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Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm 2 ). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.

Several breast pathologies can affect the skin, and clinical pathways might differ significantly depending on the underlying diagnosis. This study investigates the feasibility of using diffusion-weighted imaging (DWI) to differentiate skin pathologies in breast MRIs. This retrospective study included 88 female patients who underwent diagnostic breast MRI (1.5 or 3T), including DWI. Skin areas were manually segmented, and the apparent diffusion coefficients (ADCs) were compared between different pathologies: inflammatory breast cancer (IBC; n = 5), benign skin inflammation (BSI; n = 11), Paget's disease (PD; n = 3), and skin-involved breast cancer (SIBC; n = 11). Fifty-eight women had healthy skin (H; n = 58). The SIBC group had a significantly lower mean ADC than the BSI and IBC groups. These differences persisted for the first-order features of the ADC (mean, median, maximum, and minimum) only between the SIBC and BSI groups. The mean ADC did not differ significantly between the BSI and IBC groups. Quantitative DWI assessments demonstrated differences between various skin-affecting pathologies, but did not distinguish clearly between all of them. More extensive studies are needed to assess the utility of quantitative DWI in supplementing the diagnostic assessment of skin pathologies in breast imaging.

The purpose of this feasibility study is to investigate if latent diffusion models (LDMs) are capable to generate contrast enhanced (CE) MRI-derived subtraction maximum intensity projections (MIPs) of the breast, which are conditioned by lesions. We trained an LDM with n = 2832 CE-MIPs of breast MRI examinations of n = 1966 patients (median age: 50 years) acquired between the years 2015 and 2020. The LDM was subsequently conditioned with n = 756 segmented lesions from n = 407 examinations, indicating their location and BI-RADS scores. By applying the LDM, synthetic images were generated from the segmentations of an independent validation dataset. Lesions, anatomical correctness, and realistic impression of synthetic and real MIP images were further assessed in a multi-rater study with five independent raters, each evaluating n = 204 MIPs (50% real/50% synthetic images). The detection of synthetic MIPs by the raters was akin to random guessing with an AUC of 0.58. Interrater reliability of the lesion assessment was high both for real (Kendall's W = 0.77) and synthetic images (W = 0.85). A higher AUC was observed for the detection of suspicious lesions (BI-RADS ≥ 4) in synthetic MIPs (0.88 vs. 0.77; p = 0.051). Our results show that LDMs can generate lesion-conditioned MRI-derived CE subtraction MIPs of the breast, however, they also indicate that the LDM tended to generate rather typical or 'textbook representations' of lesions., (© 2024. The Author(s).)

Background: Radiopaque breast markers cause artifacts in dedicated spiral breast-computed tomography (SBCT). This study investigates the extent of artifacts in different marker types and the feasibility of reducing artifacts through a metal artifact reduction (MAR) algorithm., Methods: The pilot study included 18 women who underwent contrast-enhanced SBCT. In total, 20 markers of 4 different types were analyzed for artifacts. The extent of artifacts with and without MAR was measured via the consensus of two readers. Image noise was quantitatively evaluated, and the effect of MAR on the detectability of breast lesions was evaluated on a 3-point Likert scale., Results: Breast markers caused significant artifacts that impaired image quality and the detectability of lesions. MAR decreased artifact size in all analyzed cases, even in cases with multiple markers in a single slice. The median length of in-plain artifacts significantly decreased from 31 mm (range 11-51 mm) in uncorrected to 2 mm (range 1-5 mm) in corrected images ( p ≤ 0.05). Artifact size was dependent on marker size. Image noise in slices affected by artifacts was significantly lower in corrected (13.6 ± 2.2 HU) than in uncorrected images (19.2 ± 6.8 HU, p ≤ 0.05). MAR improved the detectability of lesions affected by artifacts in 5 out of 11 cases., Conclusion: MAR is feasible in SBCT and improves the image quality and detectability of lesions.

The objective of this IRB approved retrospective study was to apply deep learning to identify magnetic resonance imaging (MRI) artifacts on maximum intensity projections (MIP) of the breast, which were derived from diffusion weighted imaging (DWI) protocols. The dataset consisted of 1309 clinically indicated breast MRI examinations of 1158 individuals (median age [IQR]: 50 years [16.75 years]) acquired between March 2017 and June 2020, in which a DWI sequence with a high b-value equal to 1500 s/mm 2 was acquired. From these, 2D MIP images were computed and the left and right breast were cropped out as regions of interest (ROI). The presence of MRI image artifacts on the ROIs was rated by three independent observers. Artifact prevalence in the dataset was 37% (961 out of 2618 images). A DenseNet was trained with a fivefold cross-validation to identify artifacts on these images. In an independent holdout test dataset (n = 350 images) artifacts were detected by the neural network with an area under the precision-recall curve of 0.921 and a positive predictive value of 0.981. Our results show that a deep learning algorithm is capable to identify MRI artifacts in breast DWI-derived MIPs, which could help to improve quality assurance approaches for DWI sequences of breast examinations in the future., (© 2023. The Author(s).)

Background: In the German Mammography Screening Program, 62% of ductal carcinoma in situ (DCIS) and 38% of invasive breast cancers are associated with microcalcifications (MCs). Vacuum-assisted stereotactic breast biopsies are necessary to distinguish precancerous lesions from benign calcifications because mammographic discrimination is not possible. The aim of this study was to investigate if breast magnetic resonance imaging (MRM) could assist the evaluation of MCs and thus help reduce biopsy rates., Methods: In this IRB-approved study, 58 women (mean age 58 +/- 24 years) with 59 suspicious MC clusters in the MG were eligible for this prospective single-center trial. Additional breast magnetic resonance imaging (MRI) was conducted before biopsy., Results: The breast MRI showed a sensitivity of 86%, a specificity of 84%, a positive predictive value (PPV) of 75% and a negative predictive value (NPV) of 91% for the differentiation between benign and malignant in these 59 MCs found with MG. Breast MRI in addition to MG could increase the PPV from 36% to 75% compared to MG alone. The MRI examination led to nine additional suspicious classified lesions in the study cohort. A total of 55% (5/9) of them turned out to be malignant. A total of 32 of 59 (54 %) women with suspicious MCs and benign histology were classified as non-suspicious by MRI., Conclusion: An additionally performed breast MRI could have increased the diagnostic reliability in the assessment of MCs. Further, in our small cohort, a considerable number of malignant lesions without mammographically visible MCs were revealed.

The primary objective of the study was to compare a spiral breast computed tomography system (SBCT) to digital breast tomosynthesis (DBT) for the detection of microcalcifications (MCs) in breast specimens. The secondary objective was to compare various reconstruction modes in SBCT. In total, 54 breast biopsy specimens were examined with mammography as a standard reference, with DBT, and with a dedicated SBCT containing a photon-counting detector. Three different reconstruction modes were applied for SBCT datasets (Recon1 = voxel size (0.15 mm) 3 , smooth kernel; Recon2 = voxel size (0.05 mm) 3 , smooth kernel; Recon3 = voxel size (0.05 mm) 3 , sharp kernel). Sensitivity and specificity of DBT and SBCT for the detection of suspicious MCs were analyzed, and the McNemar test was used for comparisons. Diagnostic confidence of the two readers (Likert Scale 1 = not confident; 5 = completely confident) was analyzed with ANOVA. Regarding detection of MCs, reader 1 had a higher sensitivity for DBT (94.3%) and Recon2 (94.9%) compared to Recon1 (88.5%; p < 0.05), while sensitivity for Recon3 was 92.4%. Respectively, reader 2 had a higher sensitivity for DBT (93.0%), Recon2 (92.4%), and Recon3 (93.0%) compared to Recon1 (86.0%; p < 0.05). Specificities ranged from 84.7-94.9% for both readers ( p > 0.05). The diagnostic confidence of reader 1 was better with SBCT than with DBT (DBT 4.48 ± 0.88, Recon1 4.77 ± 0.66, Recon2 4.89 ± 0.44, and Recon3 4.75 ± 0.72; DBT vs. Recon1/2/3: p < 0.05), while reader 2 found no differences. Sensitivity and specificity for the detection of MCs in breast specimens is equal for DBT and SBCT when a small voxel size of (0.05 mm) 3 is used with an equal or better diagnostic confidence for SBCT compared to DBT.

Background: Mammography can identify calcifications up to 50-100 μm in size as a surrogate parameter for breast cancer or ductal carcinoma in situ (DCIS). Microcalcifications measuring <50 µm are also associated with breast cancer or DCIS and are frequently not detected on mammography, although they can be detected with dark-field imaging. This study examined whether additional breast examination using X-ray dark-field imaging can increase the detection rate of calcifications. Advances in knowledge: (1) evaluation of additional modality of breast imaging; (2) specific evaluation of breast calcifications.Implications for patient care: the addition of X-ray dark-field imaging to conventional mammography could detect additional calcifications., Methods: Talbot-Lau X-ray phase-contrast imaging and X-ray dark-field imaging were used to acquire images of breast specimens. The radiation dosage with the technique is comparable with conventional mammography. Three X-ray gratings with periods of 5-10 µm between the X-ray tube and the flat-panel detector provide three different images in a single sequence: the conventional attenuation image, differential phase image, and dark-field image. The images were read by radiologists. Radiological findings were marked and examined pathologically. The results were described in a descriptive manner., Results: A total of 81 breast specimens were investigated with the two methods; 199 significant structures were processed pathologically, consisting of 123 benign and 76 malignant lesions (DCIS or invasive breast cancer). X-ray dark-field imaging identified 15 additional histologically confirmed carcinoma lesions that were visible but not declared suspicious on digital mammography alone. Another four malignant lesions that were not visible on mammography were exclusively detected with X-ray dark-field imaging., Conclusions: Adding X-ray dark-field imaging to digital mammography increases the detection rate for breast cancer and DCIS associated lesions with micrometer-sized calcifications.The use of X-ray dark-field imaging may be able to provide more accurate and detailed radiological classification of suspicious breast lesions.Adding X-ray dark-field imaging to mammography may be able to increase the detection rate and improve preoperative planning in deciding between mastectomy or breast-conserving therapy, particularly in patients with invasive lobular breast cancer., Competing Interests: Conflict of interest statement: MR is an employee of Siemens Healthcare GmbH, Erlangen, Germany. All other authors declare that they do not have any conflicts of interest., (© The Author(s), 2020.)

Background: BRCA1/2 deleterious variants account for most of the hereditary breast and ovarian cancer cases. Prediction models and guidelines for the assessment of genetic risk rely heavily on criteria with high variability such as family cancer history. Here we investigated the efficacy of MRI (magnetic resonance imaging) texture features as a predictor for BRCA mutation status., Methods: A total of 41 female breast cancer individuals at high genetic risk, sixteen with a BRCA1/2 pathogenic variant and twenty five controls were included. From each MRI 4225 computer-extracted voxels were analyzed. Non-imaging features including clinical, family cancer history variables and triple negative receptor status (TNBC) were complementarily used. Lasso-principal component regression (L-PCR) analysis was implemented to compare the predictive performance, assessed as area under the curve (AUC), when imaging features were used, and lasso logistic regression or conventional logistic regression for the remaining analyses., Results: Lasso-selected imaging principal components showed the highest predictive value (AUC 0.86), surpassing family cancer history. Clinical variables comprising age at disease onset and bilateral breast cancer yielded a relatively poor AUC (~ 0.56). Combination of imaging with the non-imaging variables led to an improvement of predictive performance in all analyses, with TNBC along with the imaging components yielding the highest AUC (0.94). Replacing family history variables with imaging components yielded an improvement of classification performance of ~ 4%, suggesting that imaging compensates the predictive information arising from family cancer structure., Conclusions: The L-PCR model uncovered evidence for the utility of MRI texture features in distinguishing between BRCA1/2 positive and negative high-risk breast cancer individuals, which may suggest value to diagnostic routine. Integration of computer-extracted texture analysis from MRI modalities in prediction models and inclusion criteria might play a role in reducing false positives or missed cases especially when established risk variables such as family history are missing.

To investigate whether automated volumetric radiomic analysis of breast cancer vascularization (VAV) can improve survival prediction in primary breast cancer. 314 consecutive patients with primary invasive breast cancer received standard clinical MRI before the initiation of treatment according to international recommendations. Diagnostic work-up, treatment, and follow-up was done at one tertiary care, academic breast-center (outcome: disease specific survival/DSS vs. disease specific death/DSD). The Nottingham Prognostic Index (NPI) was used as the reference method with which to predict survival of breast cancer. Based on the MRI scans, VAV was accomplished by commercially available, FDA-cleared software. DSD served as endpoint. Integration of VAV into the NPI gave NPI VAV . Prediction of DSD by NPI VAV compared to standard NPI alone was investigated (Cox regression, likelihood-test, predictive accuracy: Harrell's C, Kaplan Meier statistics and corresponding hazard ratios/HR, confidence intervals/CI). DSD occurred in 35 and DSS in 279 patients. Prognostication of the survival outcome by NPI (Harrell's C = 75.3%) was enhanced by VAV (NPI VAV : Harrell's C = 81.0%). Most of all, the NPI VAV identified patients with unfavourable outcome more reliably than NPI alone (hazard ratio/HR = 4.5; confidence interval/CI = 2.14-9.58; P = 0.0001). Automated volumetric radiomic analysis of breast cancer vascularization improved survival prediction in primary breast cancer. Most of all, it optimized the identification of patients at higher risk of an unfavorable outcome. Future studies should integrate MRI as a "gate keeper" in the management of breast cancer patients. Such a "gate keeper" could assist in selecting patients benefitting from more advanced diagnostic procedures (genetic profiling etc.) in order to decide whether are a more aggressive therapy (chemotherapy) is warranted.

We investigated whether the integration of machine learning (ML) into MRI interpretation can provide accurate decision rules for the management of suspicious breast masses. A total of 173 consecutive patients with suspicious breast masses upon complementary assessment (BI-RADS IV/V: n = 100/76) received standardized breast MRI prior to histological verification. MRI findings were independently assessed by two observers (R1/R2: 5 years of experience/no experience in breast MRI) using six (semi-)quantitative imaging parameters. Interobserver variability was studied by ICC (intraclass correlation coefficient). A polynomial kernel function support vector machine was trained to differentiate between benign and malignant lesions based on the six imaging parameters and patient age. Ten-fold cross-validation was applied to prevent overfitting. Overall diagnostic accuracy and decision rules (rule-out criteria) to accurately exclude malignancy were evaluated. Results were integrated into a web application and published online. Malignant lesions were present in 107 patients (60.8%). Imaging features showed excellent interobserver variability (ICC: 0.81-0.98) with variable diagnostic accuracy (AUC: 0.65-0.82). Overall performance of the ML algorithm was high (AUC = 90.1%; BI-RADS IV: AUC = 91.6%). The ML algorithm provided decision rules to accurately rule-out malignancy with a false negative rate <1% in 31.3% of the BI-RADS IV cases. Thus, integration of ML into MRI interpretation can provide objective and accurate decision rules for the management of suspicious breast masses, and could help to reduce the number of potentially unnecessary biopsies., Competing Interests: The authors of this manuscript declare relationships with the following companies: Michael Uder is on the speakers’ bureau for Bracco, Medtronic, Siemens and Bayer Schering. Rolf Janka is on the speakers’ bureau for Bracco. Tobias Bäuerle is on the speakers’ bureau for Bracco and Boehringer Ingelheim. For the remaining authors no potential conflicts of interest were declared. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: We aimed to investigate an automated semi-quantitative software as an imaging biomarker for the prediction of tissue response (TR) after completion of neoadjuvant chemotherapy (NAC)., Methods: Breast magnetic resonance imaging (MRI) (1.5T, protocol according to international recommendations) of 67 patients with biopsy-proven invasive breast cancer were examined before and after NAC. After completion of NAC, histopathologic assessments of TR were classified according to the Chevallier grading system (CG1/4: full/non-responder; CG2/C3: partial responder). A commercially available fully automatic software (CADstream) extracted MRI parameters of tumor extension (tumor diameter/volume: TD/TV). Pre- versus post-NAC values were compared (ΔTV and ΔTD). Additionally, the software performed volumetric analyses of vascularization (VAV) after NAC. Accuracy of MRI parameters to predict TR were identified (cross-tabs, ROC, AUC, Kruskal-Wallis)., Results: There were 37 (34.3%) CG1, 7 (6.5%) CG2, 53 (49.1%) CG3, and 11 (10.2%) CG4 lesions. The software reached area under the curve levels of 79.5% (CG1/complete response: ΔTD), 68.6% (CG2, CG3/partial response: VAV), and 88.8% to predict TR (CG4/non-response: ΔTV)., Conclusion: Semi-quantitative automated analysis of breast MRI data enabled the prediction of tissue response to NAC.

Rationale and Objectives: This study compared a novel photon-counting breast computed tomography (pcBCT) system with digital mammography (DM) and digital breast tomosynthesis (DBT) systems. For this reason, surgical specimens were examined with all three techniques and rated by three observers., Materials and Methods: A total of 30 surgical specimens were investigated with DM, DBT, and pcBCT; the associated images were shown to three experienced radiologists. Findings (22 microcalcifications and 23 mass lesions) were recorded and compared to the results of the pathological examination. Sensitivity and specificity for detection of microcalcifications and lesions were calculated and displayed using receiver operating characteristic curves., Results: Sensitivity for microcalcifications was 82% for DM, 70% for DBT, and 85% for pcBCT. Specificity for microcalcifications was 71% for DM, 75% for DBT, and 83% for pcBCT. Sensitivity for lesions was 45% for DM, 62% for DBT, and 65% for pcBCT. Specificity for lesions was 76% for DM, 62% for DBT, and 76% for pcBCT., Conclusions: pcBCT showed a comparable or superior performance compared to the clinically approved DM and DBT systems. Mass lesion detectability can be increased further by the use of contrast media., (Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: Early detection of loco-regional breast cancer recurrence improves patients' overall survival, as treatment can be initiated or active treatment can be changed. If a suspicious lymph node is diagnosed during a follow-up exam, surgical excision is often performed. The aim of this study was to evaluate the diagnostic performance of the minor invasive ultrasound-guided fine-needle aspiration cytology (FNAC) in sonomorphologically suspicious lymph nodes in breast cancer follow-up., Methods: Between April 2010 and November 2012, we performed ultrasound-guided FNAC in 38 sonographically suspicious lymph nodes of 37 breast cancer follow-up patients. Cytological specimens were evaluated if the sample material was sufficient for diagnosis and if they contained cancer cells. Patients with negative cytology were followed up clinically and sonographically. To evaluate the diagnostic performance we calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for physical examination, the different sonomorphological malignancy criteria and FNAC., Results: In 36/38 (94.7 %) lymph nodes, the pathologist had enough material to establish a final diagnosis; in 2/38 (5.3 %) lymph nodes, the probe material was non-evaluable during cytology, these 2 were excluded from further statistical evaluation. Cytology revealed malignancy in 21 lymph nodes and showed no evidence for malignancy in 15 lymph nodes. There was no evidence for malignant disease in follow-up exams in the 15 cytologically benign lymph nodes with an average follow-up time of 3 years. The diagnostic performances of physical examination and FNAC were: Sensitivity 52/100 %, specificity 88/100 %, PPV 85/100 %, NPV 60/100 %, respectively., Conclusions: Our preliminary results show that FNAC is a safe and fast diagnostic approach for the evaluation of suspicious lymph nodes in the follow-up of patients with breast cancer and, thus, together with follow-up represents a feasible alternative to surgery.

Various applications require information on breast parameters, such as breast length and volume. An optical system was designed and tested for measuring these parameters with subjects in a prone position. The study results were used for optimizing patient positioning and handling for a future breast computed tomography (BCT) system. Measurements were conducted using an optical measurement system. To test the functionality and accuracy of the system, measurements were performed using reference phantoms. Additionally, 20 women and 5 men were examined to calculate breast parameters in alternative positions and breathing states. The results of the optical measurements were compared with magnetic resonance imaging (MRI) measurements. Volume and length of the reference phantoms were determined with errors below 2%. The patient study demonstrated a mean breast volume of 530.7 ml for women during normal breathing. During an exhalation state, breast volume increased significantly by 17.7 ml in comparison with normal breathing. Differences with MRI measurements were found to be 3% for breast length and 9% for breast volume on average. The proposed optical measurement system was found to be suitable for measuring the dimensional parameters of the breast in a prone position and provides a tool for evaluating breast coverage for BCT.

Aim: To analyze how patients experience Stereotactic guided vacuum-assisted breast biopsy (VABB) both physically and mentally., Patients and Methods: Two hundred and eleven consecutive women underwent VABB using one of two different biopsy devices (ATEC® and Mammotome®). Patients were queried using a questionnaire., Results: One hundred and eighty-nine patients were included. 90% would again prefer VABB over a surgical biopsy. Average grading for the condition during the procedure was 2.5 (very good to good) and 2.1 (very good) for the condition the week following VABB. Minor complications were mentioned in 37%. (>90% pain and hematoma). 97% of the women were satisfied by the cosmetic results. Patients with malignant histology and younger age experienced the procedure significantly worse. A significant higher rate of minor complications was found in younger patients and in the ATEC® group., Conclusion: VABB is a physical and mental stressor to the women. Nonetheless, the majority of women indeed prefer the VABB., (Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.)

Mastectomy specimens were investigated using a Talbot-Lau X-ray imaging set-up. Significant structures in the darkfield were observed, which revealed considerably higher contrast than those observed in digital mammography. Comparison with the histomorphometric image proofs that the darkfield signal correlates with a tumor region containing small calcification grains of 3 to 30μm size., (Copyright © 2013. Published by Elsevier GmbH.)

Aim: The aim of this prospective clinical study was to assess whether it would be possible to reduce the rate of re-excisions and improve the quality using CMOS technology or digital breast tomosynthesis (DBT) compared to a conventional FFDM system. Material and Methods: An invasive breast cancer (BI-RADS 5) was diagnosed in 200 patients in the period from 5/2011 to 1/2012. After histological verification, a breast-conserving therapy was performed with intraoperative imaging. Three different imaging systems were used: 1) Inspiration™ (Siemens, Erlangen, Germany), amorphous selenium, tungsten source, focus 0.1 mm, resolution 85 µm pixel pitch, 8 l/mm as the standard; 2) BioVision™ (Bioptics, Tucson, USA), flat panel photodiode array, tungsten source, focus 0.05, resolution 50 µm pixel pitch, 12 l/mm; 3) Tomosynthesis (Siemens, Erlangen, Germany), amorphous selenium, tungsten source, focus 0.1 mm, resolution 85 µm pixel pitch, 8 l/mm, range: 50°, 25 projections, scan time > 20 s, geometry: uniform scanning, reconstruction: filtered back projection. The 600 radiograms were prospectively shown to 3 radiologists. Results: Out of a total of 200 patients with histologically confirmed breast cancer (BI-RADS 6) 156 patients required no further operative therapy (re-excision) after breast-conserving therapy. A retrospective analysis (n = 44) showed an increase in sensitivity with tomosynthesis compared to the BioVision™ (CMOS technology) and the Inspiration™ at a magnification of 1.0 : 1.0 of 8 % (p < 0.05), i.e. re-excision would not have been necessary in 16 patients with tomosynthesis. Conclusions: The sensitivity of tomosynthesis for intraoperative radiography is significantly (p < 0.05) higher compared to both CMOS technology and an FFDM system with a conventional detector. Additional studies using higher magnification, e.g. 2.0 : 1.0, but no zooming will be necessary to evaluate the method further.

Introduction: MicroRNAs (miRNAs, miRs) are a class of small, non-coding RNA molecules with relevance as regulators of gene expression thereby affecting crucial processes in cancer development. MiRNAs offer great potential as biomarkers for cancer detection due to their remarkable stability in blood and their characteristic expression in many different diseases. We investigated whether microarray-based miRNA profiling on whole blood could discriminate between early stage breast cancer patients and healthy controls., Methods: We performed microarray-based miRNA profiling on whole blood of 48 early stage breast cancer patients at diagnosis along with 57 healthy individuals as controls. This was followed by a real-time semi-quantitative Polymerase Chain Reaction (RT-qPCR) validation in a separate cohort of 24 early stage breast cancer patients from a breast cancer screening unit and 24 age matched controls using two differentially expressed miRNAs (miR-202, miR-718)., Results: Using the significance level of p<0.05, we found that 59 miRNAs were differentially expressed in whole blood of early stage breast cancer patients compared to healthy controls. 13 significantly up-regulated miRNAs and 46 significantly down-regulated miRNAs in our microarray panel of 1100 miRNAs and miRNA star sequences could be detected. A set of 240 miRNAs that was evaluated by radial basis function kernel support vector machines and 10-fold cross validation yielded a specificity of 78.8%, and a sensitivity of 92.5%, as well as an accuracy of 85.6%. Two miRNAs were validated by RT-qPCR in an independent cohort. The relative fold changes of the RT-qPCR validation were in line with the microarray data for both miRNAs, and statistically significant differences in miRNA-expression were found for miR-202., Conclusions: MiRNA profiling in whole blood has potential as a novel method for early stage breast cancer detection, but there are still challenges that need to be addressed to establish these new biomarkers in clinical use.

Aim: A prospective clinical study was done to see whether it is possible to reduce the rate of re-excisions using digital breast tomosynthesis (DBT) compared commercial FFDM. Material and Method: Between 1/2011 and 5/2011 we diagnosed an invasive breast cancer (BI-RADS 5) in 100 patients. After histological verification we performed breast-conserving therapy with intraoperative imaging using one of 2 different systems: 1. Tomosynthesis (Siemens, Erlangen, Germany), amorphous selenium, Tungsten source, focus 0.1 mm, resolution 85 µm, pixel pitch, 8 l/mm, range: 50°, 25 projections, time for scanning > 20 s, geometry: same scanning scope, reconstruction: filtered back projection; or 2. Inspiration™ (Siemens, Erlangen, Germany), amorphous selenium, tungsten source, focus 0.1 mm, resolution 85 µm, pixel pitch, 8 l/mm as the standard. The 100 radiograms obtained with both systems were prospectively shown on a monitor to 3 radiologists. Results: Out of a total of 100 patients with histologically proven breast cancer (BI-RADS 6) no re-excision was necessary in 78 patients. A retrospective analysis (n = 22) demonstrated an increase in sensitivity of tomosynthesis compared to the Inspiration™ at a magnification of 1.0 : 1.0 of 8 % (p < 0.05), i.e., in 8 patients re-excision would not have been necessary with tomosynthesis. Conclusion: Tomosynthesis has a significant higher sensitivity (p < 0.05) compared with a commercial FFDM system. Studies with higher numbers of patients will be necessary to evaluate this method.

Rationale and Objectives: Comparison of two different diffusion weighted (DW) sequences in breast MRI regarding the differentiation between benign and malignant lesions., Materials and Methods: Breast MRI including two different DW sequences was performed in 165 consecutive women. Inclusion criteria for DW imaging and ADC evaluation were histologically proven focal mass lesions with a diameter of more than 5 mm in dynamic contrast-enhanced MRI. The DW sequences were pre-contrast echo-planar imaging with spectral fat saturation (EPI fs) and DW EPI with inversion recovery (EPI STIR) (b-values: 50, 400, and 800). Lesions were analyzed regarding visibility in DW sequences and ADC values., Results: Inclusion criteria were fulfilled in 56 women with 69 lesions. Five lesions could not be evaluated for different reasons. Finally, DW sequences were evaluated in 51 women with 64 focal mass lesions (15 benign, 49 malignant). The visibility of the lesions was significantly better in the EPI fs sequence (P<0.05). The ADC values (10(-3) mm(2)/s) in the EPI fs were 1.76, 2.58, and 1.21 (mean, maximum, minimum, respectively) for benign lesions and 0.90, 1.19, and 0.34 for malignant lesions. Respective values in the EPI STIR sequence were 1.92, 3.20, 1.10, and 0.91, 1.43, 0.35. Only in the EPI fs sequence there was no overlap in ADC values between benign and malignant lesions., Conclusion: The DW MRI of the breast with EPI fs and EPI STIR sequences has a high potential to differentiate between benign and malignant breast lesions. Due to better lesion visibility and selectivity, the EPI fs sequence should be preferred.

Background: Multislice spiral computed tomography (MSCT) with retrospectively ECG-gated image reconstruction permits coronary artery visualization. We investigated the method's ability to identify high-grade coronary artery stenoses and occlusions., Methods and Results: A total of 64 consecutive patients were studied by MSCT (4x1 mm cross-sections, 500-ms rotation, table feed 1.5 mm/rotation, intravenous contrast agent, retrospectively ECG-gated image reconstruction). All coronary arteries and side branches with a luminal diameter >/=2.0 mm were assessed concerning evaluability and the presence of high-grade stenoses (>70% diameter stenosis) or occlusions. Results were compared with quantitative coronary angiography. Of 256 coronary arteries (left main, left anterior descending, left circumflex and right coronary artery, including their respective side branches), 174 could be evaluated (68%). In 19 patients (30%), all arteries were evaluable. Artifacts caused by coronary motion were the most frequent reason for unevaluable arteries. Overall, 32 of 58 high-grade stenoses and occlusions were detected by MSCT (58%). In evaluable arteries, 32 of 35 lesions were detected, and the absence of stenosis was correctly identified in 117 of 139 arteries (sensitivity, 91%; specificity, 84%). If analysis was extended to all stenoses with >50% diameter reduction, sensitivity was 85% (40 of 47) and specificity was 76% (96 of 127)., Conclusions: MSCT with retrospective ECG gating permits the detection of coronary artery stenoses with high accuracy if image quality is sufficient, but its clinical use may presently be limited due to degraded image quality in a substantial number of cases, mainly due to rapid coronary motion.