ESR Essentials: screening for breast cancer - general recommendations by EUSOBI.

Breast cancer is the most frequently diagnosed cancer in women accounting for about 30% of all new cancer cases and the incidence is constantly increasing. Implementation of mammographic screening has contributed to a reduction in breast cancer mortality of at least 20% over the last 30 years. Screening programs usually include all women irrespective of their risk of developing breast cancer and with age being the only determining factor. This approach has some recognized limitations, including underdiagnosis, false positive cases, and overdiagnosis. Indeed, breast cancer remains a major cause of cancer-related deaths in women undergoing cancer screening. Supplemental imaging modalities, including digital breast tomosynthesis, ultrasound, breast MRI, and, more recently, contrast-enhanced mammography, are available and have already shown potential to further increase the diagnostic performances. Use of breast MRI is recommended in high-risk women and women with extremely dense breasts. Artificial intelligence has also shown promising results to support risk categorization and interval cancer reduction. The implementation of a risk-stratified approach instead of a "one-size-fits-all" approach may help to improve the benefit-to-harm ratio as well as the cost-effectiveness of breast cancer screening. KEY POINTS: Regular mammography should still be considered the mainstay of the breast cancer screening. High-risk women and women with extremely dense breast tissue should use MRI for supplemental screening or US if MRI is not available. Women need to participate actively in the decision to undergo personalized screening. KEY RECOMMENDATIONS: Mammography is an effective imaging tool to diagnose breast cancer in an early stage and to reduce breast cancer mortality (evidence level I). Until more evidence is available to move to a personalized approach, regular mammography should be considered the mainstay of the breast cancer screening. High-risk women should start screening earlier; first with yearly breast MRI which can be supplemented by yearly or biennial mammography starting at 35-40 years old (evidence level I). Breast MRI screening should be also offered to women with extremely dense breasts (evidence level I). If MRI is not available, ultrasound can be performed as an alternative, although the added value of supplemental ultrasound regarding cancer detection remains limited. Individual screening recommendations should be made through a shared decision-making process between women and physicians.

Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI.

OBJECTIVES: Development of automated segmentation models enabling standardized volumetric quantification of fibroglandular tissue (FGT) from native volumes and background parenchymal enhancement (BPE) from subtraction volumes of dynamic contrast-enhanced breast MRI. Subsequent assessment of the developed models in the context of FGT and BPE Breast Imaging Reporting and Data System (BI-RADS)-compliant classification. METHODS: For the training and validation of attention U-Net models, data coming from a single 3.0-T scanner was used. For testing, additional data from 1.5-T scanner and data acquired in a different institution with a 3.0-T scanner was utilized. The developed models were used to quantify the amount of FGT and BPE in 80 DCE-MRI examinations, and a correlation between these volumetric measures and the classes assigned by radiologists was performed. RESULTS: To assess the model performance using application-relevant metrics, the correlation between the volumes of breast, FGT, and BPE calculated from ground truth masks and predicted masks was checked. Pearson correlation coefficients ranging from 0.963 ± 0.004 to 0.999 ± 0.001 were achieved. The Spearman correlation coefficient for the quantitative and qualitative assessment, i.e., classification by radiologist, of FGT amounted to 0.70 (p < 0.0001), whereas BPE amounted to 0.37 (p = 0.0006). CONCLUSIONS: Generalizable algorithms for FGT and BPE segmentation were developed and tested. Our results suggest that when assessing FGT, it is sufficient to use volumetric measures alone. However, for the evaluation of BPE, additional models considering voxels' intensity distribution and morphology are required. CRITICAL RELEVANCE STATEMENT: A standardized assessment of FGT density can rely on volumetric measures, whereas in the case of BPE, the volumetric measures constitute, along with voxels' intensity distribution and morphology, an important factor. KEY POINTS: • Our work contributes to the standardization of FGT and BPE assessment. • Attention U-Net can reliably segment intricately shaped FGT and BPE structures. • The developed models were robust to domain shift.

Axillary lymph nodes enlargement after Sars-CoV-2 vaccine in patients undergoing breast examination: a single-centre experience in 285 women.

PURPOSE: To assess the incidence of axillary lymphadenopathy over established time ranges after COVID-19 vaccination and lymph node pathologic features (i.e. size increase and qualitative characteristics) in subjects undergoing axillary evaluation during a breast imaging examination. METHODS AND MATERIALS: The institutional review board approved this prospective study. INCLUSION CRITERIA: women undergoing mammography and breast ultrasound between July and October 2021; information about the COVID-19 vaccine and infection, if any. EXCLUSION CRITERIA: known metastatic lymphadenopathy. Participants were divided into 5 subgroups according to time between vaccine and imaging: < 6 weeks; 7-8 weeks; 9-10 weeks; 11-12 weeks; > 12 weeks. Evaluation of axillary lymph nodes was performed with ultrasound. Descriptive statistical analysis was performed. p < 0.05 was considered significant. RESULTS: A total of 285 women were included. Most of the patients underwent Moderna vaccine (n = 175, 61.4%). 63/285 patients had a previous history of breast cancer (22.1%). 13/17 (76.5%) patients with previous COVID-19 infection had no previous history of cancer, whereas 4/17 had a previous history of cancer (p < .001). 41/285 (14.4%) women showed lymphadenopathy, and they were significantly younger (46.9 ± 11.6 years) than women with borderline (54.0 ± 11.9 years) or no lymphadenopathy (57.3 ± 11.9 years) (p < .001). Lymphadenopathy and borderline lymphadenopathy were more frequently observed in the Moderna-vaccinated women and in the subgroup of patients evaluated < 6 weeks after vaccination (p < 0.001). The most common pathologic feature was cortical thickening, followed by complete or partial effacement of fatty hilum. CONCLUSION: A lymphadenopathy within 12 weeks after vaccination is a common finding particularly in younger women and after Moderna vaccine and no further assessment should be required.

Mammographic calcifications undergoing percutaneous biopsy: outcome in women with and without a personal history of breast cancer.

PURPOSE: To compare the positive predictive values (PPVs) of BI-RADS categories used to assess pure mammographic calcifications in women with and without a previous history of breast cancer (PHBC). MATERIALS AND METHODS: In this retrospective study, all consecutive pure mammographic calcifications (n = 320) undergoing a stereotactic biopsy between 2016 and 2018 were identified. Mammograms were evaluated in consensus by two radiologists according to BI-RADS and blinded to patient history and pathology results. Final pathologic results were used as the standard of reference. PPV of BI-RADS categories were compared between the two groups. Data were evaluated using standard statistics, Mann-Whitney U tests and Chi-square tests. RESULTS: Two hundred sixty-eight patients (274 lesions, median age 54 years, inter-quartile range, 50-65 years) with a PHBC (n = 46) and without a PHBC (n = 222) were included. Overall PPVs were the following: BI-RADS 2, 0% (0 of 56); BI-RADS 3, 9.1% (1 of 11); BI-RADS 4a, 16.2% (6 of 37); BI-RADS 4b, 37.5% (48 of 128); BI-RADS 4c, 47.3% (18 of 38) and BI-RADS 5, 100% (4 of 4). The PPV of BI-RADS categories was similar in patients with and without a PHBC (P = .715). Calcifications were more often malignant in patients with a PHBC older than 10 years (47.3%, 9 of 19) compared to 1-2 years (25%, 1 of 4), 2-5 years (20%, 2 of 10) and 5-10 years (0%, of 13) from the first breast cancer (P = .005). CONCLUSION: PPV of mammographic calcifications is similar in women with or without PHBC when BI-RADS classification is strictly applied. A higher risk of malignancy was observed in patients with a PHBC longer than 10 years.

Dedicated breast computed-tomography in women with a personal history of breast cancer: A proof-of-concept study.

PURPOSE: To compare the subjective image quality assessment using B-CT and digital mammography in women with personal history of breast cancer (PHBC). METHOD: In this retrospective study 32 patients with PHBC were included. Each patient had undergone a B-CT examination and a previous mammogram in a time interval of less than 18 months between the two examinations. Two radiologists evaluated the two examinations independently with regard to the presence of lesions, BI-RADS classification, level of confidence for the overall exam interpretation, scar evaluation and image quality including image degradation due to clip artifacts. Level of confidence and image quality were assessed using a 5-point Likert scale. A p-value of less than 0.01 was considered statistically significant. RESULTS: Thirty-seven operated and 27 non-operated breasts were included. Confidence for the overall interpretation with B-CT was equal or superior to mammography in 63 cases (98.4 %) for reader 1 and in 58 cases (90.6 %) for reader 2 (p <.001). Confidence for scar evaluation with B-CT was equal or superior to mammography in all cases for reader 1 and in 34 cases (91.9 %) for readers 2 (p <.001). One case with local recurrence in B-CT was identified by both readers and no false positive findings were reported. A moderate to high image degradation due to beam-hardening artifacts has been reported by both readers in 29.4 % of cases due to surgical clips in the B-CT volume. CONCLUSIONS: B-CT in patients with PHBC provides high quality images that can be evaluated with confidence equal or superior to mammography.

Clinical assessment of image quality, usability and patient comfort in dedicated spiral breast computed tomography.

OBJECTIVE: To investigate aspects of image quality, feasibility and patient comfort in dedicated spiral breast computed tomography (B-CT) in a large patient cohort. METHODS: This retrospective study was approved by the institutional review board. 2418 B-CT scans from 1222 women examined between 04/16/2019 and 04/13/2022 were analyzed. Patients evaluated their comfort during the examination, radiographers carrying out the scans evaluated the patient's mobility and usability of the B-CT device, whereas radiologists assessed lesion contrast, detectability of calcifications, breast coverage and overall image quality. For semi-quantitative assessment, a Likert-Scale was used and statistical significance and correlations were calculated using ANOVAs and Spearman tests. RESULTS: Comfort, mobility and usability of the B-CT were rated each with either "no" or "negligible" complaints in >99%. Image quality was rated with "no" or "negligible complaints" in 96.7%. Lesion contrast and detectability of calcifications were rated either "optimal" or "good" in 92.6% and 98.4%. "Complete" and "almost complete" breast coverage were reported in 41.9%, while the pectoral muscle was found not to be covered in 56.0%. Major parts of the breast were not covered in 2.1%. Some variables were significantly correlated, such as age with comfort (ρ = -0.168, p < .001) and mobility (ρ = -0.172, p < .001) as well as patient weight with lesion contrast (ρ = 0.172, p < .001) and breast coverage (ρ = -0.109, p < .001). CONCLUSIONS: B-CT provides high image quality and contrast of soft tissue lesions as well as calcifications, while covering the pre-pectoral areas of the breast remains challenging. B-CT is easy to operate for the radiographer and comfortable for the majority of women.

Fully automated breast segmentation on spiral breast computed tomography images.

INTRODUCTION: The quantification of the amount of the glandular tissue and breast density is important to assess breast cancer risk. Novel photon-counting breast computed tomography (CT) technology has the potential to quantify them. For accurate analysis, a dedicated method to segment the breast components-the adipose and glandular tissue, skin, pectoralis muscle, skinfold section, rib, and implant-is required. We propose a fully automated breast segmentation method for breast CT images. METHODS: The framework consists of four parts: (1) investigate, (2) segment the components excluding adipose and glandular tissue, (3) assess the breast density, and (4) iteratively segment the glandular tissue according to the estimated density. For the method, adapted seeded watershed and region growing algorithm were dedicatedly developed for the breast CT images and optimized on 68 breast images. The segmentation performance was qualitatively (five-point Likert scale) and quantitatively (Dice similarity coefficient [DSC] and difference coefficient [DC]) demonstrated according to human reading by experienced radiologists. RESULTS: The performance evaluation on each component and overall segmentation for 17 breast CT images resulted in DSCs ranging 0.90-0.97 and in DCs 0.01-0.08. The readers rated 4.5-4.8 (5 highest score) with an excellent inter-reader agreement. The breast density varied by 3.7%-7.1% when including mis-segmented muscle or skin. CONCLUSION: The automatic segmentation results coincided with the human expert's reading. The accurate segmentation is important to avoid the significant bias in breast density analysis. Our method enables accurate quantification of the breast density and amount of the glandular tissue that is directly related to breast cancer risk.

BI-RADS-Based Classification of Mammographic Soft Tissue Opacities Using a Deep Convolutional Neural Network.

The aim of this study was to investigate the potential of a machine learning algorithm to classify breast cancer solely by the presence of soft tissue opacities in mammograms, independent of other morphological features, using a deep convolutional neural network (dCNN). Soft tissue opacities were classified based on their radiological appearance using the ACR BI-RADS atlas. We included 1744 mammograms from 438 patients to create 7242 icons by manual labeling. The icons were sorted into three categories: "no opacities" (BI-RADS 1), "probably benign opacities" (BI-RADS 2/3) and "suspicious opacities" (BI-RADS 4/5). A dCNN was trained (70% of data), validated (20%) and finally tested (10%). A sliding window approach was applied to create colored probability maps for visual impression. Diagnostic performance of the dCNN was compared to human readout by experienced radiologists on a "real-world" dataset. The accuracies of the models on the test dataset ranged between 73.8% and 89.8%. Compared to human readout, our dCNN achieved a higher specificity (100%, 95% CI: 85.4-100%; reader 1: 86.2%, 95% CI: 67.4-95.5%; reader 2: 79.3%, 95% CI: 59.7-91.3%), and the sensitivity (84.0%, 95% CI: 63.9-95.5%) was lower than that of human readers (reader 1:88.0%, 95% CI: 67.4-95.4%; reader 2:88.0%, 95% CI: 67.7-96.8%). In conclusion, a dCNN can be used for the automatic detection as well as the standardized and observer-independent classification of soft tissue opacities in mammograms independent of the presence of microcalcifications. Human decision making in accordance with the BI-RADS classification can be mimicked by artificial intelligence.

Added value of combined acromiohumeral distance and critical shoulder angle measurements on conventional radiographs for the prediction of rotator cuff pathology.

Purpose: To investigate the role of acromiohumeral distance (AHD) and critical shoulder angle (CSA) measurements from conventional radiographs (CR) in isolation and combined (prognostic index PIAHD-CSA) as predictors of full thickness rotator cuff tendon tears (RCT) and critical fatty degeneration (CFD; i.e. as much fat as muscle). Method: In this retrospective study AHD and CSA were measured in 127 CR. MR arthrograms served as reference standard and were screened for RCT and CFD. Statistical analysis for inter-reader agreement, Spearman's rank correlation, linear stepwise regression and logistic regression for AHD and CSA with ROC analyses including PIAHD-CSA were performed. Results: In 90 subjects (17 females, mean age 36.1 ± 14.1) no RCT were found on MR imaging and served as control group. In 37 patients (13 females, mean age 58.7 ± 13.2) ≥ one RCT was found. Inter-reader agreements rated between к = 0.42-0.82 for categorical and 0.91-0.96 for continuous variables. No significant correlation of AHD and CSA with either age or sex was seen (p = 0.28 and p = 0.74, respectively). Case group had significantly smaller mean AHD (8.7 ± 3.2 vs. 10.8 ± 2.2 mm; p < 0.001) and larger mean CSA (36.5 ± 4.5° vs. 33.1 ± 4.0°; p < 0.001). PIAHD-CSA increased diagnostic performance for prediction of RCT and CFD (AUC = 0.78 and 0.71), compared to isolated AHD (0.74 and 0.71) and CSA (0.71 and 0.66). Conclusions: AHD and CSA do not depend on age or sex but differ significantly between healthy and pathologic rotator cuffs. A decreased AHD is most influenced by infraspinatus muscle atrophy and fatty degeneration. Combined PIAHD-CSA increases diagnostic performance for predicting RCT and CFD.

Contrast Media-Enhanced Breast Computed Tomography With a Photon-Counting Detector: Initial Experiences on In Vivo Image Quality and Correlation to Histology.

OBJECTIVES: The aim of this study was to investigate the feasibility, the image quality, and the correlation with histology of dedicated spiral breast computed tomography (B-CT) equipped with a photon-counting detector in patients with suspicious breast lesions after application of iodinated contrast media. MATERIALS AND METHODS: The local ethics committee approved this prospective study. Twelve women with suspicious breast lesions found in mammography or B-CT underwent contrast-enhanced spiral B-CT and supplementary ultrasound. For all lesions, biopsy-proven diagnosis and histological workup after surgical resection were obtained including the size of cancer/ductal carcinoma in situ, which were correlated to sizes measured in B-CT. Signal-to-noise ratio and contrast-to-noise ratio were evaluated for tumor, glandular tissue, and fatty tissue. RESULTS: Of the 12 patients, 15 suspicious lesions were found, 14 were malignant, and 1 benign lesion corresponded to a chronic inflammation. All lesions showed strong contrast media uptake with a signal-to-noise ratio of 119.7 ± 52.5 with a contrast-to-noise ratio between glandular tissue and breast cancer lesion of 12.6 ± 5.9. The correlation of the size of invasive tumors measured in B-CT compared with histological size was significant and strong R = 0.77 ( P < 0.05), whereas the correlation with the size of the peritumoral ductal carcinoma in situ was not significant R = 0.80 ( P = 0.11). CONCLUSIONS: Contrast-enhanced B-CT shows high contrast between breast cancer and surrounding glandular tissue; therefore, it is a promising technique for cancer detection and staging depicting both soft tissue lesions and microcalcifications, which might be a substantial advantage over breast MRI.

Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network.

PURPOSE: The aim of this study was to develop and test a post-processing technique for detection and classification of lesions according to the BI-RADS atlas in automated breast ultrasound (ABUS) based on deep convolutional neural networks (dCNNs). METHODS AND MATERIALS: In this retrospective study, 645 ABUS datasets from 113 patients were included; 55 patients had lesions classified as high malignancy probability. Lesions were categorized in BI-RADS 2 (no suspicion of malignancy), BI-RADS 3 (probability of malignancy < 3%), and BI-RADS 4/5 (probability of malignancy > 3%). A deep convolutional neural network was trained after data augmentation with images of lesions and normal breast tissue, and a sliding-window approach for lesion detection was implemented. The algorithm was applied to a test dataset containing 128 images and performance was compared with readings of 2 experienced radiologists. RESULTS: Results of calculations performed on single images showed accuracy of 79.7% and AUC of 0.91 [95% CI: 0.85-0.96] in categorization according to BI-RADS. Moderate agreement between dCNN and ground truth has been achieved (κ: 0.57 [95% CI: 0.50-0.64]) what is comparable with human readers. Analysis of whole dataset improved categorization accuracy to 90.9% and AUC of 0.91 [95% CI: 0.77-1.00], while achieving almost perfect agreement with ground truth (κ: 0.82 [95% CI: 0.69-0.95]), performing on par with human readers. Furthermore, the object localization technique allowed the detection of lesion position slice-wise. CONCLUSIONS: Our results show that a dCNN can be trained to detect and distinguish lesions in ABUS according to the BI-RADS classification with similar accuracy as experienced radiologists. KEY POINTS: • A deep convolutional neural network (dCNN) was trained for classification of ABUS lesions according to the BI-RADS atlas. • A sliding-window approach allows accurate automatic detection and classification of lesions in ABUS examinations.

Dual energy CT arthrography in shoulder instability: successful iodine removal with virtual non-contrast images and accurate 3D reformats of the glenoid for assessment of bone loss.

OBJECTIVE: To evaluate the image quality of dual energy CT (DECT) of the shoulder after arthrography and of virtual non-contrast (VNC) 3D reformats of the glenoid and to compare glenoid measurements on VNC 3D reformats and on 2D CTs. MATERIALS AND METHODS: DECT arthrography (80 kV/140 kV) was performed in 42 shoulders of 41 patients with instability using diluted iodinated contrast media (80 mg/ml). VNC images and VNC 3D reformats of the glenoid were calculated using image postprocessing. Dose parameters, CT values of intraarticular iodine and muscle, image contrast (iodine/muscle), and image quality (5-point scale: 1 = worst, 5 = best) were evaluated. Two independent readers assessed glenoid morphology and performed glenoid measurements on 2D and 3D images. RESULTS: Calculation of VNC images and VNC 3D reformats was successful in 42/42 shoulders (100%). The effective dose was mean 1.95 mSv (± 0.9 mSv). CT values of iodine and muscle were mean 1014.6 HU (± 235.8 HU) and 64.5 HU(± 8.6 HU), respectively, and image contrast was mean 950.2 HU (± 235.5 HU). Quality of cross-sectional images, VNC images, and VNC 3D reformats was rated good (median 4 (4-5), 4 (3-4), 4 (3-5), respectively). Detection of an osseous defect was equal on 2D and 3D images (13/42, P > 0.99) with no difference for measurement of the glenoid diameter with mean 28.3 mm (± 2.8 mm) vs. 28.4 mm (± 2.9 mm) (P = 0.5), width of the glenoid defect with 3.2 mm (± 2.1 mm) vs. 3.1 mm (± 2.3 mm) (P = 0.84), surface area with 638.5 mm2 (± 127 mm2) vs. 640.8 mm2 (± 129.5 mm2) (P = 0.47), and surface area of the defect with 46.6 mm2 (± 44.3 mm2) vs. 47.2 mm2 (± 48.0 mm2) (P = 0.73), respectively. CONCLUSION: DECT shoulder arthrography is feasible and allows successful iodine removal with generation of VNC images and accurate VNC 3D reformats of the glenoid for assessment of bone loss.

Breast Computed Tomography: Diagnostic Performance of the Maximum Intensity Projection Reformations as a Stand-Alone Method for the Detection and Characterization of Breast Findings.

OBJECTIVES: This study aimed to evaluate the diagnostic performance of the maximum intensity projection (MIP) reformations of breast computed tomography (B-CT) images as a stand-alone method for the detection and characterization of breast imaging findings. MATERIALS AND METHODS: A total of 160 women undergoing B-CT between August 2018 and December 2020 were retrospectively included; 80 patients with known breast imaging findings were matched with 80 patients without imaging findings according to age and amount of fibroglandular tissue (FGT). A total of 71 benign and 9 malignant lesions were included. Images were evaluated using 15-mm MIP in 3 planes by 2 radiologists with experience in B-CT. The presence of lesions and FGT were evaluated, using the BI-RADS classification. Interreader agreement and descriptive statistics were calculated. RESULTS: The interreader agreement of the 2 readers for finding a lesion (benign or malignant) was 0.86 and for rating according to BI-RADS classification was 0.82. One of 9 cancers (11.1%) was missed by both readers due to dense breast tissue. BI-RADS 1 was correctly applied to 73 of 80 patients (91.3%) by reader 1 and to 74 of 80 patients (92.5%) by reader 2 without recognizable lesions. BI-RADS 2 or higher with a lesion in at least one of the breasts was correctly applied in 69 of 80 patients (86.3%) by both readers. For finding a malignant lesion, sensitivity was 88.9% (95% confidence interval [CI], 51.75%-99.72%) for both readers, and specificity was 99.3% (95% CI, 96.4%-100%) for reader 1 and 100% (95% CI, 97.20%-100.00%) for reader 2. CONCLUSIONS: Evaluation of B-CT images using the MIP reformations may help to reduce the reading time with high diagnostic performance and confidence.

Breast density in dedicated breast computed tomography: Proposal of a classification system and interreader reliability.

ABSTRACT: The aim of this study was to develop a new breast density classification system for dedicated breast computed tomography (BCT) based on lesion detectability analogous to the ACR BI-RADS breast density scale for mammography, and to evaluate its interrater reliability.In this retrospective study, 1454 BCT examinations without contrast media were screened for suitability. Excluding datasets without additional ultrasound and exams without any detected lesions resulted in 114 BCT examinations. Based on lesion detectability, an atlas-based BCT density (BCTD) classification system of breast parenchyma was defined using 4 categories. Interrater reliability was examined in 40 BCT datasets between 3 experienced radiologists.Among the included lesions were 63 cysts (55%), 18 fibroadenomas (16%), 7 lesions of fatty necrosis (6%), and 6 breast cancers (5%) with a median diameter of 11 mm. X-ray absorption was identical between lesions and breast tissue; therefore, the lack of fatty septae was identified as the most important criteria for the presence of lesions in glandular tissue. Applying a lesion diameter of 10 mm as desired cut-off for the recommendation of an additional ultrasound, an atlas of 4 BCTD categories was defined resulting in a distribution of 17.5% for density A, 39.5% (B), 31.6% (C), and 11.4% (D) with an intraclass correlation coefficient (ICC) among 3 readers of 0.85 to 0.87.We propose a dedicated atlas-based BCTD classification system, which is calibrated to lesion detectability. The new classification system exhibits a high interrater reliability and may be used for the decision whether additional ultrasound is recommended.

Tibial torsion analysis in computed tomography: development and validation of a real 3D measurement technique.

PURPOSE: Pathological tibial torsion is known to negatively influence the functionality of the lower extremity, and therefore, its assessment might play an important role. While 3D imaging is used for many examinations of the musculoskeletal system, for the determination of tibial torsion no 3D measurement technique has been available so far. We developed a 3D measurement method and assess its interobserver reliability as well as its correlation with standard 2D measurement methods. METHODS: CT scans of 82 tibiae in 79 patients with a mean age of 41 years were included. A novel 3D measurement technique was developed and applied. Measurements were compared with two frequently used 2D measurement methods. ICC (intraclass correlation coefficient) for the new technique was determined and compared to the 2D measurement method. Furthermore, differences between left and right legs as well as between males and females were assessed. RESULTS: The ICC for the 2D methods was 0.917 and 0.938, respectively. For the 3D measurements, ICCs were calculated to be 0.954 and 0.950. Agreement between 2 and 3D methods was moderate to good with ICCs between 0.715 and 0.795. Torsion values for left and right legs did not differ significantly in 2D and in 3D (26.2 vs 28.5° and 27.2 vs. 25.9°). The same is true for the differences between male and female in 2D and 3D (26.2 vs. 29.6° and 25.0 vs. 31.2°). CONCLUSION: The newly developed 3D measurement technique shows a high intraclass agreement and offers an applicable opportunity to assess the tibial torsion three-dimensionally.

Classification of Mammographic Breast Microcalcifications Using a Deep Convolutional Neural Network: A BI-RADS-Based Approach.

MATERIALS AND METHODS: Over 56,000 images of 268 mammograms from 94 patients were labeled to 3 classes according to the BI-RADS standard: "no microcalcifications" (BI-RADS 1), "probably benign microcalcifications" (BI-RADS 2/3), and "suspicious microcalcifications" (BI-RADS 4/5). Using the preprocessed images, a dCNN was trained and validated, generating 3 types of models: BI-RADS 4 cohort, BI-RADS 5 cohort, and BI-RADS 4 + 5 cohort. For the final validation of the trained dCNN models, a test data set consisting of 141 images of 51 mammograms from 26 patients labeled according to the corresponding BI-RADS classification from the radiological reports was applied. The performances of the dCNN models were evaluated, classifying each of the mammograms and computing the accuracy in comparison to the classification from the radiological reports. For visualization, probability maps of the classification were generated. RESULTS: The accuracy on the validation set after 130 epochs was 99.5% for the BI-RADS 4 cohort, 99.6% for the BI-RADS 5 cohort, and 98.1% for the BI-RADS 4 + 5 cohort. Confusion matrices of the "real-world" test data set for the 3 cohorts were generated where the radiological reports served as ground truth. The resulting accuracy was 39.0% for the BI-RADS 4 cohort, 80.9% for BI-RADS 5 cohort, and 76.6% for BI-RADS 4 + 5 cohort. The probability maps exhibited excellent image quality with correct classification of microcalcification distribution. CONCLUSIONS: The dCNNs can be trained to successfully classify microcalcifications on mammograms according to the BI-RADS classification system in order to act as a standardized quality control tool providing the expertise of a team of radiologists.

Diagnostic value of a spiral breast computed tomography system equipped with photon counting detector technology in patients with implants: An observational study of our initial experiences.

To evaluate the value of a breast computed tomography (CT) (B-CT) in assessing breast density, pathologies and implant integrity in women with breast implants.This retrospective study was approved by the local ethics committee. B-CT images of 21 women with implants (silicone/saline; 20 bilateral, 1 unilateral) who underwent opportunistic screening or diagnostic bilateral B-CT were included. Breast density, implant integrity, extensive capsular fibrosis, soft tissue lesions and micro-/macrocalcifications were rated. In 18 of the 21 women, an additional ultrasound and in two patients breast magnetic resonance imaging was available for comparison. The average dose was calculated for each breast using verified Monte Carlo simulations on 3D image data sets.Breast density was nearly completely fatty (ACR a) in two patients, scattered fibroglandular (ACR b) in five, heterogeneously dense (ACR c) in ten and very dense (ACR d) in four women. In three women showed a unilateral positive Linguine sign indicative of an inner capsule rupture. Extensive capsular fibrosis was found in three women. In three women, soft tissue lesions were depicted, which revealed to be cysts (n = 2) and lymph nodes (n = 1) on subsequent sonography. Diffuse, non-clustered microcalcifications were found in nine women. Eleven women showed cutaneous or intramammary macrocalcifications. Average dose was 6.45 mGy (range 5.81-7.28 mGy).In women with implants, B-CT presents a promising modality for evaluating breast density, implant integrity, extensive capsular fibrosis, soft tissue lesions and micro-/macrocalcifications without the need of breast compression utilizing a lower dose compared to doses reported for conventional four-view mammography.

Fully automatic classification of breast MRI background parenchymal enhancement using a transfer learning approach.

Marked enhancement of the fibroglandular tissue on contrast-enhanced breast magnetic resonance imaging (MRI) may affect lesion detection and classification and is suggested to be associated with higher risk of developing breast cancer. The background parenchymal enhancement (BPE) is qualitatively classified according to the BI-RADS atlas into the categories "minimal," "mild," "moderate," and "marked." The purpose of this study was to train a deep convolutional neural network (dCNN) for standardized and automatic classification of BPE categories.This IRB-approved retrospective study included 11,769 single MR images from 149 patients. The MR images were derived from the subtraction between the first post-contrast volume and the native T1-weighted images. A hierarchic approach was implemented relying on 2 dCNN models for detection of MR-slices imaging breast tissue and for BPE classification, respectively. Data annotation was performed by 2 board-certified radiologists. The consensus of the 2 radiologists was chosen as reference for BPE classification. The clinical performances of the single readers and of the dCNN were statistically compared using the quadratic Cohen's kappa.Slices depicting the breast were classified with training, validation, and real-world (test) accuracies of 98%, 96%, and 97%, respectively. Over the 4 classes, the BPE classification was reached with mean accuracies of 74% for training, 75% for the validation, and 75% for the real word dataset. As compared to the reference, the inter-reader reliabilities for the radiologists were 0.780 (reader 1) and 0.679 (reader 2). On the other hand, the reliability for the dCNN model was 0.815.Automatic classification of BPE can be performed with high accuracy and support the standardization of tissue classification in MRI.

Atypical ductal hyperplasia: breast DCE-MRI can be used to reduce unnecessary open surgical excision.

PURPOSE: To evaluate the diagnostic performance of dynamic contrast-enhanced (DCE)-MRI in predicting malignancy after percutaneous biopsy diagnosis of atypical ductal hyperplasia (ADH). METHODS AND MATERIALS: In this retrospective study, 68 lesions (66 women) with percutaneous biopsy diagnosis of ADH and pre-operative breast DCE-MRI performed between January 2016 and December 2017 were included. Two radiologists reviewed in consensus mammography, ultrasound, and MR images. The final diagnosis after surgical excision was used as standard of reference. Clinical and imaging features were compared in patients with and without upgrade to malignancy after surgery. The diagnostic performance of DCE-MRI in predicting malignant upgrade was evaluated. RESULTS: A 9-gauge vacuum-assisted biopsy was performed in 40 (58.8%) cases and a 14-gauge core needle biopsy in 28 (41.2%) cases. Upgrade to malignancy was observed in 17/68 (25%) lesions, including 4/17 (23.5%) cases of invasive cancer and 13/17 (76.5%) cases of ductal carcinoma in situ (DCIS). In 16/17 (94.1%) malignant and 20/51 (39.2%) benign lesions, a suspicious enhancement could be recognized in DCE-MRI. The malignant lesion without suspicious enhancement was a low-grade DCIS (4 mm size). Sensitivity, specificity, positive predictive value, and negative predictive value of DCE-MRI on predicting malignancy were respectively 94.1%, 60.7%, 44.4%, and 96.8%. No other clinical or imaging features were significantly different in patients with and without upgrade to malignancy. CONCLUSION: After a percutaneous biopsy diagnosis of ADH, malignancy can be ruled out in most of the cases, if no suspicious enhancement is present in the biopsy area at DCE-MRI. Breast DCE-MRI may be used to avoid surgery in more than half of the patients with final benign diagnosis. KEY POINTS: • Breast DCE-MRI can safely rule out malignancy if no suspicious enhancement is present in the biopsy area after a percutaneous biopsy diagnosis of ADH. • All cases of upgrade to high-grade DCIS and invasive cancers can be identified at breast DCE-MRI after a percutaneous biopsy diagnosis of ADH. • Breast DCE-MRI may be used to avoid surgery in more than half of the patients with final benign diagnosis.