Mediolateral Oblique Projections Research Articles

A survey on mean glandular dose in mammography examination and the factors affecting it in Shahid Sadoughi Hospital, Yazd, Iran.

mammography is the gold standard in the early detection of breast cancer. Due to the increase in the rate of women suffering from this malignancy all over the world, this imaging modality has been widely used. Considering the side effects caused by ionizing radiation to measure the carcinogenic risk of mammography X-rays, mean glandular dose (MGD) is the best parameter to evaluate the dose received by patients undergoing mammography. The aims of this study were to measure MGD in mammography in mammographic craniocaudal (CC) and mediolateral oblique (MLO) projections and investigate the relationship of MGD with compressed breast thickness (CBT), body mass index, age of the patient, and device exposure conditions. this cross-sectional analytical study was conducted on women aged 30-70 referring to the mammography unit of Shahid Sadoughi Hospital in Yazd city from May to August 2022. The TLD-GR 200 (thermoluminescence dosimeter) was placed on the breast of the patients for CC and MLO projections, and then the MGD was obtained by multiplying the entrance surface dose and the normalized glandular dose. Analysis of data (such as demographic information of patients, CBT, kVp, and mAs) was done by SPSS 23 software. The normality of the data was checked using Shapiro-Wilk tests. It was found that except for age, other variables did not have a normal distribution; therefore, equivalent parametric and nonparametric tests were used. In this regard, Spearman's correlation was used to assess the correlation between variables. P-value < .05 was considered as level of significance. The mean±standard deviation (SD) age of patients was 47.3±7.1years. The median±IQR (the interquartile range (IQR) is a measure of statistical dispersion, which is the spread of the data. It is defined as the difference between the 75th and 25th percentiles of the data.) (mean±SD) value of MGD per woman was 1.2±0.4mGy (1.3±0.3mGy). The median±IQR MGD in the MLO and CC projections was 1.6±0.6mGy and 0.9±0.4mGy, respectively. Significant relationships (P-value ≤ .001) were observed between MGD with CBT (R=0.62) and age (R=-0.85). The results showed that the mammography unit at Shahid Sadoughi Hospital in Yazd is functioning properly. The calculated median±IQR MGD per woman referring to this unit (1.2±0.4mGy) was clearly below the dose limit recommended by American College of Radiology and International Commission on Radiological Protection (3mGy). Moreover, among the factors affecting MGD, the highest correlation was seen between MGD and age (R=-0.85).

Proposed DRLs for mammography in Switzerland

The aim of this study is to propose diagnostic reference levels (DRLs) values for mammography in Switzerland. For the data collection, a survey was conducted among a sufficient number of centres, including five University hospitals, several cantonal hospitals, and large private clinics, covering all linguistic regions of Switzerland to be representative of the clinical practice. The data gathered contained the mean glandular dose (MGD), the compressed breast thickness (CBT), the mammography model and the examination parameters for each acquisition. The data collected was sorted into the following categories: 2D or digital breast tomosynthesis (DBT) examination, craniocaudal (CC) or mediolateral oblique (MLO) projection, and eight categories of CBT ranging from 20 mm to 100 mm in 10 mm intervals. A total of 24 762 acquisitions were gathered in 31 centres on 36 mammography units from six manufacturers. The analysis showed that the data reflects the practice in Switzerland. The results revealed that the MGD is larger for DBT than for 2D acquisitions for the same CBT. From 20–30 mm to 90–100 mm of CBT, the 75th percentile of the MGD values obtained increased from 0.81 mGy to 2.55 mGy for 2D CC acquisitions, from 0.83 mGy to 2.96 mGy for 2D MLO acquisitions, from 1.22 mGy to 3.66 mGy for DBT CC acquisitions and from 1.33 mGy to 4.04 mGy for DBT MLO acquisitions. The results of the survey allow us to propose Swiss DRLs for mammography according to the examination type (2D/DBT), projection (CC/MLO) and CBT. The proposed values are very satisfactory in comparison with other studies.

Clinical and Imaging Characteristics of Contrast-enhanced Mammography and MRI to Distinguish Microinvasive Carcinoma from Ductal Carcinoma In situ

Rationale and ObjectivesThe prognosis of ductal carcinoma in situ with microinvasion (DCISM) is more similar to that of small invasive ductal carcinoma (IDC) than to pure ductal carcinoma in situ (DCIS). It is particularly important to accurately distinguish between DCISM and DCIS. The present study aims to compare the clinical and imaging characteristics of contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) between DCISM and pure DCIS, and to identify predictive factors of microinvasive carcinoma, which may contribute to a comprehensive understanding of DCISM in clinical diagnosis and support surveillance strategies, such as surgery, radiation, and other treatment decisions. Materials and MethodsForty-seven female patients diagnosed with DCIS were included in the study from May 2019 to August 2023. Patients were further divided into two groups based on pathological diagnosis: DCIS and DCISM. Clinical and imaging characteristics of these two groups were analyzed statistically. The independent clinical risk factors were selected using multivariate logistic regression and used to establish the logistic model [Logit(P)]. The diagnostic performance of independent predictors was assessed and compared using receiver operating characteristic (ROC) analysis and DeLong's test. ResultsIn CEM, the maximum cross-sectional area (CSAmax), the percentage signal difference between the enhancing lesion and background in the craniocaudal and mediolateral oblique projection (%RSCC, and %RSMLO) were found to be significantly higher for DCISM compared to DCIS (p = 0.001; p < 0.001; p = 0.008). Additionally, there were noticeable statistical differences in the patterns of enhancement morphological distribution (EMD) and internal enhancement pattern (IEP) between DCIS and DCISM (p = 0.047; p = 0.008). In MRI, only CSAmax (p = 0.012) and IEP (p = 0.020) showed significant statistical differences. The multivariate regression analysis suggested that CSAmax (in CEM or MR) and %RSCC were independent predictors of DCISM (all p < 0.05). The area under the curve (AUC) of CSAmax (CEM), %RSCC (CEM), Logit(P) (CEM), and CSAmax (MR) were 0.764, 0.795, 0.842, and 0.739, respectively. There were no significant differences in DeLong's test for these values (all p > 0.10). DCISM was significantly associated with high nuclear grade, comedo type, high axillary lymph node (ALN) metastasis, and high Ki-67 positivity compared to DCIS (all p < 0.05). ConclusionThe tumor size (CSAmax), enhancement index (%RS), and internal enhancement pattern (IEP) were highly indicative of DCISM. DCISM tends to express more aggressive pathological features, such as high nuclear grade, comedo-type necrosis, ALN metastasis, and Ki-67 overexpression. As with MRI, CEM has the capability to help predict when DCISM is accompanying DCIS.

Radiation dose to breast during digital mammography in Tanzania.

The aim of this study was to evaluate the mean glandular dose (MGD), to assess the potential for optimization, and to propose diagnostic reference levels (DRLs). MGD was estimated from air kerma measurements and patient information collected during mammography examinations. The 75th percentile values were determined as the third quartile of the median MGD values for all hospitals, and DRLs set as 75th percentile of MGD values. The estimated median values of MGD ranged from 1.5 to 3.9mGy for craniocaudal projection for median range of 15-59mm compressed breast thickness (CBT). For a CBT range of 15-63mm, the median MGD value was 1.5-5.1mGy for medio-lateral oblique projection. Comparison with other studies showed that the MGD values obtained in this study were relatively high. The magnitude and wide variation of the exposure parameters suggest existing potential for optimization. The training of radiology staff was identified as a top priority.

Estimating Local Diagnostic Reference Levels for Mammography in Dubai.

As the total volume of mammograms in Dubai is increasing consistently, it is crucial to focus on the process of dose optimization by determining dose reference levels for such sensitive radiographic examinations as mammography. This work aimed to determine local diagnostic reference levels (DRLs) for mammography procedures in Dubai at different ranges of breast thickness. A total of 2599 anonymized mammograms were randomly retrieved from a central dose survey database. Mammographic cases for screening women aged from 40 to 69 years were included, while cases of breast implants and breast thickness outside the range of 20-100 mm were excluded. Mean, median, and 75 percentiles were obtained for the mean glandular dose (MGD) distribution of each mammography projection for all compressed breast thickness (CBT) ranges. The local DRLs for mammography in Dubai were found to be between 0.80 mGy and 0.82 mGy for the craniocaudal (CC) projection and between 0.89 mGy and 0.971.8 mGy for the mediolateral oblique (MLO) projection. Local DRLs were proposed according to different breast thicknesses, starting from 20 to 100 mm. All groups of CBT showed a slight difference in MGD values, with higher values in MLO views rather than CC views. The local DRLs in this study were lower than some other Middle Eastern countries and lower than the standard reference levels reported by the International Atomic Energy Agency (IAEA) at 3 mGy/view.

Average Glandular Dose (AGD) and Radiation Dose Optimization in Screen-Film and Digital X-ray Mammography

We determined the average glandular dose (AGD) from the craniocaudally (CC) and mediolateral oblique (MLO) views of 496 breasts (247 women) at eight clinics in Sudan. The incident air kerma from the X-ray tube output values and the typical patient-specific breast exposure factors were measured. The AGD values were inferred from the measured incident air kerma and breast-specific dose-conversion coefficients. The AGD per CC and MLO projection and per woman ranged from 0.56 to 2.89 mGy (average: 1.36), 0.48 to 2.08 mGy (average: 1.19), and 2.08 to 9.94 mGy (average: 5.10). The proposed national diagnostic reference levels (mGy) for digital mammography are 1.8 and 1.6 mGy for CC and MLO projection, respectively. Establishing the proposed diagnostic reference levels is an essential step in ensuring patient protection from radiation and will help promote dose optimization for X-ray mammography at the national level and beyond. These results provide important baseline data that can be used to formulate national diagnostic reference levels.

Learning radiologists’ annotation styles with multi-annotator labeling for improved neural network performance

BACKGROUND: One of the common problems in labeling medical images is inter-observer variability. The same image can be labeled differently by doctors. The main reasons are the human factor, differences in experience and qualifications, different radiology schools, poor image quality, and unclear instructions. The influence of some factors can be reduced by proper organization of the annotation; however, the opinion of doctors frequently differs.&#x0D; AIM: The study aimed to test whether a neural network with an additional module can learn the style and labeling features of different radiologists and whether such modeling can improve the final metrics of object detection on radiological images.&#x0D; METHODS: For training artificial intelligence systems in radiology, cross-labeling, i.e., annotation of the same image by several doctors, is frequently used. The easiest way is to use labeling from each doctor as an independent example when training the model. Some methods use different rules or algorithms to combine annotation before training. Finally, Guan et al. use separate classification heads to model the labeling style of different doctors. Unfortunately, this method is not suitable for more complex tasks, such as detecting objects on an image. For this analysis, a machine learning model designed to detect objects of different classes on mammographic scans was used. This model is a neural network based on Deformable DETR architecture. A dataset consisting of 7,756 mammographic breast scans and 12,543 unique annotations from 19 doctors was used to train the neural network. For validation and testing, a dataset consisting of 700 and 300 Bi-Rads-labeled scans, respectively, was taken. In all data sets, the proportion of images with pathology was in the 15%20% range. A unique index was assigned to each of the 19 doctors, and a special module at each iteration of the neural network training found a vector corresponding to this index. The vector was expanded to the size of the feature map of each level of the feature pyramid, and then attached by separate channels to the maps. Thus, the encoder and the decoder of the detector had access to the information about which doctor labeled the scan. The vectors were updated using the back-propagation method. Three methods were chosen for comparison:&#x0D; &#x0D; Basic model: Combining labels by different doctors using the voting method.&#x0D; New stylistic module: For predictions on the test dataset, a single doctors index was taken, which showed the best metrics on the validation dataset.&#x0D; New stylistic module: The indexes of the five doctors with the best metrics on the validation dataset were used for predictions on the test dataset. Weighted Boxes Fusion was chosen to combine the predictions.&#x0D; &#x0D; The area under the receiver operating characteristic curve (ROC-AUC) was used as the primary metric on the test dataset (Bi-Rads 3, 4, and 5 categories were referred to pathology). The sum of maximum probabilities of detected malignant objects (malignant masses and calcinates) by cranio-caudal and medio-lateral oblique projections was assumed as the probability of malignancy for each method.&#x0D; RESULTS: The following ROC-AUC metrics were obtained for the three methods: 0.82, 0.87, and 0.89.&#x0D; CONCLUSIONS: The information about the labeling doctor allows the neural network to learn and model the labeling style of different doctors more effectively. In addition, this method may obtain an estimate of the uncertainty of the networks prediction. The use of embedding from different doctors, leading to different predictions, may mean that this data is difficult for an artificial intelligence system to process.

The significance of morphological verification of calcifications in differential diagnsis of the mammary gland pathology

Relevance of the research. An important advantage of radiological imaging methods is their ability to detect areas of calcification in tissues, even in the form of microcalcifications, measuring up to 1 mm. These microcalcifications are formed under the influence of various factors and serve as important diagnostic criteria for a number of pathologies. Microcalcifications, detected during mammographic examination, are considered reliable markers of breast cancer. The classification of breast microcalcifications is based on their radiological and histological characteristics, which are evaluated during the diagnostic process to identify breast pathology. The aim of the study is to determine the frequency of detecting calcifications in the breast through mammographic examination and verify their significance as diagnostic criteria for breast cancer.Materials and methods. The study utilized a processed randomized sample of anonymized archival mammograms from 100 women of mature and elderly age who underwent screening or preventive examinations at the mammography room of the CNE "6th city polyclinic of Lviv". The examinations were conducted using the Fujifilm Amulet Innоvality machine (Japan) in the craniocaudal (CC) and mediolateral oblique (MLO) projections. Histological slides from the archives of the Histology, Cytology, and Embryology Department of Danylo Halytsky Lviv National Medical University and anonymized archival preparations of DOBRA DIAGNOSTYKA LLC were used for the study.The slides were examined under a UlabXSP-137TLED microscope at various magnifications and photographed using an XCAM 1080P camera.Results. It has been established that the frequency of detecting calcifications during screening/preventive examinations of mature and elderly women was 69% of the total number of subjects examined, more than half of them bilaterally. The detected calcifications had different localization, shape, size, and quantity. The analysis of scientific literature sources indicates the existence of different views on the presence of calcifications in breast tissue - some authors consider them to be a characteristic radiological sign of the breast, while others assert that the frequency of malignant neoplasms increases, including in cases of calcification of the glandular tissue. The results of mammary glands histological studying in mature and elderly women indicate that calcifications can be visualized in the context of age-related changes in the stroma or parenchyma of the organ, which don`t exhibit morphological signs of neoplastic development. These calcifications can be manifestations of age-related involution and are possibly formed as a result of calcification of blocked secretions. Conclusion. The presence of calcifications can`t be considered a definitive diagnostic criterion for breast cancer. The final verification of the diagnosis can only be made after conducting a histological examination.

The impact of subjective image quality evaluation in mammography

IntroductionAccording to the guidelines, the mammograms obtained in a screening program must be evaluated to ensure that the quality of the images obtained is above 75% of score 1 (perfect and good) and less than 3% of score 3 (inadequate). This is performed by a person (usually a radiographer), so subjectivity may influence the final evaluation of the images. The aim of this study was to evaluate the impact of subjectivity on breast positioning assessments on resultant screening mammograms. Materials and methodsFive radiographers evaluated a total of 1000 mammograms. One radiographer was an expert in assessing mammography images, while the other four evaluators had varying levels of experience. All images were anonymized, and the ViewDEX software was used for visual grading analysis. The evaluators were divided into two groups, each with two evaluators. Each group evaluated 600 images, with 200 images identical between the two groups. All images had already been evaluated by the expert radiographer. All scores were compared using the Fleiss' and Cohen's kappa coefficient and accuracy score. ResultsThe results from Fleiss' kappa showed fair agreement in the mediolateral oblique (MLO) projection in the first group of evaluators whereas the other results showed poor agreement. When comparing the results from Cohen's kappa a maximum value of agreement between the evaluators was moderate 0.433 [95% CI 0.264–0.587] for the craniocaudal (CC) projection and 0.374 [95% CI 0.212–0.538] for the MLO projection. ConclusionsBased on our results, we can conclude that the agreement between all five raters was poor for both CC (κ = 0.165) and MLO (κ = 0.135) projections, based on the results of Fleiss' kappa statistic. The results show that the influence of subjectivity has a great impact on the evaluation of the quality of mammography images. Implications for practiceThus, the images are evaluated by a person, which has a high impact on subjectivity in the assessment of positioning in mammography. To achieve a more objective assessment of the images and the resulting agreement between the evaluators, we would propose to change the method of assessment. The images could be evaluated by two persons, and in the event of a discrepancy, the images would be evaluated by a third person. A computer programme could also be developed that would allow a more objective evaluation based on the geometric characteristics of the image (angle and length of the pectoral muscle, symmetry, etc.)

Mammographic breast density status in women aged more than 40 years in Sulaimaniyah, Iraq: a cross-sectional study.

Mammography is the gold standard screening procedure for the early diagnosis of breast cancer. This study aimed to determine the distribution of breast density among women older than 40 years in Sulaimaniyah, Iraq, and to examine the correlations between breast density and various risk factors. This cross-sectional study included 750 women who received routine mammographic breast screening at Sulaimaniyah Breast Center. Bilateral standard two-view mammographic images (craniocaudal and mediolateral oblique projections) were acquired and reported using a picture archiving and communication system. American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) assessment categories C and D were considered as dense. A total of 54.3% of breasts were classified as dense, with ACR-BI-RADS categories C or D. Breast density was significantly associated with age, body mass index, a family history of breast cancer, and pre-menopause, and women with no history of breastfeeding were more likely to have dense breasts than those with partial or complete breastfeeding. This study revealed that women from Sulaimaniyah with a distinct breast-density profile at mammographic screening may have a significantly increased risk of breast cancer.

Prevalence and Severity of Breast Arterial Calcification on Routine Mammography

Aim. To determine the frequency of detection and severity of breast arterial calcification (BAC) among women undergoing mammography on the basis of medical institutions in Moscow.Material and methods. The analysis included 4274 digital mammograms of women aged 40-93 who underwent preventive or diagnostic mammography. Standard full-format digital mammograms were performed in craniocaudal and mediolateral oblique projections. In addition to the standard diagnosis of breast disease, all mammograms were evaluated for the presence of BAC. The severity of BAC was assessed on a 12-point scale: mild 3-4 points, moderate 5-6 points, severe 7-12 points.Results. The average frequency of BAC was 10.1%, in the middle age group 50-59 years – 6.0%. The incidence of BAC increased with age, from 0.4% to 0.6% in women &lt;50 years of age to &gt;50% in women ≥80 years of age. A statistically significant and pronounced correlation was found between the woman's age and the presence of BAC r Pearson =0.769 (p&lt;0.001). There was also a less noticeable but statistically significant correlation between age and severity of BAC r Spearman =0.319 (p&lt;0.001). Regression analysis made it possible to estimate the probability of CAD depending on age. In women &lt;50 years of age, only mild to moderate calcification occurred, while those ≥65 years of age had a significant increase in the incidence of severe CAD.Conclusion. There was an expected increase with age in both prevalence and severity of BAC. Cases of severe BAC in women younger than 65 years of age and any BAC in women younger than 50 years of age are atypical and require clarification of their association with cardiovascular and other diseases.

Abbreviated Molecular Breast Imaging: Feasibility and Future Considerations.

Molecular breast imaging (MBI) is a supplemental screening modality consistently demonstrating incremental cancer detection over mammography alone; however, its lengthy duration may limit widespread utilization. The study purpose was to assess feasibility of an abbreviated MBI protocol, providing readers with mediolateral oblique (MLO) projections only and assessing performance in lesion detection and localization. Retrospective IRB-exempt blinded reader study administered to 5 fellowship-trained breast imaging radiologists. Independent reads performed for 124 screening MBI cases, half abnormal and half negative/normal. Readers determined whether an abnormality was present, side of abnormality, and location of abnormality (medial/lateral). Abnormal cases had confirmatory biopsy or surgical pathology; normal cases had imaging follow-up ensuring true negative results. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess performance. A false negative result indicated that a reader failed to detect abnormal uptake; a false positive result indicated a reader incorrectly called an abnormality for a negative case. Tests for association included chi-square, Fisher-exact, and analysis of variance. Mean reader performance for detecting abnormal uptake: sensitivity 96.8%, specificity 98.7%, PPV 98.8%, and NPV 96.9%. Accuracy in localizing lesions to the medial or lateral breast was 100%. There were no associations in reader performance with reader experience, reader technique, lesion morphology, or lesion pathology. Median lesion size was 1.0 cm (range: 0.4-8.0 cm). All readers correctly identified 97.7% (42/43) of lesions with malignant or elevated risk pathology. An abbreviated MBI protocol (MLO images only) maintained high accuracy in lesion detection and localization.

Calcified Axillary Lesions on Mammography: Review and Management.

When interpreting mammography, breast radiologists may identify radiopaque densities in the axilla on the mediolateral oblique or lateral projections. When such densities are encountered, true calcifications must be differentiated from pseudocalcifications (artifact). Using imaging, breast radiologists should be able to localize the finding as being dermal, within the soft tissues, within a lymph node, or intramuscular. By combining the anatomic location with the clinical presentation and any other imaging findings, breast radiologists will be able to determine the most appropriate management.

Diagnostic Reference Levels (DRLs) and Image Quality Evaluation for Digital Mammography in a Nigerian Facility

Diagnostic Reference Levels (DRLs) for digital mammography and image quality evaluation are important optimization tools in medical imaging. High quality mammograms are essential to the successful early detection of breast cancer. The objective of the study is to establish DRLs for digital mammography and to assess image quality of the mammograms for optimization. DRLs were established using thermoluminescent dosimeter (TLD) chips to estimate the mean glandular dose for both cranio-caudal and medio-lateral oblique projections. The TLD chips were calibrated. The DRLs were set at the 75th percentile of the distribution of the median value of mean glandular dose. Image quality was assessed using European Commission guideline for mammographic image quality assessment. Results for DRLs were 0.53 mGy for cranio-caudal and also 0.53 mGy for medio-lateral oblique. Image quality evaluation showed criteria scores for cranio-caudal and medio-lateral oblique projections as 76 % and 61.2 % respectively. The mammograms scored the highest and lowest score of 100 % and 44 % on criteria 2 and criteria 6 (absence of skin fold) respectively for cranio-caudal projections while for the mediolateral oblique projections, criteria 1 (all breast tissue clearly shown) and criteria 5 (inframammary angle clearly demonstrated) have the highest and lowest score of 96 % and 8 % respectively. The study showed that the DRLs in this study was lower than the established values in other regions of Nigeria and international established values. Image quality was within acceptable level. DRLs for digital mammography and image quality evaluation are important optimization tool that should be adopted by every radiology department with mammography unit.

Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network.

The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) “breast tissue”, (2) “benign lymph nodes”, and (3) “suspicious lymph nodes”. Following data preprocessing, a dCNN model was trained and validated with 5385 images. Subsequently, the trained dCNN was tested on a “real-world” dataset and the performance compared to human readers. For visualization, colored probability maps of the classification were calculated using a sliding window approach. The accuracy was 98% for the training and 99% for the validation set. Confusion matrices of the “real-world” dataset for the three classes with radiological reports as ground truth yielded an accuracy of 98.51% for breast tissue, 98.63% for benign lymph nodes, and 95.96% for suspicious lymph nodes. Intraclass correlation of the dCNN and the readers was excellent (0.98), and Kappa values were nearly perfect (0.93–0.97). The colormaps successfully detected abnormal lymph nodes with excellent image quality. In this proof-of-principle study in a small patient cohort from a single institution, we found that deep convolutional networks can be trained with high accuracy and reliability to detect abnormal axillary lymph nodes on mammograms.

Standardised or individualised X-ray tube angle for mediolateral oblique projection in digital mammography?

IntroductionWe aimed to investigate whether there were any differences in positioning criteria related to the presentation of the pectoralis major muscle (pectoral muscle) for women of different heights using a standardized 60° X-ray tube angle for mammograms in mediolateral oblique (MLO) projection. MethodsData from MLO mammograms of right breasts of 45,193 women screened in BreastScreen Norway 2016–2019 were used. The positioning criteria were related to the pectoral muscle length (measure A and measure B), width and shape and considered adequate or inadequate depending on the degree of fulfilling the criteria. Data associated with the pectoral muscle were extracted from Volpara, an automated software for breast density assessment. Information on height was obtained from a self-reported questionnaire received by the women together with the invitation to attend the screening program. Women were divided into three groups based on the height percentiles (P) in the Norwegian growth curves: < 25th percentile (<P25th: ≤163 cm), 25th-75th percentile (P25–P75th: 164–170 cm), and >75th percentile (>P75th: >170 cm). Logistic regression was used to analyse the odds of adequate pectoral muscle length A and B, and shape, adjusting each model for screening technique and equipment model. Results were presented with odds ratios (OR) and 95% confidence intervals (CI). ResultsMean age of the screened women was 61.5 (SD = 4.8) years. The adequate measure for the pectoral muscle length A was obtained for 25.9% (11,724/45,193), length B for 76.3% (34,489/45,193), width for 75.0% (33,894/45,193) and shape for 97.6% (44,118/45,193) of the mammograms. Adjusted odds of an adequate pectoral muscle length A were lower for women of <P25th (OR = 0.90, 95% CI: 0.86–0.95) compared to women of P25-75th. Odds of an adequate pectoral muscle length B were lower for women of <P25th (OR = 0.88, 95% CI 0.84–0.93) and higher for women of >P75th (OR = 1.08, 95% CI 1.02–1.14) compared to women of P25-75. Odds of an adequate pectoral muscle shape were higher for women of <P25th (OR = 1.14, 95%CI 1.08–1.19) and lower for women of >P75th (OR = 0.92, 95% CI 0.87–0.97) compared to women of P25-75th. ConclusionThe 60° X-ray tube angle might suit most of the female population offered mammographic screening in Norway, but women of a relatively low height (163 cm or lower) might benefit from an X-ray tube angle less than 60-degrees. Implications for practiceUsing 60° X-ray tube angle for the MLO mammograms in BreastScreen Norway fit the majority of the participating women. More research is needed to change the protocol associated with the tube angle for women shorter than 163 cm.

Screening Digital Breast Tomosynthesis: Radiation Dose Among Patients With Breast Implants.

To compare the mean glandular dose (MGD), cancer detection rate (CDR), and recall rate (RR) among screening examinations of patients with breast implants utilizing various digital breast tomosynthesis (DBT)-based imaging protocols. This IRB-approved retrospective study included 1998 women with breast implants who presented for screening mammography between December 10, 2013, and May 29, 2020. Images were obtained using various protocol combinations of DBT and 2D digital mammography. Data collected included MGD, implant type and position, breast density, BI-RADS final assessment category, CDR, and RR. Statistical analysis utilized type II analysis of variance and the chi-square test. The highest MGD was observed in the DBT only protocol, while the 2D only protocol had the lowest (10.29 mGy vs 5.88 mGy, respectively). Statistically significant difference in MGD was observed across protocols (P < 0.0001). The highest per-view MGD was among DBT full-field (FF) views in both craniocaudal and mediolateral oblique projections (P < 0.0001). No significant difference was observed in RR among protocols (P = 0.17). The combined 2D (FF only) + DBT implant-displaced (ID) views protocol detected the highest number of cancers (CDR, 7.2 per 1000), but this was not significantly different across protocols (P = 0.48). The combination of 2D FF views and DBT ID views should be considered for women with breast implants in a DBT-based screening practice when aiming to minimize radiation exposure without compromising the sensitivity of cancer detection. Avoidance of DBT FF in this patient population is recommended to minimize radiation dose.

Kuwait National Mammography Screening Program: outcomes of 5 years of screening in Kuwaiti women.

BACKGROUND: Breast cancer is the most common malignancy among women in Kuwait, representing 39.8% of all female cancer cases.OBJECTIVES: Report the data of the Kuwait National Mammography Screening Program (KNMSP) for a 5-year period.DESIGN: Prospective data collection.SETTING: Population-based screening.SUBJECTS AND METHODS: We included mammography screens done for Kuwaiti women (age 40 years and older) who attended the KNMSP from 2014 to 2019 to screen for breast cancer. A full-field digital mammography system was used to acquire the mammographic images in craniocaudal and mediolateral oblique projections. Independent double-blind reading of the mammograms was performed by two radiologists.MAIN OUTCOME MEASURE: Early detection of breast cancer.SAMPLE SIZE: 14 773 asymptomatic women met inclusion criteria (mean [SD] age, 51.8 (8.2).RESULTS: Lesions were detected in 551 women (3.7%). These included 233 malignant lesions (233/551, 42.3%), 57 high-risk lesions (10.3%) and 261 benign lesions (47.4%). The participation rate was 7.8% of the target population of women 40–69 years of age. The majority of breast cancer cases were reported in the age group 45–49 years (23.2%). The KNMSP study recall rate for 5 consecutive years was in a range of 11.9–16.5% (mean, 14.3%). The detection rate of ductal/lobular carcinoma in situ and invasive breast cancer were 2.5 and 13.6 per 1000 screened women, respectively. Invasive ductal carcinoma was the most common type. Only 4314 women followed up within 12–15 months of the first mammography for a retention rate of 29.2%.CONCLUSIONS: Screening mammography improves early detection of breast cancer in women older than 40 years but poor participation is a limitation. We are aiming to increase the participation rate to 70% of the population.LIMITATIONS: Lack of participation by women.CONFLICT OF INTEREST: None.

Estimated risk of radiation-induced cancer following breast screening employing tomosynthesis and digital mammography

The objective of this study was to estimate the risk of radiation-induced injury to the organs due to ionizing radiation following breast screening recommendations employing digital breast tomosynthesis (DBT) and digital mammography (DM). Using the Monte Carlo method, absorbed doses in the tissues and organs were calculated on an adult female phantom, considering two-view craniocaudal (CC) and mediolateral oblique (MLO) projections for each breast. The results showed differences in the total effective risk due to DM (CC + MLO) and DBT (CC + MLO) examinations in Brazil, ranging from 20.73 cases 10−5 (DM) to 27.19 cases 10−5 (DBT). Significant differences were also observed in the total effective risk of cancer incidence in the lungs due to DM (CC + MLO) and DBT (CC + MLO), ranging from 1.75×10−01 cases 10−5 (DM) to 1.76×10−01 cases 10−5 (DBT). The results indicate that the total effective risk of incidence should be considered as an additional parameter for the evaluation of DBT or DBT + DM program performance.

Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification.

The aim of the study was to estimate the diagnostic accuracy of textural features extracted by dual-energy contrast-enhanced mammography (CEM) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. In total, 80 patients with known breast lesion were enrolled in this prospective study according to regulations issued by the local Institutional Review Board. All patients underwent dual-energy CEM examination in both craniocaudally (CC) and double acquisition of mediolateral oblique (MLO) projections (early and late). The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy, and vacuum assisted breast biopsy for benign lesions. In total, 104 samples of 80 patients were analyzed. Furthermore, 48 textural parameters were extracted by manually segmenting regions of interest. Univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), artificial neural network (NNET), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance considering the CC view (accuracy (ACC) = 0.75; AUC = 0.82) was reached with a DT trained with leave-one-out cross-variation (LOOCV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of three robust textural features (MAD, VARIANCE, and LRLGE). The best performance (ACC = 0.77; AUC = 0.83) considering the early-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of ten robust features (MEAN, MAD, RANGE, IQR, VARIANCE, CORRELATION, RLV, COARSNESS, BUSYNESS, and STRENGTH). The best performance (ACC = 0.73; AUC = 0.82) considering the late-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of eleven robust features (MODE, MEDIAN, RANGE, RLN, LRLGE, RLV, LZLGE, GLV_GLSZM, ZSV, COARSNESS, and BUSYNESS). Multivariate analyses using pattern recognition approaches, considering 144 textural features extracted from all three mammographic projections (CC, early MLO, and late MLO), optimized by adaptive synthetic sampling and feature selection operations obtained the best results (ACC = 0.87; AUC = 0.90) and showed the best performance in the discrimination of benign and malignant lesions.