of breast compression.

Distribution of pressure on the breast in mammography using flexible and rigid compression plates: implications on patient handling.

BACKGROUND: Breast compression in mammography is important but is a source of discomfort and has been linked to screening non-attendance. Reducing compression has little effect on breast thickness, and likely little effect on image quality, due to force being absorbed in the stiff juxta thoracic area instead of in the central breast. PURPOSE: To investigate whether a flexible compression plate can redistribute force to the central breast and whether this affects perceived pain. MATERIAL AND METHODS: Twenty-eight women recalled from mammography screening were compressed with flexible and rigid plates while retaining force and positioning, 15 in the craniocaudal (CC) view and 13 in the mediolateral oblique (MLO) view. Pressure distribution was continuously measured using pressure sensors. RESULTS: The flexible plate showed greater mean breast pressure in both views: 2.8 versus 2.3 kPa for CC (confidence interval [CI] = 0.2-0.8) and 1.0 versus 0.5 kPa for MLO (CI = 0.2-0.6). The percentage of applied force distributed to the breast was significantly higher with the flexible plate, both on CC (36% vs. 22%, CI = 1-11) and MLO (30% vs. 14%, CI = 4-13). CONCLUSION: The flexible plate redistributes pressure to the central breast, achieving a better compression, particularly in the MLO view, though much applied force is still applied to the juxta thoracic region.

High-attenuation artifact reduction in breast tomosynthesis using a novel reconstruction algorithm.

PURPOSE: To assess the effect on reducing the out-of-plane artifacts from metal objects in breast tomosynthesis (BT) using a novel artifact-reducing reconstruction algorithm in specimen radiography. METHODS AND MATERIALS: The study was approved by the Regional Ethical Review Board. BT images of 18 partial- and whole mastectomy specimens from women with breast cancer were acquired before and after a needle was inserted close to the lesion. The images were reconstructed using both a standard reconstruction algorithm, and a novel algorithm; the latter uses pre-segmentation to remove highly attenuating artifact-inducing objects from projection images before reconstruction. Images were separately reconstructed with and without segmentation, and combined into an artifact-reduced reconstruction. Standard and artifact-reduced BT-algorithms were compared visually and quantitatively using clinical images of mastectomy specimens and a physical anthropomorphic phantom. Six readers independently assessed the visibility of the lesion with and without artifact-reduction in a side-by-side comparison. A quantitative analysis was performed, comparing the signal-difference to background ratio (SDBR) and artifact spread function (ASF) between the two reconstruction methods. RESULTS: The magnitude of out-of-plane artifacts was clearly reduced with the novel reconstruction compared to BT-images without artifact reduction. Lesion masking by artifacts was largely averted; tumour visibility was comparable to standard BT images without a needle. In 76 ± 8% (standard deviation) of cases overall, readers could confidently state needle location. The same figure was 94 ± 6% for whole mastectomy cases, compared to 62 ± 17% for partial mastectomies. With metal artifact reduction, SDBR increased by 97% in the phantom, and by 69% in the mastectomies. The artifact spread function was substantially narrower. CONCLUSION: Artifact reduction in BT using a novel reconstruction method enables qualitatively and quantitatively improved clinical use of BT when metal artifacts can be a limiting factor such as in tomosynthesis-guided biopsy.

One-view breast tomosynthesis versus two-view mammography in the Malmö Breast Tomosynthesis Screening Trial (MBTST): a prospective, population-based, diagnostic accuracy study.

BACKGROUND: Digital breast tomosynthesis is an advancement of the mammographic technique, with the potential to increase detection of lesions during breast cancer screening. The main aim of the Malmö Breast Tomosynthesis Screening Trial (MBTST) was to investigate the accuracy of one-view digital breast tomosynthesis in population screening compared with standard two-view digital mammography. METHODS: In this prospective, population-based screening study, of women aged 40-74 years invited to attend national breast cancer screening at Skåne University Hospital, Malmö, Sweden, a random sample was asked to participate in the trial (every third woman who was invited to attend regular screening was invited to participate). Participants had to be able to speak English or Swedish and were excluded from the study if they were pregnant. Participants underwent screening with two-view digital mammography (ie, craniocaudal and mediolateral oblique views) followed by one-view digital breast tomosynthesis with reduced compression in the mediolateral oblique view (with a wide tomosynthesis angle of 50°) at one screening visit. Images were read with masked double reading and scoring by two separate reading groups, one for each method, made up of seven radiologists. Any cancer detected with a malignancy probability score of three or higher by any reader in either group was discussed in a consensus meeting of at least two readers, from which the decision of whether or not to recall the woman for further investigation was made. The primary outcome measures were sensitivity and specificity of breast cancer detection. Secondary outcome measures were screening performance measures of cancer detection, recall, and interval cancers (cancers clinically detected between screenings), and positive predictive value for screen recalls and negative predictive value of each method. Outcomes were analysed in the per-protocol population. Follow-up of the participants for at least 2 years allowed for identification of interval cancers. This trial is registered with ClinicalTrials.gov, number NCT01091545. FINDINGS: Between Jan 27, 2010, and Feb 13, 2015, of 21 691 women invited, 14 851 (68%) agreed to participate. Three women withdrew consent during follow-up and were excluded from the analyses. 139 breast cancers were detected in 137 (<1%) of 14 848 women. Sensitivity was higher for digital breast tomosynthesis than for digital mammography (81·1%, 95% CI 74·2-86·9, vs 60·4%, 52·3-68·0) and specificity was slightly lower for digital breast tomosynthesis than was for digital mammography (97·2%, 95% CI 97·0-97·5, vs 98·1%, 97·9-98·3). The proportion of cancers detected was significantly higher with digital breast tomosynthesis than with digital mammography (8·7 cancers per 1000 women screened, 95% CI 7·3-10·3 vs 6·5 cancers per 1000 screened, 5·2-7·9; p<0·0001). The proportion of women recalled after discussion was higher among cancers detected by digital breast tomosynthesis than for those detected by digital mammography after consensus (3·6%, 95% CI 3·3-3·9 vs 2·5%, 2·2-2·8; p<0·0001). The positive predictive value for screen recalls was 24·1% (95% CI 20·5-28·0) for digital breast tomosynthesis and 25·9% (21·6-30·7) for digital mammography, and the negative predictive value was 99·8% (99·7-99·9) and 99·6% (99·4-99·7), respectively. The proportion of women who developed interval cancers after trial screening was 1·48 cancers per 1000 women screened (95% CI 0·93-2·24). INTERPRETATION: Breast cancer screening by use of one-view digital breast tomosynthesis with a reduced compression force has higher sensitivity at a slightly lower specificity for breast cancer detection compared with two-view digital mammography and has the potential to reduce the radiation dose and screen-reading burden required by two-view digital breast tomosynthesis with two-view digital mammography. FUNDING: The Swedish Cancer Society, The Swedish Research Council, The Breast Cancer Foundation, The Swedish Medical Society, The Crafoord Foundation, The Gunnar Nilsson Cancer Foundation, The Skåne University Hospital Foundation, Governmental funding for clinical research, The South Swedish Health Care Region, The Malmö Hospital Cancer Foundation and The Cancer Foundation at the Department of Oncology, Skåne University Hospital.

One-view digital breast tomosynthesis as a stand-alone modality for breast cancer detection: do we need more?

PURPOSE: To compare the performance of one-view digital breast tomosynthesis (1v-DBT) to that of three other protocols combining DBT and mammography (DM) for breast cancer detection. MATERIALS AND METHODS: Six radiologists, three experienced with 1v-DBT in screening, retrospectively reviewed 181 cases (76 malignant, 50 benign, 55 normal) in two sessions. First, they scored sequentially: 1v-DBT (medio-lateral oblique, MLO), 1v-DBT (MLO) + 1v-DM (cranio-caudal, CC) and two-view DM + DBT (2v-DM+2v-DBT). The second session involved only 2v-DM. Lesions were scored using BI-RADS® and level of suspiciousness (1-10). Sensitivity, specificity, receiver operating characteristic (ROC) and jack-knife alternative free-response ROC (JAFROC) were computed. RESULTS: On average, 1v-DBT was non-inferior to any of the other protocols in terms of JAFROC figure-of-merit, area under ROC curve, sensitivity or specificity (p>0.391). While readers inexperienced with 1v-DBT screening improved their sensitivity when adding more images (69-79 %, p=0.019), experienced readers showed similar sensitivity (76 %) and specificity (70 %) between 1v-DBT and 2v-DM+2v-DBT (p=0.482). Subanalysis by lesion type and breast density showed no difference among modalities. CONCLUSION: Detection performance with 1v-DBT is not statistically inferior to 2v-DM or to 2v-DM+2v-DBT; its use as a stand-alone modality might be sufficient for readers experienced with this protocol. KEY POINTS: • One-view breast tomosynthesis is not inferior to two-view digital mammography. • One-view DBT is not inferior to 2-view DM plus 2-view DBT. • Training may lead to 1v-DBT being sufficient for screening.

Can mechanical imaging increase the specificity of mammography screening?

OBJECTIVES: This study aimed to investigate the effects of adding adjunct mechanical imaging to mammography breast screening. We hypothesized that mechanical imaging could detect increased local pressure caused by both malignant and benign breast lesions and that a pressure threshold for malignancy could be established. The impact of this on breast screening was investigated with regard to reductions in recall and biopsy rates. METHODS: 155 women recalled from breast screening were included in the study, which was approved by the regional ethical review board (dnr 2013/620). Mechanical imaging readings were acquired of the symptomatic breast. The relative mean pressure on the suspicious area (RMPA) was defined and a threshold for malignancy was established. RESULTS: Biopsy-proven invasive cancers had a median RMPA of 3.0 (interquartile range (IQR) = 3.7), significantly different from biopsy-proven benign at 1.3 (IQR = 1.0) and non-biopsied cases at 1.0 (IQR = 1.3) (P < 0.001). The lowest RMPA for invasive cancer was 1.4, with 23 biopsy-proven benign and 33 non-biopsied cases being below this limit. Had these women not been recalled, recall rates would have been reduced by 36% and biopsy rates by 32%. CONCLUSIONS: If implemented in a screening situation, this may substantially lower the number of false positives. KEY POINTS: • Mechanical imaging is used as an adjunct to mammography in breast screening. • A threshold pressure can be established for malignant breast cancer. • Recalls and biopsies can be substantially reduced.

VALIDATION OF A SIMULATION PROCEDURE FOR GENERATING BREAST TOMOSYNTHESIS PROJECTION IMAGES.

In order to achieve optimal diagnostic performance in breast tomosynthesis (BT) imaging, the parameters of the imaging chain should be evaluated. For the purpose of such evaluations, a simulation procedure based on the Monte Carlo code system Penelope and the geometry of a Siemens BT system has been developed to generate BT projection images. In this work, the simulation procedure is validated by comparing contrast and sharpness in simulated images with contrast and sharpness in real images acquired with the BT system. The results of the study showed a good agreement of sharpness in real and simulated reconstructed image planes, but the contrast was shown to be higher in the simulated compared with the real projection images. The developed simulation procedure could be used to generate BT images, but it is of interest to further investigate how the procedure could be modified to generate more realistic image noise and contrast.

No evidence for shedding of circulating tumor cells to the peripheral venous blood as a result of mammographic breast compression.

This pilot study aimed to investigate whether mammographic compression procedures might cause shedding of tumor cells into the circulatory system as reflected by circulating tumor cell (CTC) count in peripheral venous blood samples. From March to October 2012, 24 subjects with strong suspicion of breast malignancy were included in the study. Peripheral blood samples were acquired before and after mammography. Enumeration of CTCs in the blood samples was performed using the CellSearch(®) system. The pressure distribution over the tumor-containing breast was measured using thin pressure sensors. The median age was 66.5 years (range, 51-87 years). In 22 of the 24 subjects, breast cancer was subsequently confirmed. The difference between the average mean tumor pressure 6.8 ± 5.3 kPa (range, 1.0-22.5 kPa) and the average mean breast pressure 3.4 ± 1.6 kPa (range, 1.5-7.1 kPa) was statistically significant (p < 0.001), confirming that there was increased pressure over the tumor. The median pathological tumor size was 19 mm (range, 9-30 mm). Four subjects (17 %) were CTC positive before compression and two of these (8 %) were also CTC positive after compression. A total of seven CTCs were isolated with a mean size of 8 × 6 µm(2) (range of the longest diameter, 5-12 µm). The study supports the view that mammography is a safe procedure from the point of view of tumor cell shedding to the peripheral blood.

Breast compression in mammography: pressure distribution patterns.

BACKGROUND: Breast compression is important in mammography in order to improve image quality, better separate tissue components, and reduce absorbed dose to the breast. In this study we use a method to measure and visualize the distribution of pressure over a compressed breast in mammography. PURPOSE: To measure and describe the pressure distribution over the breast as a result of applied breast compression in mammography. MATERIAL AND METHODS: One hundred and three women aged 40.7-74.3 years (median, 48.9 years) invited for mammographic screening consented to take part in this study. They were subjected to two additional breast compressions of the left breast (standard force and approximately 50% reduction). Pressure images of the compressed breast were obtained using force sensing resistor (FSR) sensors placed underneath the compression plate. Subjects rated their experience of pain on a visual analogue scale (VAS). RESULTS: Four pressure patterns were identified, fitting 81 of the 103 breasts, which were grouped accordingly. The remaining 22 breasts were found to correspond to a combination of any two patterns. Two groups (43 breasts) showed pressure mainly over the juxtathoracic part of the breast, had significantly greater breast thickness (P = 0.003) and had a lower mean pressure over dense tissue (P < 0.0001) than those with more evenly distributed pressure. Reducing compression force increased average breast thickness by 1.8 mm (P < 0.0001). CONCLUSION: The distribution of pressure differed greatly between breasts. In a large proportion of breasts the compression plate did not provide optimal compression of the breast, the compression force being absorbed in juxtathoracic structures.