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.

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%).

Diagnostic performance of diffusion tensor imaging parameters in breast cancer and correlation with the prognostic factors.

PURPOSE: To evaluate the diagnostic performances of the diffusion tensor imaging (DTI) parameters in the diagnosis of breast cancer and to investigate the variations in DTI parameters according to the breast cancer biomarkers. MATERIALS AND METHODS: At 3.0 Tesla (T), DTI was performed in 85 patients with 92 enhancing breast lesions. λ1 , λ2 , λ3 , mean diffusivity (MD), radial diffusivity (RD), fractional anisotropy (FA), relative anisotropy (RA), and geodesic anisotropy (GA) were studied and compared with diffusion-weighted imaging-derived apparent diffusion coefficient. Lesions were analyzed according to BIRADS lexicon. Logistic regression models were constructed to determine the contribution of DTI to the specificity and the accuracy of DCE-MRI. Breast cancer biomarkers; estrogen receptor (ER), HER-2 status, and Ki-67 were correlated with DTI in malignant cases. RESULTS: Malignant lesions exhibited significantly lower MD, RD, λ1 , λ2 , λ3 and higher FA, RA, GA values (P < 0.001). Logistic regression models showed that MD, RD, λ1 , λ2 , λ3 , FA, and RA increase the specificity of the DCE-MRI (from 83.0% to 89.4-93.6%; P < 0.05). Higher RD, λ2 , λ3 and lower FA, RA, and GA values were observed in ER-negative breast cancer (P < 0.05). Ki-67 showed significant, negative correlation with FA, RA, GA, λ1 -λ3 and λ1 -λ2 (r = -0.336 to -0.435; P < 0.05). CONCLUSION: Besides its ability to differentiate malignant breast lesions, DTI improves the specificity of conventional 3.0T breast MRI and shows correlation with biomarkers ER and Ki-67. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:660-672.

Volume Navigation Technique for Ultrasound-Guided Biopsy of Breast Lesions Detected Only at MRI.

OBJECTIVE: The purpose of this study is to assess the utility of a volume navigation technique (VNT) for ultrasound-guided biopsy of MRI-detected, but sonographically ambiguous or occult, breast lesions. SUBJECTS AND METHODS: Within a recruitment period of 13 months (January 1, 2014, through February 1, 2015), 22 patients with 26 BI-RADS category 4 or 5 lesions that were detected at MRI but missed at second-look ultrasound were reimaged using a rapid sequence and a flexible body coil in a 3-T MRI scanner. Patients were supine, with three skin markers placed on the breasts. MRI volume data were coregistered to real-time ultrasound in a dedicated platform, and MRI-detected lesions (six masses, 11 nonmass enhancements, eight foci, and one architectural distortion) were sought using VNT-guided ultrasound. Five needle biopsy specimens were obtained either from each sonographically detected lesion (n = 11) or from VNT-guided sonographically localized breast volume corresponding to the MRI-detected, but still ultrasound-occult, lesions (n = 15). RESULTS: Histopathologic analysis revealed 18 benign and six malignant lesions. The remaining two lesions, both of which appeared as masses at MRI, were high risk and were upgraded to carcinoma after excisional biopsy. All malignant lesions underwent curative surgery; the final histopathologic diagnoses remained unchanged. Of the six malignant lesions, one was a mass, three were nonmass enhancements, and two were enhancing foci at MRI. Three malignant lesions were occult at ultrasound, and three were discerned as subtle hypoechoic changes. No benign lesion was sonographically visualized as a mass, and none progressed, with 56% disappearing at MRI performed during the follow-up period (mean, 14 months). CONCLUSION: Coregistration of MRI and real-time ultrasound enables sonographic localization of breast lesions detected at MRI only. VNT is a feasible alternative to MRI-guided biopsy of ultrasound-occult breast lesions.

Effects of nodule characteristics on sampling number and duration of thyroid fine-needle aspiration biopsy: size does not matter, but cystic degeneration ratio does.

Background Ultrasound-guided fine needle aspiration biopsy (FNAB) of thyroid nodules, though the most accurate method to rule out malignancy, still has an inherent risk of yielding non-diagnostic specimens despite immediate assessment of specimen adequacy by an on-site cytopathologist. Purpose To evaluate the effects of nodule volume and extent of cystic degeneration on total biopsy time and number of aspirations required for obtaining an adequate specimen. Material and Methods A total of 510 patients underwent FNAB by a single radiologist accompanied by a cytopathologist who immediately assessed each sample for specimen adequacy. All sampled nodules were single and had one maximum diameter > 10 mm. Nodule volumes and cystic degeneration ratios were calculated prior to the intervention. Aspirations were repeated until the cytological material was deemed adequate by the cytopathologist; the number of aspirations and total biopsy time were then recorded. Results Nodule volumes did not have significant effect on neither number of aspirations necessary for achieving specimen adequacy ( P > 0.05) nor total biopsy time (r = -0.148 with P = 0.001). Predominantly cystic nodules, compared to predominantly solid nodules, required more sampling per nodule (4.58 ± 1.11 vs. 3.44 ± 1.19 aspirations, P = 0.001) and longer total biopsy time (16.40 ± 6.19 vs. 11.15 ± 6.18 min, P = 0.001). Conclusion Predominantly cystic nodules require allocation of more time for biopsy. To ensure specimen adequacy when immediate specimen evaluation by an on-site cytopathologist is not possible, four samples from predominantly solid nodules and five passes through predominantly cystic nodules are required.

Quantitative differentiation of breast lesions at 3T diffusion-weighted imaging (DWI) using the ratio of distributed diffusion coefficient (DDC).

PURPOSE: To investigate the accuracy of diffusion coefficients and diffusion coefficient ratios of breast lesions and of glandular breast tissue from mono- and stretched-exponential models for quantitative diagnosis in diffusion-weighted magnetic resonance imaging (MRI). MATERIALS AND METHODS: We analyzed pathologically confirmed 170 lesions (85 benign and 85 malignant) imaged using a 3.0T MR scanner. Small regions of interest (ROIs) focusing on the highest signal intensity for lesions and also for glandular tissue of contralateral breast were obtained. Apparent diffusion coefficient (ADC) and distributed diffusion coefficient (DDC) were estimated by performing nonlinear fittings using mono- and stretched-exponential models, respectively. Coefficient ratios were calculated by dividing the lesion coefficient by the glandular tissue coefficient. RESULTS: A stretched exponential model provides significantly better fits then the monoexponential model (P < 0.001): 65% of the better fits for glandular tissue and 71% of the better fits for lesion. High correlation was found in diffusion coefficients (0.99-0.81 and coefficient ratios (0.94) between the models. The highest diagnostic accuracy was found by the DDC ratio (area under the curve [AUC] = 0.93) when compared with lesion DDC, ADC ratio, and lesion ADC (AUC = 0.91, 0.90, 0.90) but with no statistically significant difference (P > 0.05). At optimal thresholds, the DDC ratio achieves 93% sensitivity, 80% specificity, and 87% overall diagnostic accuracy, while ADC ratio leads to 89% sensitivity, 78% specificity, and 83% overall diagnostic accuracy. CONCLUSION: The stretched exponential model fits better with signal intensity measurements from both lesion and glandular tissue ROIs. Although the DDC ratio estimated by using the model shows a higher diagnostic accuracy than the ADC ratio, lesion DDC, and ADC, it is not statistically significant. J. Magn. Reson. Imaging 2016;44:1633-1641.

Use of a Volume Navigation Technique for Combining Real-Time Ultrasound and Contrast-Enhanced MRI: Accuracy and Feasibility of a Novel Technique for Locating Breast Lesions.

OBJECTIVE: The objective of this study was to evaluate the accuracy of the volume navigation technique for combining real-time ultrasound and contrast-enhanced MRI (CE-MRI) of breast lesions. SUBJECTS AND METHODS: Thirty-eight women with single breast lesions underwent 3-T MRI. A 3.5-minute CE-MRI sequence was used, as was a flexible body coil. Patients underwent imaging in the supine position, with three markers placed on their breasts. Real-time sonographic images were coregistered to the preloaded breast CE-MRI volume by coupling skin markers, with the use of an electromagnetic transmitter positioned next to the subjects. The transmitter detected the spatial positions of the two electromagnetic sensors mounted on the transducer bracket. After this fusion process in 3D space was completed, divergences in the location of the center of each lesion on CE-MRI and ultrasound images were analyzed. RESULTS: The mean lesion size was 17.4 mm on ultrasound and 17.9 mm on MRI, whereas the mean (± SD) misalignment of the lesion centers on CE-MRI and ultrasound was 3.9 ± 2.5 mm on the x-axis (mediolateral view), 3.6 ± 2.7 mm on the y-axis (anteroposterior view), and 4.3 ± 2.6 mm on the z-axis (craniocaudal view). No lesion had a misalignment greater than 10 mm on any of three axes. The accuracy of volume navigation was independent of patient age and the lesion size, location, and histopathologic findings (p > 0.05). Intermediate lesions, which had a depth of center of 11-20 mm on ultrasound had a mean misalignment of 2.6 ± 1.9 mm, compared with 5.5 ± 2.2 mm for deep lesions, which had a depth of center greater than 20 mm (p = 0.049). CONCLUSION: The volume navigation technique is an accurate method for coregistration of CE-MRI and sonographic images, enabling lesion localization within a limited volume.

Outcomes of unconventional utilization of BI-RADS category 3 assessment at opportunistic screening.

Background An important difficulty regarding the Breast Imaging Reporting and Data System (BI-RADS) category 3 assessment is the need for extensive diagnostic workup and an additional 6-month follow-up study. Purpose To evaluate the feasibility of the BI-RADS category 3 assessments at opportunistic screening. Material and Methods Mammography charts of 9062 screening patients in a major teaching hospital situated in an urban setting of a developing country were evaluated retrospectively (1997-2010). BI-RADS category 3 patients, called for a 6-month follow-up, which comprised a single-view spot or magnification mammogram. The length of follow-up period, compliance to periodic mammographic surveillance, cancer detection rate, and negative predictive values of category 3 assessments were calculated. Results Of the screened population, 9.2% were assigned BI-RADS category 3, and 31.2% of these cases were lost to follow-up. The mean follow-up period for 606 patients was 36.9 months. The negative predictive values for 6-month, 12-month, and final control studies were 90.9%, 87.5%, and 100%, respectively. Patient compliance for 6 months, 12 months, and any control evaluations beyond 12 months was low (50.0%, 29.8%, and 47.5%, respectively). Cancer detection rate was 0.8%. Conclusion Results of the study supports the feasibility of the BI-RADS category 3 assessments at opportunistic screening without any additional diagnostic workup. The practice of category 3 assessment following screening mammograms may be a more cost-effective method for developing countries with high recall rates and low resources in eliminating the maximum risk with minimum cost within the limits of available resources.

Thyroid fine needle aspiration biopsy: do nodule volume and cystic degeneration ratio affect specimen adequacy and cytological diagnosis time?

BACKGROUND: A fine needle aspiration biopsy (FNAB) of thyroid nodules - the least invasive and most accurate method used to investigate malignant lesions - may yield non-diagnostic specimens even under ultrasonographic guidance. PURPOSE: To evaluate the effects of thyroid nodule volume and extent of cystic degeneration on both the non-diagnostic specimen ratio as well as cytopathologist's definitive cytological diagnosis time. MATERIAL AND METHODS: In this single center study, FNAB was performed on 505 patients with single thyroid nodules greater than 10 mm. Nodule volume was calculated prior to FNAB and cystic degeneration ratio was recorded. All biopsies were performed by a single radiologist who also prepared specimen slides. Specimen adequacy and final diagnosis were made in the pathology laboratory by a single-blinded cytopathologist based on the Bethesda system. Definitive cytological diagnosis time was recorded upon reaching a definitive diagnosis. RESULTS: The specimen adequacy ratio was 85.3%. The mean nodule volume of adequate specimens was larger than those of non-diagnostic samples (6.00 mL vs. 3.05 mL; P = 0.001). There was no correlation between nodule volume and cytopathologist's definitive cytological diagnosis time (r = 0.042). Biopsy of predominantly solid nodules yielded better specimen adequacy ratios compared to predominantly cystic nodules (87.8% vs. 75.3%; P = 0.028). Definitive cytological diagnosis times were longer in predominantly cystic nodules compared to predominantly solid nodules (376 s vs. 294 s; P = 0.019). CONCLUSION: Predominantly cystic nodules are likely to benefit from repeated nodular sampling until the specimen is declared adequate by an on-site cytopathologist. If a cytopathologist is not available, obtaining more specimens per nodule may achieve desired adequacy ratios.

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.

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.

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.

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.

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.

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.

Survey on a mammographic screening program in Istanbul, Turkey.

Breast cancers in Turkey tend to be diagnosed at advanced stages due to lack of organized comprehensive mammographic screening. In this study, factors associated with having a mammogram among healthy women of screening age in Bahcesehir county, a region in Istanbul, were investigated to assess the feasibility of organized breast cancer screening in Turkey. In this cross-sectional study, 659 healthy women aged between 40 and 69 years were surveyed. A multiple-choice questionnaire was used to obtain information regarding patient demographics, family history of cancer, and patient knowledge on mammographic screening. Factors associated with increased likelihood of having a mammogram included age older than 50 (OR=1.75; 95% CI=1.23-2.49), higher educational level (high school or university graduate; OR=1.55; 95% CI=1.07-2.25), and undergoing periodic gynecologic examinations (OR=5.53; 95% CI= 3.88-7.89). Women aged between 40 and 49 years, who were most likely to have a mammogram within the last 2 years were characterized by a higher educational level (OR=1.94; 95% CI=1.14-3.31), periodic gynecologic examinations (OR=4.06; 95% CI=2.53-6.51), and a first or second degree family history of breast cancer (OR=2.2; 95% CI= 1.06-4.50). In contrast, women aged between 50 and 69 years were more likely to have undergone mammography within the previous 2 years if they also had undergone periodic gynecologic examinations (OR=8.63; 5.04-14.77). Our findings suggest that women of lower educational level and those who do not undergo routine wellness visits with their gynecologist will need to be specifically targeted for educational outreach to achieve broad screening compliance within the population.