Value of automated breast ultrasound in screening: Standalone and as a supplemental to digital breast tomosynthesis.

We aimed to determine the value of standalone and supplemental automated breast ultrasound (ABUS) in detecting cancers in an opportunistic screening setting with digital breast tomosynthesis (DBT) and compare this combined screening method to DBT and ABUS alone in women older than 39 years with BI-RADS B-D density categories. In this prospective opportunistic screening study, 3466 women aged 39 or older with BI-RADS B-D density categories and with a mean age of 50 were included. The screening protocol consisted of DBT mediolateral-oblique views, 2D craniocaudal views, and ABUS with three projections for both breasts. ABUS was evaluated blinded to mammography findings. Statistical analysis evaluated diagnostic performance for DBT, ABUS, and combined workflows. Twenty-nine cancers were screen-detected. ABUS and DBT exhibited the same cancer detection rates (CDR) at 7.5/1000 whereas DBT + ABUS showed 8.4/1000, with ABUS contributing an additional CDR of 0.9/1000. Standalone ABUS outperformed DBT in detecting 12.5% more invasive cancers. DBT displayed better accuracy (95%) compared to ABUS (88%) and combined approach (86%). Sensitivities for DBT and ABUS were the same (84%), with DBT + ABUS showing a higher rate (94%). DBT outperformed ABUS in specificity (95% vs. 88%). DBT + ABUS exhibited a higher recall rate (14.89%) compared to ABUS (12.38%) and DBT (6.03%) (p < .001). Standalone ABUS detected more invasive cancers compared to DBT, with a higher recall rate. The combined approach showed a higher CDR by detecting one additional cancer per thousand.

Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program.

OBJECTIVES: We aimed to evaluate the early-detection capabilities of AI in a screening program over its duration, with a specific focus on the detection of interval cancers, the early detection of cancers with the assistance of AI from prior visits, and its impact on workload for various reading scenarios. MATERIALS AND METHODS: The study included 22,621 mammograms of 8825 women within a 10-year biennial two-reader screening program. The statistical analysis focused on 5136 mammograms from 4282 women due to data retrieval issues, among whom 105 were diagnosed with breast cancer. The AI software assigned scores from 1 to 100. Histopathology results determined the ground truth, and Youden's index was used to establish a threshold. Tumor characteristics were analyzed with ANOVA and chi-squared test, and different workflow scenarios were evaluated using bootstrapping. RESULTS: The AI software achieved an AUC of 89.6% (86.1-93.2%, 95% CI). The optimal threshold was 30.44, yielding 72.38% sensitivity and 92.86% specificity. Initially, AI identified 57 screening-detected cancers (83.82%), 15 interval cancers (51.72%), and 4 missed cancers (50%). AI as a second reader could have led to earlier diagnosis in 24 patients (average 29.92 ± 19.67 months earlier). No significant differences were found in cancer-characteristics groups. A hybrid triage workflow scenario showed a potential 69.5% reduction in workload and a 30.5% increase in accuracy. CONCLUSION: This AI system exhibits high sensitivity and specificity in screening mammograms, effectively identifying interval and missed cancers and identifying 23% of cancers earlier in prior mammograms. Adopting AI as a triage mechanism has the potential to reduce workload by nearly 70%. CLINICAL RELEVANCE STATEMENT: The study proposes a more efficient method for screening programs, both in terms of workload and accuracy. KEY POINTS: • Incorporating AI as a triage tool in screening workflow improves sensitivity (72.38%) and specificity (92.86%), enhancing detection rates for interval and missed cancers. • AI-assisted triaging is effective in differentiating low and high-risk cases, reduces radiologist workload, and potentially enables broader screening coverage. • AI has the potential to facilitate early diagnosis compared to human reading.

MRI only detected lesions: Can contrast enhanced mammography guided biopsy be an alternative method: Initial clinical findings.

OBJECTIVE: This study aims to analyze our initial findings regarding CEM-guided stereotactic vacuum-assisted biopsy for MRI-only detected lesions and compare biopsy times by MRI-guided biopsy. MATERIALS AND METHODS: In this retrospective analysis, CEM-guided biopsies of MRI-only detected breast lesions from December 2021 to June 2023were included. Patient demographics, breast density, lesion size, background parenchymal enhancement on CEM, lesion positioning, procedure duration, and number of scout views were documented. Initially, seven patients had CEM imaging before biopsy; for later cases, CEM scout views were used for simultaneous lesion depiction and targeting. RESULTS: Two cases were excluded from the initial 28 patients with 29 lesions resulting in a total of 27 lesions in 26 women (mean age:44.96 years). Lesion sizes ranged from 4.5 to 41 mm, with two as masses and the remaining as non-mass enhancements. Histopathological results identified nine malignancies (33.3 %, 9/27), including invasive cancers (55.6 %, 5/9) and DCIS (44.4 %, 4/9). The biopsy PPV rate was 33.3 %. Benign lesions comprised 66.7 %, with 22.2 % high-risk lesions. The biopsy success rate was 93.1 % (27/29), and minor complications occurred in seven cases (25.9 %, 7/27), mainly small hematomas and one vasovagal reaction (3.7 %, 1/27). Median number of scout views required was 2, with no significant differences between cases with or without prior CEM (P = 0.8). Median duration time for biopsy was 14 min, significantly shorter than MRI-guided bx at the same institution (P < 0.001) by 24 min with predominantly upright positioning of the patient (88.9 %) and horizontal approach of the needle (92.6 %). CONCLUSION: This study showed that CEM-guided biopsy is a feasible and safe alternative method and a faster solution for MRI-only detected enhancing lesions and can be accurately performed without the need for prior CEM imaging.

Artificial Intelligence in BI-RADS Categorization of Breast Lesions on Ultrasound: Can We Omit Excessive Follow-ups and Biopsies?

RATIONALE AND OBJECTIVES: Artificial intelligence (AI) systems have been increasingly applied to breast ultrasonography. They are expected to decrease the workload of radiologists and to improve diagnostic accuracy. The aim of this study is to evaluate the performance of an AI system for the BI-RADS category assessment in breast masses detected on breast ultrasound. MATERIALS AND METHODS: A total of 715 masses detected in 530 patients were analyzed. Three breast imaging centers of the same institution and nine breast radiologists participated in this study. Ultrasound was performed by one radiologist who obtained two orthogonal views of each detected lesion. These images were retrospectively reviewed by a second radiologist blinded to the patient's clinical data. A commercial AI system evaluated images. The level of agreement between the AI system and the two radiologists and their diagnostic performance were calculated according to dichotomic BI-RADS category assessment. RESULTS: This study included 715 breast masses. Of these, 134 (18.75%) were malignant, and 581 (81.25%) were benign. In discriminating benign and probably benign from suspicious lesions, the agreement between AI and the first and second radiologists was moderate statistically. The sensitivity and specificity of radiologist 1, radiologist 2, and AI were calculated as 98.51% and 80.72%, 97.76% and 75.56%, and 98.51% and 65.40%, respectively. For radiologist 1, the positive predictive value (PPV) was 54.10%, the negative predictive value (NPV) was 99.58%, and the accuracy was 84.06%. Radiologist 2 achieved a PPV of 47.99%, NPV of 99.32%, and accuracy of 79.72%. The AI system exhibited a PPV of 39.64%, NPV of 99.48%, and accuracy of 71.61%. Notably, none of the lesions categorized as BI-RADS 2 by AI were malignant, while 2 of the lesions classified as BI-RADS 3 by AI were subsequently confirmed as malignant. By considering AI-assigned BI-RADS 2 as safe, we could potentially avoid 11% (18 out of 163) of benign lesion biopsies and 46.2% (110 out of 238) of follow-ups. CONCLUSION: AI proves effective in predicting malignancy. Integrating it into the clinical workflow has the potential to reduce unnecessary biopsies and short-term follow-ups, which, in turn, can contribute to sustainability in healthcare practices.

Evaluating Efficiency of Time Use and Operational Costs in a Breast Clinic Workflow: A Comparative Analysis Between Automated Breast Ultrasound and Handheld Ultrasound.

Objective: The aim of this study was to evaluate efficiency of time use for radiologists and operational costs of automated breast ultrasound (ABUS) versus handheld breast ultrasound (HHUS). Materials and Methods: This study was approved by the Institutional Review Board, and informed consent was waived. One hundred and fifty-three patients, aged 21-81 years, underwent both ABUS and HHUS. The time required for the ABUS scanning and radiologist interpretation and the combined scanning and interpretation time for HHUS were recorded for screening and diagnostic exams. One-Way ANOVA test was used to compare the methods, and Cohen Kappa statistics were used to achieve the agreement levels. Finally, the cost of the methods and return of interest were compared by completing a cost analysis. Results: The overall mean ± standard deviation examination time required for ABUS examination was 676.2±145.42 seconds while mean scan time performed by radiographers was 411.76±67.79 seconds, and the mean radiologist time was 234.01±81.88 seconds. The overall mean examination time required for HHUS was 452.52±171.26 seconds, and the mean scan time and radiologist time were 419.62±143.24 seconds. The reduced time translated into savings of 7.369 TL/month, and savings of 22% in operational costs was achieved with ABUS. Conclusion: The radiologist's time was reduced with ABUS in both screening and diagnostic scenarios. Although a second-look HHUS is required for diagnostic cases, ABUS still saves radiologists time by enabling a focused approach instead of a complete evaluation of both breasts. Thus, ABUS appears to save both medical staff time and operational costs.

Future of Breast Radiology.

The landscape of breast imaging has transformed significantly since mammography's introduction in the 1960s, accelerated by ultrasound and imageguided biopsies in the 1990s. The emergence of magnetic resonance imaging (MRI) in the 2000s added a valuable dimension to advanced imaging. Multimodality and multiparametric imaging have firmly established breast radiology's pivotal role in managing breast disorders. A shift from conventional to digital radiology emerged in the late 20th and early 21st centuries, enabling advanced techniques like digital breast tomosynthesis, contrast-enhanced mammography, and artificial intelligence (AI) integration. AI's impending integration into breast radiology may enhance diagnostics and workflows. It involves computer-aided diagnosis (CAD) algorithms, workflow support algorithms, and data processing algorithms. CAD systems, developed since the 1980s, optimize cancer detection rates by addressing false positives and negatives. Radiologists' roles will evolve into specialized clinicians collaborating with AI for efficient patient care and utilizing advanced techniques with multiparametric imaging and radiomics. Wearable technologies, non-contrast MRI, and innovative modalities like photoacoustic imaging show potential to enhance diagnostics. Imaging-guided therapy, notably cryotherapy, and theranostics, gains traction. Theranostics, integrating therapy and diagnostics, holds potential for precise treatment. Advanced imaging, AI, and novel therapies will revolutionize breast radiology, offering refined diagnostics and personalized treatments. Personalized screening, AI's role, and imaging-guided therapies will shape the future of breast radiology.

Linear Regression Modeling Based Scoring System to Reduce Benign Breast Biopsies Using Multi-parametric US with Color Doppler and SWE.

RATIONALE AND OBJECTIVES: To develop a simple ultrasound (US) based scoring system to reduce benign breast biopsies. MATERIALS AND METHODS: Women with BI-RADS 4 or 5 breast lesions underwent shear-wave elastography (SWE) imaging before biopsy. Standard US and color Doppler US (CDUS) parameters were recorded, and the size ratio (SzR=longest/shortest diameter) was calculated. Measured/calculated SWE parameters were minimum (SWVMin) and maximum (SWVMax) shear velocity, velocity heterogeneity (SWVH=SWVMax-SWVMin), velocity ratio (SWVR=SWVMin/SWVMax), and normalized SWVR (SWVRn=(SWVMax-SWVMin)/SWVMin). Linear regression analysis was performed by converting continuous parameters into categorical corresponding equivalents using decision tree analyses. Linear regression models were fitted using stepwise regression analysis and optimal coefficients for the predictors in the models were determined. A scoring model was devised from the results and validated using a different data set from another center consisting of 187 cases with BI-RADS 3, 4, and 5 lesions. RESULTS: A total of 418 lesions (238 benign, 180 malignant) were analyzed. US and CDUS parameters exhibited poor (AUC=0.592-0.696), SWE parameters exhibited poor-good (AUC=0.607-0.816) diagnostic performance in benign/malignant discrimination. Linear regression models of US+CDUS and US+SWE parameters revealed an AUC of 0.819 and 0.882, respectively. The developed scoring system could have avoided biopsy in 37.8% of benign lesions while missing 1.1% of malignant lesions. The scoring system was validated with a 100% NPV rate with a specificity of 74.6%. CONCLUSION: The linear regression model using US+SWE parameters performed better than any single parameter alone. The developed scoring method could lead to a significant decrease in benign biopsies.

What Has Changed in Patients Aged 65 and over Diagnosed with Breast Cancer during the COVID-19 Pandemic: A Single-Center Experience.

Introduction: The COVID-19 pandemic has a worldwide negative impact on healthcare systems. This study aims to determine how the diagnosis, clinicopathological features, and treatment approaches of patients with breast cancer (BC) diagnosed at ≥65 years old were affected during the pandemic. This survey has shown that patients, especially the elderly, had to postpone their BC health problems or delay their routine controls due to the risk of COVID-19 transmission, high mortality rates due to comorbidity, and restrictions. Materials and Methods: The medical records of 153 patients with BC diagnosed at ≥65 years old before (January-December 2019; group A, n = 61) and during (March 2020-May 2021; group B, n = 92) the COVID-19 pandemic were retrospectively analyzed. In addition, clinicopathological features of patients, including age, admission form, clinical stage, tumor (T) size-grade-histology-subtype, lymph node involvement, surgery type, and treatment protocols, were evaluated. Results: Patients mostly applied for screening purposes were included in group A and patients who frequently applied for diagnostic purposes due to their existing BC or other complaints were included in group B (p = 0.009). Group B patients had a higher clinical stage (p = 0.026) and had commonly larger (p = 0.020) and high-grade (p = 0.001) Ts. Thus, mastectomy and neoadjuvant systemic therapy were more commonly performed in group B (p = 0.041 and p = 0.005). Conclusion: The survey showed significant changes in BC diagnosis and treatment protocols for patients diagnosed at ≥65 years old during the COVID-19 pandemic. Postponing screening and delaying treatment leads to more advanced BC stages in elderly patients.

Diagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis.

Purpose: To evaluate the performance of an artificial intelligence (AI) algorithm in a simulated screening setting and its effectiveness in detecting missed and interval cancers. Methods: Digital mammograms were collected from Bahcesehir Mammographic Screening Program which is the first organized, population-based, 10-year (2009-2019) screening program in Turkey. In total, 211 mammograms were extracted from the archive of the screening program in this retrospective study. One hundred ten of them were diagnosed as breast cancer (74 screen-detected, 27 interval, 9 missed), 101 of them were negative mammograms with a follow-up for at least 24 months. Cancer detection rates of radiologists in the screening program were compared with an AI system. Three different mammography assessment methods were used: (1) 2 radiologists' assessment at screening center, (2) AI assessment based on the established risk score threshold, (3) a hypothetical radiologist and AI team-up in which AI was considered to be the third reader. Results: Area under curve was 0.853 (95% CI = 0.801-0.905) and the cut-off value for risk score was 34.5% with a sensitivity of 72.8% and a specificity of 88.3% for AI cancer detection in ROC analysis. Cancer detection rates were 67.3% for radiologists, 72.7% for AI, and 83.6% for radiologist and AI team-up. AI detected 72.7% of all cancers on its own, of which 77.5% were screen-detected, 15% were interval cancers, and 7.5% were missed cancers. Conclusion: AI may potentially enhance the capacity of breast cancer screening programs by increasing cancer detection rates and decreasing false-negative evaluations.

Comparison of 3D-Automated Breast Ultrasound With Handheld Breast Ultrasound Regarding Detection and BI-RADS Characterization of Lesions in Dense Breasts: A Study of 592 Cases.

RATIONALE AND OBJECTIVE: We aimed to compare the diagnostic performance of an automated breast ultrasound system (ABUS) with handheld ultrasound (HHUS) in the detection and characterization of lesions regarding BI-RADS classification in women with dense breasts. MATERIALS AND METHODS: After ethical approval, from July 2017 to August 2019, 592 consecutive patients were enrolled in this prospective study. On the same day, patients underwent ABUS followed by HHUS. Three breast radiologists participated in this study. The number and type of lesions and BI-RADS categorization of both ABUS and HHUS examinations of each patient were recorded in an excel file. The level of agreement between the two ultrasound systems in terms of lesion number and BI-RADS category were analyzed statistically. RESULTS: ABUS and HHUS detected 1005 and 1491 cystic and 270 and 336 mass lesions in 592 patients respectively. ABUS and HHUS detected 171 and 167 positive/suspicious cases (BIRADS 0/3/4/5). Forty suspicious lesions underwent core needle biopsy whereas 11 malignant lesions were detected by both methods. The remaining lesions were followed with a mean of 31 months. The mean size of solid lesions detected by HHUS and ABUS was 7.67 mm (range 2.1-41 mm) and 7.74 mm (range 2-42 mm) respectively. The agreement for detection of cystic lesions between two methods for each breast was good (kappa: 0.61-0.62 p < 0.001). The agreement of two methods for solid mass lesions for each breast was moderate (k = 0.57-0.60 p < 0.001). There was good agreement between the two methods for detecting suspicious lesions (kappa = 0.66 p < 0.001). CONCLUSION: The level of agreement of ABUS and HHUS for dichotomic assignment of BIRADS categories was good. Although ABUS detected fewer lesions compared to HHUS, both methods detected all malignant lesions. ABUS is a reliable method for the detection of malignancy in dense breasts.

Evaluation of Multiparametric Shear Wave Elastography Indices in Malignant and Benign Breast Lesions.

RATIONALE AND OBJECTIVES: To evaluate the shear wave elastography indices (multiparametric SWE) of breast lesions based on patient and lesion dependent features and assess the contribution of different elastographic parameters to radiological diagnosis. MATERIALS AND METHODS: Effect of patient-dependent (age and menopausal status) and lesion-dependent (distance from the areola, quadrant location, size, depth, margin and shape) factors on SWE parameters (Vmean, Vsd, Vmax, Vmin) in benign breast lesions were assessed. Only mass lesions were included in the study. Sensitivity, specificity, PPV, NPV and cut-off values for each elastography parameter was calculated. RESULTS: A total of 496 mass lesions of breast were evaluated. 467 of the lesions were benign and 29 were malignant. There was no significant relationship among SWE indices and age, menopausal status, lesion shape and distance to the areola in benign lesions (p>0.05). SWE indices were found to be associated with lesion margin, depth from the skin, and lesion size in benign lesions (p<0.05). All BI-RADS 3 lesions that underwent biopsy were benign (n:35); 23.5% of 4a lesions were malignant (n:4/17) and all 4b-4c-5 lesions were malignant (n:25/25). The cut-off values for malignant lesions were: Vmean 3.38 m/s, Vsd 0.81, Vmax 6.87 m/s, Vmin 1.53 m/s. All SWE parameters were statistically significant in predicting malignancy on ROC analysis, Vmax was the most sensitive (96.3%) and specific (94.7%) parameter. Cut-off values for Vmax was 6.87 m/s with an accuracy rate of 94.7%, and 3.37 m/s for Vmean and 0.8 for Vsd with 92.5% accuracy. CONCLUSION: The SWE parameters to predict malignancy in breast lesions can be affected by lesion dependent features, whereas no significant effect of patient's age or menopausal status on stiffness of the lesions was observed. Vmax had the highest sensitivity for predicting malignancy.

Bahcesehir long-term population-based screening compared to National Breast Cancer Registry Data: effectiveness of screening in an emerging country.

PURPOSE: We aimed to show the effects of long-term screening on clinical, pathologic, and survival outcomes in patients with screen-detected breast cancer and compare these findings with breast cancer patients registered in the National Breast Cancer Registry Data (NBCRD). METHODS: Women aged 40-69 years, living in Bahcesehir county, Istanbul, Turkey, were screened every 2 years using bilateral mammography. The Bahcesehir National Breast Cancer Registry Data (BMSP) data were collected during a 10-year screening period (five rounds of screening). BMSP data were compared with the NBCRD regarding age, cancer stage, types of surgery, tumor size, lymph node status, molecular subtypes, and survival rates. RESULTS: During the 10-year screening period, 8758 women were screened with 22621 mammograms. Breast cancer was detected in 130 patients; 51 (39.2%) were aged 40-49 years. The comparison of breast cancer patients in the two programs revealed that BMSP patients had earlier stages, higher breast-conserving surgery rates, smaller tumor size, more frequent negative axillary nodal status, lower histologic grade, and higher ductal carcinoma in situ rates than NBCRD patients (p = 0.001, for all). CONCLUSION: These results indicate the feasibility of successful population-based screening in middle-income countries.

Effects of iron oxide particles on MRI and mammography in breast cancer patients after a sentinel lymph node biopsy with paramagnetic tracers.

OBJECTIVE: The aim of this study is to evaluate the effect of iron oxide particle deposition on follow-up mammograms and MRI examinations of patients who underwent sentinel lymph node detection with iron oxide particles. MATERIALS AND METHODS: Two hundred and eighteen patients who had sentinel lymph node biopsy (SLNB) with iron oxide particles were evaluated. Follow-up MRI and mammography were available in 36 and 69 cases respectively. MRI examinations were evaluated for ferromagnetic artifacts that were graded as follows: 0 = No artifact, 1 = Focal area, 2 = Segmental and 3 = Regional signal void artifact. Mammography artifacts were evaluated for the presence of dense particles. Pearson's chi-square test was used for statistical analyses and P < 0.05 was accepted as significant. RESULTS: MRI artifact grading was as follows: Grade 0: 11 (30.6%), Grade 1: 14 (38.9%), Grade 2: 3 (8.3%), and Grade 3: 8 (22.2%). The grade of artifacts differed across surgery types (P = 0.019). Grade 3 artifacts were higher in breast conserving cases whereas Grade 0 was more frequent in subcutaneous mastectomy cases. Three out of 69 (4.4%) cases who had follow-up mammography had artifacts due to iron oxide particle accumulation which presented as Grade 3 MRI artifact in all. CONCLUSION: Accumulation of iron oxide particles after SLNB with paramagnetic tracers causes artifacts on follow-up MRI examinations in half of the cases but it is significantly low in mammograms. These artifacts may be confusing in the evaluation of the images. Radiologists must be aware of these tracers and their artifacts whereas patients should be questioned for the type of SLNB before a follow-up examination.

The value of MRI contrast enhancement in biopsy decision of suspicious mammographic microcalcifications: a prospective multicenter study.

OBJECTIVES: To investigate the inclusion of breast MRI in radiological assessment of suspicious, isolated microcalcifications detected with mammography. METHODS: In this prospective, multicenter study, cases with isolated microcalcifications in screening mammography were examined with dynamic contrast-enhanced MRI (DCE-MRI) before biopsy, and contrast enhancement of the relevant calcification localization was accepted as a positive finding on MRI. Six experienced breast radiologists evaluated the images and performed the biopsies. Imaging findings and histopathological results were recorded. Sensitivity, specificity, NPV, and PPV of breast MRI were calculated and compared with histopathological findings. RESULTS: Suspicious microcalcifications, which were detected by screening mammograms of 444 women, were evaluated. Of these, 276 (62.2%) were diagnosed as benign and 168 (37.8%) as malignant. Contrast enhancement was present in microcalcification localization in 325 (73.2%) of the cases. DCE-MRI was positive in all 102 invasive carcinomas and in 58 (87.9%) of 66 DCIS cases. MRI resulted in false negatives in eight DCIS cases; one was high grade and the other seven were low-to-medium grade. The false-negative rate of DCE-MRI was 4.76%. The sensitivity, specificity, PPV, and NPV for DCE-MRI for mammography-detected suspicious microcalcifications were 95.2%, 40.2%, 49.2%, and 93.3%, respectively. CONCLUSIONS: In this study, all invasive cancers and all DCIS except eight cases (12.1%) were detected with DCE-MRI. DCE-MRI can be used in the decision-making algorithm to decrease the number of biopsies in mammography-detected suspicious calcifications, with a tradeoff for overlooking a small number of DCIS cases that are of low-to-medium grade. KEY POINTS: • All invasive cancer cases and 87.8% of all in situ cancer cases were detected with MRI, showing a low false-negative rate of 4.7%. • Dynamic contrast-enhanced MRI can be used in the decision-making algorithm to decrease the number of biopsies in mammography-detected suspicious calcifications, with a tradeoff for overlooking a small number of DCIS cases that are predominantly low-to-medium grade. • If a decision for biopsy were made based on MRI findings in mammography-detected microcalcifications in this study, biopsy would not be performed to 119 cases (26.8%).

Contribution of Magnetic Resonance Imaging in Determining Lumpectomy Cavity in Breast Radiotherapy.

BACKGROUND: Accurate localization of the lumpectomy cavity is important for breast cancer radiotherapy after breast-conserving surgery (BCS), but the LC localization based on CT is often difficult to delineate accurately. The study aimed to compare CT-defined LC planning to MRI-defined findings in the supine position for higher soft-tissue resolution of MRI. METHODS: Fifty-nine breast cancer patients underwent radiotherapy CT planning in supine position followed by MR imaging on the same day. LC was contoured by the radiologist and radiation oncologist together by CT and MRI separately. T2 weighted MR images and tomography findings were combined and the LC volume, mean diameter and the longest axis length were measured after contouring. Subsequently, patients were divided into two groups according to seroma in LC and the above-mentioned parameters were compared. RESULTS: We did not find any statistically significant difference in the LC volume, mean diameter and length at the longest axis between CT and MRI but based on the presence or absence of seroma, statistically significant differences were found in the LC volumes and the length at the longest axis of LC volumes. CONCLUSION: We believe that the supine MRI in the same position with CT will be more effective for radiotherapy planning, particularly in patients without a seroma in the surgical cavity.

Poor Biological Factors and Prognosis of Interval Breast Cancers: Long-Term Results of Bahçesehir (Istanbul) Breast Cancer Screening Project in Turkey.

PURPOSE: The Turkish Bahçesehir Breast Cancer Screening Project was a 10-year, organized, population-based screening program carried out in Bahçesehir county, Istanbul. Our aim was to examine the biologic features and outcome of screen-detected and interval breast cancers during the 10-year study period. METHODS: Between 2009 and 2019, 2-view mammograms were obtained at 2-year intervals for women aged 40 to 69 years. Clinicopathological characteristics including ER, PR, HER2-neu, and Ki-67 status were analyzed for those diagnosed with breast cancer. RESULTS: In 8,758 screened women, 131 breast cancers (1.5%) were detected. The majority of patients (82.3%) had prognostic stage 0-I disease. Contrarily, patients with interval cancers (n = 15; 11.4%) were more likely to have a worse prognostic stage (II-IV disease; odds ratio [OR], 3.59, 95% CI, 0.9 to 14.5) and high Ki-67 scores (OR, 3.14; 95% CI, 0.9 to 11.2). Interval cancers detected within 1 year were more likely to have a luminal B (57.1% v 31.9%) and triple-negative (14.3% v 1%) subtype and less likely to have a luminal A subtype (28.6% v 61.5%; P = .04). Patients with interval cancers had a poor outcome in 10-year disease-specific (DSS) and disease-free survival (DFS) compared with those with screen-detected cancers (DSS: 68.2% v 98.1%, P = .002; DFS: 78.6% v 96.5%, P = .011). CONCLUSION: Our findings suggest the majority of screen-detected breast cancers exhibited a luminal A subtype profile with an excellent prognosis. However, interval cancers were more likely to have aggressive subtypes such as luminal B subtype or triple-negative cancers associated with a poor prognosis requiring other preventive strategies.

Comparison of Qualitative and Volumetric Assessments of Breast Density and Analyses of Breast Compression Parameters and Breast Volume of Women in Bahcesehir Mammography Screening Project.

OBJECTIVE: We aimed to compare visual and quantitative measurements of breast density and to reveal the density profile with compression characteristics. MATERIALS AND METHODS: Screening mammograms of 1399 women between May 2014 and May 2015 were evaluated by using Volpara 4th and 5th version. First 379 mammograms were assessed according to ACR BI-RADS 4th edition and compared to Volpara. We categorized the breast density in two subgroups as dens or non-dens. Two radiologists reviewed the images in consensus. Agreement level between visual and volumetric methods and volumetric methods between themselves assessed using weighted kappa statistics. Volpara data such as fibroglandular volume (FGV), breast volume (BV), compression thickness (CT), compression force (CF), compression pressure (CP) were also analyzed with relation to the age. RESULTS: 1399 mammograms were distributed as follows: 12.7% VDG1, 39.3% VDG2, 34.1% VDG3, 13.9% VDG4 according to the 4th edition of Volpara; 1.2% VDG1, 46% VDG2, 36.8% VDG3, 15.9% VDG4 according to the 5th edition of Volpara. The difference between two editions was 4.7% increase in dense category. 379 mammograms, according to ACR BI-RADS 4th edition, were distributed as follows: 25.9% category A, 50.9% category B, 19.8% category C, 3.4% category D. The strength of agreement between the Volpara 4th and 5th editions was found substantial (k=0.726). The agreements between visual assessment and both Volpara editions were poor (k=-0.413, k=-0.399 respectively). There was a 142% increase in dense group with the VDG 4th edition and 162% with the VDG 5th edition when compared to visual assessment. Compression force decreased while compression pressure increased with increasing Volpara Density Grade (VDG) (p for trend <0.001 for both). Compression thickness and breast volume decreased with increasing VDG (p for trend <0.001 for both). The FGV decreases with age and the breast volume increases with increasing age (p<0.001). CONCLUSION: Visual assessment of breast density doesn't correlate well with volumetric assessments. Obtaining additional information about physical parameters and breast profile by the results of quantified methods is important for breast cancer risk assessments and prevention strategies.

A New Technical Mode in Mammography: Self-Compression Improves Satisfaction.

OBJECTIVE: We aimed to evaluate the mammography experience of patients using a manually controlled self- compression tool compared to their previous experience based on technician performed breast compression by a questionnaire survey study. MATERIALS AND METHODS: The survey studies of 365 patients who underwent screening or diagnostic mammography between April 2017 and July 2017 at our center were reviewed retrospectively. Each patient had completed a 12-item questionnaire following mammography examinations. Women who never had a mammography before or who had a previous mammography examination more than 2 years ago or who did not want to use the self-compression device were excluded from the study. 106 women were included in the study. RESULTS: Patient satisfaction was high. Regarding the comparison of the experience of the exam to previous ones, 70.8% said it was a better experience. The examination was found comfortable by 85.4% of the participants and 75.5% found the examination more comfortable compared to previous ones. Only 11.3% were anxious and 52.8% declared they were less anxious compared to previous examinations. Regarding the attractiveness of the new design, 66.9% declared they found the new design attractive, 39.7% found it more attractive than previous examinations, and 27.3% said the new design decreased anxiety. In the evaluation of impact of patient-assisted compression (PAC) on comfort, 80.2% said that they found it more comfortable and 64.2% said that PAC decreased anxiety. Furthermore, 72.6% said the exam was shorter. CONCLUSION: Self-compression technique decreases pain and anxiety of women during mammography examinations and promises to enhance compliance of clients and patients with follow-up mammography recommendations.