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Mammography and MRI screening typically occur in combination or in alternating sequence. We compared multimodality screening performance accounting for the relative timing of mammography and MRI and overlapping follow-up periods. We identified 8,260 screening mammograms performed 2005 to 2017 in the Breast Cancer Surveillance Consortium, paired with screening MRIs within ±90 days (combined screening) or 91 to 270 days (alternating screening). Performance for combined screening (cancer detection rate [CDR] per 1,000 examinations and sensitivity) was calculated with 1-year follow-up for each modality, and with a single follow-up period treating the two tests as a single test. Alternating screening performance was calculated with 1-year follow-up for each modality and also with follow-up ending at the next screen if within 1 year (truncated follow-up). For 3,810 combined screening pairs, CDR per 1,000 screens was 6.8 (95% confidence interval [CI]: 4.6-10.0) for mammography and 12.3 (95% CI: 9.3-16.4) for MRI as separate tests compared with 13.1 (95% CI: 10.0-17.3) as a single combined test. Sensitivity of each test was 48.1% (35.0%-61.5%) for mammography and 79.7% (95% CI: 67.7%-88.0%) for MRI compared with 96.2% (95% CI: 85.9%-99.0%) for combined screening. For 4,450 alternating screening pairs, mammography CDR per 1,000 screens changed from 3.6 (95% CI: 2.2-5.9) to zero with truncated follow-up; sensitivity was incalculable (denominator = 0). MRI CDR per 1,000 screens changed from 12.1 (95% CI 9.3-15.8) to 11.7 (95% CI: 8.9-15.3) with truncated follow-up; sensitivity changed from 75.0% (95% CI 63.8%-83.6%) to 86.7% (95% CI 75.5%-93.2%). Updating auditing approaches to account for combined and alternating screening sequencing and to address outcome attribution issues arising from overlapping follow-up periods can improve the accuracy of multimodality screening performance evaluation. [Display omitted] [ABSTRACT FROM AUTHOR]

Contrast-enhanced breast magnetic resonance imaging (MRI) is currently recommended as a screening tool for high-risk women and has been advocated for women with radiologically dense breast tissue. While breast MRI is acknowledged for its high sensitivity (with an exception for lower-grade ductal carcinoma in situ (DCIS) where emerging techniques like diffusion-weighted imaging offer improvement), its limitations include sensitivity to hormonal changes and a relatively high false-positive rate, potentially leading to overdiagnosis, increased imaging uncertainty, and unnecessary biopsies. These factors can exacerbate patient anxiety and impose additional costs. Molecular imaging with breast-targeted Positron Emission Tomography (PET) has shown the capability to detect malignancy independent of breast density and hormonal changes. Furthermore, breast-targeted PET has shown higher specificity when compared with MRI. However, traditional PET technology is associated with high radiation dose, which can limit its widespread use particularly in repeated studies or for undiagnosed patients. In this case report, we present a clinical application of low-dose breast imaging utilizing a breast-targeted PET camera (Radialis PET imager, Radialis Inc). The case involves a 33-year-old female patient who had multiple enhanced lesions detected on breast MRI after surgical removal of a malignant phyllodes tumor from the right breast. A benign core biopsy was obtained from the largest lesion seen in the left breast. One month after the MRI, 18F-fluorodeoxyglucose (18F-FDG) PET imaging session was performed using the Radialis PET Imager. Although the Radialis PET Imager has proven high count sensitivity and the capability to detect breast lesions with low metabolic activity (at a dose similar to mammography), no areas of increased 18F-FDG uptake were visualized in this particular case. The patient underwent a right-sided nipple-sparing mastectomy and left-sided lumpectomy, with bilateral reconstruction. The excised left breast tissue was completely benign, as suggested by both core biopsy and the PET results. The case presented highlights a promising clinical application of low-dose breast-targeted PET imaging to mitigate the uncertainty associated with MRI while keeping radiation doses within the safe range typically used in X-ray mammography. [ABSTRACT FROM AUTHOR]

Background: Assessment of treatment response in triple‐negative breast cancer (TNBC) may guide individualized care for improved patient outcomes. Diffusion tensor imaging (DTI) measures tissue anisotropy and could be useful for characterizing changes in the tumors and adjacent fibroglandular tissue (FGT) of TNBC patients undergoing neoadjuvant systemic treatment (NAST). Purpose: To evaluate the potential of DTI parameters for prediction of treatment response in TNBC patients undergoing NAST. Study Type: Prospective. Population: Eighty‐six women (average age: 51 ± 11 years) with biopsy‐proven clinical stage I–III TNBC who underwent NAST followed by definitive surgery. 47% of patients (40/86) had pathologic complete response (pCR). Field Strength/Sequence: 3.0 T/reduced field of view single‐shot echo‐planar DTI sequence. Assessment: Three MRI scans were acquired longitudinally (pre‐treatment, after 2 cycles of NAST, and after 4 cycles of NAST). Eleven histogram features were extracted from DTI parameter maps of tumors, a peritumoral region (PTR), and FGT in the ipsilateral breast. DTI parameters included apparent diffusion coefficients and relative diffusion anisotropies. pCR status was determined at surgery. Statistical Tests: Longitudinal changes of DTI features were tested for discrimination of pCR using Mann–Whitney U test and area under the receiver operating characteristic curve (AUC). A P value <0.05 was considered statistically significant. Results: 47% of patients (40/86) had pCR. DTI parameters assessed after 2 and 4 cycles of NAST were significantly different between pCR and non‐pCR patients when compared between tumors, PTRs, and FGTs. The median surface/average anisotropy of the PTR, measured after 2 and 4 cycles of NAST, increased in pCR patients and decreased in non‐pCR patients (AUC: 0.78; 0.027 ± 0.043 vs. −0.017 ± 0.042 mm2/s). Data Conclusion: Quantitative DTI features from breast tumors and the peritumoral tissue may be useful for predicting the response to NAST in TNBC. Evidence Level: 1 Technical Efficacy: Stage 4 [ABSTRACT FROM AUTHOR]

Current breast cancer screening relies on mammography, digital breast tomosynthesis and breast ultrasound. In select populations, breast MRI is also of great utility. However, multiple factors limit widespread use of breast MRI for screening. Efforts have been made to increase the availability of breast MRI for screening, in large part due to the increased cancer detection rate of breast MRI compared to mammography. Techniques include shortening standard breast MRI protocols with the potential for accommodating MRI screening in a higher number of patients. This review will explain the role of abbreviated breast MRI and ultrafast breast MRI in breast imaging, and detail how these approaches differ from standard dynamic contrast-enhanced breast MRI. In addition, limitations and advantages of these techniques will also be discussed. [ABSTRACT FROM AUTHOR]

For patients with complete breast resection, conventional contrast-enhanced T1-weighted imaging (CE-T1WI) with frequency-selective spectral attenuated inversion recovery (SPAIR) provides limited fat suppression on the postoperative side due to the uneven skin surface, inhomogeneous tissue environment, and frequency-selective feature of the SPAIR scheme, leading to difficulties in precise diagnosis. This study aimed to investigate the image quality and performance of the Dixon method compared with SPAIR in breast high-resolution CE-T1WI for mastectomy patients. Sixty female patients who had not performed any breast surgeries were randomly selected retrospectively as the control group. Postmastectomy female patients were enrolled to undergone high-resolution CE-T1WI with SPAIR and Dixon breast scans. Subjective scores were rated using a 5-point scale. Objective parameters, including contrast-to-noise ratio (CNR), edge sharpness, and signal uniformity were measured and calculated. The Wilcoxon rank-sum test and Kappa statistic were used. A total of 114 consecutive postmastectomy patients were included. Subjective scores of T1WI-SPAIR in the control group were all significantly better than those with SPAIR on the postoperative side of mastectomy patients (P < 0.01). Dixon outperformed SPAIR with significantly better subjective scores in regards to uniformity and degree of fat-suppression, anatomical structures depiction, lesion conspicuity, and axillary visibility (p < 0.05) in both post- and non-operative sides and bilateral axillary areas through the paired comparison. The objective parameters of Dixon were significantly better than those of SPAIR. The Dixon method provided better image uniformity and higher fat suppression efficiency, and showed significant advantages in delineating the anatomical structures, with better axillary and lesion visibilities, especially on the completely removed breast side. [ABSTRACT FROM AUTHOR]

Background/Aim: This study examined the influence of preoperative MRI on the choice of implant volume in patients undergoing subcutaneous mastectomy with immediate breast reconstruction. It was postulated that preoperative MRI scans can adequately estimate glandular tissue, which in turn correlates with implant size. Patients and Methods: Preoperative and postoperative MRI scans were used in oncological and prophylactical subcutaneous mastectomy scenarios in 67 cases at the Department of Gynaecology, Breast Cancer Center, University of Cologne, Germany. The preoperative MRI was used to estimate the resected tissue and the postoperative MRI was used to scan for residual glandular tissue. In addition, a correlation found by Malter et al. in 2021 was evaluated with the available data. Results: Preoperative MRIs result in an adequate estimation of resected tissue. This in turn correlates with implant volume. The correlation by Malter et al. also holds when estimating implant volume. The likelihood of residual gland was low if the preoperatively estimate volume was removed. Conclusion: Our results indicate that the use of preoperative and postoperative MRI scans for subcutaneous mastectomies is advantageous. We suggest a routine estimation of glandular tissue, especially for small breasts. [ABSTRACT FROM AUTHOR]

Breast radiologists encounter unusual lesions, which may not be well described in the literature. Previously based on histologic and molecular classifications, the World Health Organization (WHO) classification of tumors has become increasingly multidisciplinary. Familiarity with imaging features and basic pathology of infrequent breast lesions, as well as their current classification according to the WHO, may help the radiologist evaluate biopsy results for concordance and help direct the management of uncommon breast lesions. This review article provides a case-based review of imaging features and WHO histologic classification of rare breast tumors. [ABSTRACT FROM AUTHOR]

BackgroundBackground parenchymal enhancement (BPE) is an established breast cancer risk factor. However, the relationship between BPE levels and breast cancer risk stratification remains unclear.PurposeTo evaluate the clinical relationship between BPE levels and breast cancer risk with covariate adjustments for age, ethnicity, and hormonal status.Study typeRetrospective.Population954 screening breast MRI datasets representing 721 women divided into four cohorts: women with pathogenic germline breast cancer (BRCA) mutations (Group 1, N = 211), women with non-BRCA germline mutations (Group 2, N = 60), women without high-risk germline mutations but with a lifetime breast cancer risk of ≥20% using the Tyrer-Cuzick model (Group 3, N = 362), and women with

Rationale and objectivesDiagnosis of breast cancer on MRI requires, first, the identification of suspicious lesions; second, the characterization to give a diagnostic impression. We implemented Mask Reginal-Convolutional Neural Network (R-CNN) to detect abnormal lesions, followed by ResNet50 to estimate the malignancy probability.Materials and methodsTwo datasets were used. The first set had 176 cases, 103 cancer, and 73 benign. The second set had 84 cases, 53 cancer, and 31 benign. For detection, the pre-contrast image and the subtraction images of left and right breasts were used as inputs, so the symmetry could be considered. The detected suspicious area was characterized by ResNet50, using three DCE parametric maps as inputs. The results obtained using slice-based analyses were combined to give a lesion-based diagnosis.ResultsIn the first dataset, 101 of 103 cancers were detected by Mask R-CNN as suspicious, and 99 of 101 were correctly classified by ResNet50 as cancer, with a sensitivity of 99/103 = 96%. 48 of 73 benign lesions and 131 normal areas were identified as suspicious. Following classification by ResNet50, only 16 benign and 16 normal areas remained as malignant. The second dataset was used for independent testing. The sensitivity was 43/53 = 81%. Of the total of 121 identified non-cancerous lesions, only 6 of 31 benign lesions and 22 normal tissues were classified as malignant.ConclusionResNet50 could eliminate approximately 80% of false positives detected by Mask R-CNN. Combining Mask R-CNN and ResNet50 has the potential to develop a fully-automatic computer-aided diagnostic system for breast cancer on MRI.

Male breast cancer is a rare entity consisting of less than 1% of all breast cancer diagnoses, in which pure ductal carcinoma in situ (DCIS) without an invasive component accounts for approximately 10% of these diagnoses. Early diagnosis and appropriate management are essential to ensure favorable outcomes. We present a rare case of mammographically and sonographically occult pure DCIS in a male patient presenting with unilateral bloody nipple discharge, highlighting imaging features and the potential utility of breast MRI that aided diagnosis and management.

Background Early detection of breast cancer significantly improves treatment outcomes and survival rates. While mammography is the standard screening tool, breast MRI is increasingly being used due to its higher sensitivity. This study aims to compare the diagnostic accuracy of mammography and breast MRI in detecting early breast cancer. Methods A total of 100 women aged 35-70 years (mean age: 52) participated in this study. Each participant underwent both mammography and breast MRI. Diagnostic measures calculated included true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). From these, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were derived for both diagnostic methods. Results Mammography identified 60 TP, 25 TN, 10 FP, and 5 FN cases. The sensitivity of mammography was 92.3%, specificity was 71.4%, PPV was 85.7%, NPV was 83.3%, and accuracy was 85.0%. Breast MRI identified 62 TP, 30 TN, 5 FP, and 3 FN cases. The sensitivity of breast MRI was 95.4%, specificity was 85.7%, PPV was 92.5%, NPV was 90.9%, and accuracy was 92.0%. Comparative analysis showed that breast MRI had higher sensitivity, specificity, PPV, NPV, and overall accuracy than mammography. Conclusion Breast MRI outperforms mammography in detecting early breast cancer, exhibiting superior sensitivity, specificity, PPV, NPV, and accuracy. Breast MRI identified more true positive cases and had fewer false negatives and false positives. These results suggest that breast MRI may be a more reliable diagnostic tool for early breast cancer detection. However, factors such as cost, availability, and patient-specific circumstances should be considered when choosing the diagnostic method. [ABSTRACT FROM AUTHOR]

Purpose: To assess the diagnostic performance of breast MRI for BI-RADS 4A microcalcifications on mammography and propose a potential clinical pathway to avoid unnecessary biopsies. Methods: Bibliometrics analysis of breast MRI and BI-RADS 4 was provided. A retrospective analysis was conducted on 139 women and 142 cases of BI-RADS 4A microcalcifications on mammography from Fudan University Shanghai Cancer Center. The mammographic BI-RADS level and the MRI reports were compared with the final pathological diagnosis. Results: Much attention has been given to breast MRI and BI-RADS 4 in the literature. However, studies on BI-RADS 4A are limited. Pathological results showed 117 cases (82.4%) were benign lesions, malignant cases of 25 (17.6%) in our study. The positive predictive values (PPV), specificity, sensitivity and negative predictive values (NPV) of MRI were 44.2% (23/52), 75.2% (88/117), 92.0% (23/25), and 97.8% (88/90), respectively. Therefore, 75.2% (88/117) of biopsies for benign lesions could potentially be avoided. There were 2.2% (2/90) malignant lesions missed. Logistic regression indicated that patients who are postmenopausal (HR = 2.655, p = 0.012), have a history of breast cancer (family history) (HR = 2.833, p = 0.029), and exhibit clustered microcalcifications (HR = 2.179, p = 0.046) are more likely to have a higher MRI BI-RADS level. Conclusions: Breast MRI has the potential to improve the diagnosis of BI-RADS 4A microcalcifications on mammography. We propose a potential clinical pathway that patients with BI-RADS 4A on mammography who are premenopausal, have no personal history of breast cancer (family history) or have non-clustered distribution of calcifications can undergo MRI to avoid unnecessary biopsies. [ABSTRACT FROM AUTHOR]

With the growing utilization and expanding role of breast MRI, breast imaging radiologists may encounter an increasing number of incidental findings beyond the breast and axilla. Breast MRI encompasses a large area of anatomic coverage extending from the lower neck to the upper abdomen. While most incidental findings on breast MRI are benign, identifying metastatic disease can have a substantial impact on staging, prognosis, and treatment. Breast imaging radiologists should be familiar with common sites, MRI features, and breast cancer subtypes associated with metastatic disease to assist in differentiating malignant from benign findings. Furthermore, detection of malignancies of nonbreast origin as well as nonmalignant, but clinically relevant, incidental findings can significantly impact clinical management and patient outcomes. Breast imaging radiologists should consistently follow a comprehensive search pattern and employ techniques to improve the detection of these important incidental findings. [ABSTRACT FROM AUTHOR]

Background High mammographic density increases breast cancer risk and reduces mammographic sensitivity. We reviewed evidence on accuracy of supplemental MRI for women with dense breasts at average or increased risk. Methods PubMed and Embase were searched 1995-2022. Articles were included if women received breast MRI following 2D or tomosynthesis mammography. Risk of bias was assessed using QUADAS-2. Analysis used independent studies from the articles. Fixed-effect meta-analytic summaries were estimated for predefined groups (PROSPERO: 230277). Results Eighteen primary research articles (24 studies) were identified in women aged 19-87 years. Breast density was heterogeneously or extremely dense (BI-RADS C/D) in 15/18 articles and extremely dense (BI-RADS D) in 3/18 articles. Twelve of 18 articles reported on increased-risk populations. Following 21 440 negative mammographic examinations, 288/320 cancers were detected by MRI. Substantial variation was observed between studies in MRI cancer detection rate, partly associated with prevalent vs incident MRI exams (prevalent: 16.6/1000 exams, 12 studies; incident: 6.8/1000 exams, 7 studies). MRI had high sensitivity for mammographically occult cancer (20 studies with at least 1-year follow-up). In 5/18 articles with sufficient data to estimate relative MRI detection rate, approximately 2 in 3 cancers were detected by MRI (66.3%, 95% CI, 56.3%-75.5%) but not mammography. Positive predictive value was higher for more recent studies. Risk of bias was low in most studies. Conclusion Supplemental breast MRI following negative mammography in women with dense breasts has breast cancer detection rates of ~16.6/1000 at prevalent and ~6.8/1000 at incident MRI exams, considering both high and average risk settings. [ABSTRACT FROM AUTHOR]

Purpose: Mitigating false negative imaging studies remains an important issue given its association with worse morbidity and mortality in patients with breast cancer. We aimed to identify risk factors that predispose to false negative breast imaging exams. Methods: In an IRB-approved, HIPAA compliant retrospective study, we identified all patients who were diagnosed with breast cancer within 365 days of a negative imaging study assessed as BI-RADS 1-3 between January 1, 2014 and January 31, 2020. A matched cohort based on mammographic breast density was created from randomly selected studies with BI-RADS 4-5 designation that yielded breast cancer at pathology within the same time frame. Patient and cancer characteristics, prior personal history of breast cancer and gene mutation status were collected from patient charts. Pearson chi-squared and Student's t-test on two independent groups with significance at < 0.05 was used for statistical analysis. Results: We identified 155 false negative studies of 129 missed cancers and 128 breast density matched true positive cancers. False negative studies were screening mammograms in 57.42% (89/155), diagnostic mammograms in 29.68% (46/155), ultrasounds in 6.45% (10/155) and MRIs in 6.45% (10/155). Rates of personal (41.09% vs. 18.75%, p < 0.001) and family history of breast cancer (68.22% vs. 49.21%, p = 0.002) were higher in the false negative cohort and remained significant when asymptomatic MRI-detected cancers were removed. Conclusion: Our findings suggest that supplemental screening may be useful in breast cancer survivors. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast MRI is a valuable imaging modality that plays a critical role in the detection, diagnosis, and treatment of breast cancer. The superior sensitivity of breast MRI allows for more accurate evaluation of the extent of disease for breast cancer, which is important in an era where de-escalation of treatment is being considered and offered. In this review, we provided a detailed overview of breast MRI and its clinical utility in the management of breast cancer as a foundation for its use in the field of radiation oncology. Specifically, we describe breast MRI technique, anatomy, common breast MR imaging findings, and the role of breast MRI in assessing extent of disease and tumor response to neoadjuvant therapy. There has been increased interest in the use of MRI during radiotherapy treatment planning and delivery for patients with early-stage breast cancer as it may allow for more personalized radiotherapy. Contrast-enhanced breast MRI has an established role in aiding in the detection, evaluation, and management of breast cancer. This article discusses MRI sequences, the clinical utility of MRI, and how MRI has been evaluated for use in breast radiotherapy treatment planning. We highlight the contribution of MRI in the decision-making regarding selecting appropriate candidates for breast conservation therapy and review the emerging role of MRI-guided breast radiotherapy. [ABSTRACT FROM AUTHOR]

Early detection of breast cancer from regular screening substantially reduces breast cancer mortality and morbidity. Multiple different imaging modalities may be used to screen for breast cancer. Screening recommendations differ based on an individual's risk of developing breast cancer. Numerous factors contribute to breast cancer risk, which is frequently divided into three major categories: average, intermediate, and high risk. For patients assigned female at birth with native breast tissue, mammography and digital breast tomosynthesis are the recommended method for breast cancer screening in all risk categories. In addition to the recommendation of mammography and digital breast tomosynthesis in high-risk patients, screening with breast MRI is recommended. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation. [ABSTRACT FROM AUTHOR]

Purpose of Review: Since its approval by the Unites States Food and Drug Administration (FDA) in 2015, cone-beam breast computed tomography (CBBCT) has gained acceptance among radiologists for breast cancer imaging. This review aims to highlight the advancements and benefits of CBBCT in the diagnostic workup of breast disease. It showcases how CBBCT, including both non-contrast (NC-CBBCT) and contrast-enhanced (CE-CBBCT) protocols, complements and often surpasses the performance of more traditional breast imaging modalities such as mammography and magnetic resonance imaging (MRI). Recent Findings: Studies in clinical settings have shown CBBCT's efficacy in detecting and characterizing breast lesions of differing morphologies, including non-mass enhancement and calcifications—tasks that previously required the use of multiple modalities. In addition, CBBCT significantly enhances patient comfort and efficiency, offering quick acquisition times without the discomfort of breast compression. The technology can be utilized for guiding biopsies, planning surgical interventions, and assessing breast density and tumor characteristics, evidence supporting its integration into clinical practice. Summary: CBBCT holds the potential to shift the imaging paradigm in breast cancer care, indicating a promising future for the modality in terms of enhancing diagnostic accuracy, improving patient experience, and influencing treatment outcomes. [ABSTRACT FROM AUTHOR]

Abstract Background Non-mass enhancement (NME) seen on dynamic contrast enhanced breast MRI (DCE-MRI) may be caused by benign, high risk or malignant lesions. Making a clear distinction between these lesions is challenging due to the significant overlap in their imaging appearance. Our study aims to assess the various patterns of distribution, internal enhancement patterns (IEPs) and kinetics of NME using the BI-RADS lexicon fifth edition with histopathologic correlation to aid in making a more confident recommendation regarding clinical management. Results Sixty-six female patients with NME on DCE-MRI were included. Thirty-four lesions (51.5%) were histopathologically proven to be benign and 32 (48.5%) were malignant. Segmental distribution was the most common pattern and was found in 22 cases (33.3%), 14 of them were malignant with p-value 0.05. Thirteen cases (19.7%) had focal distribution, only two of them were malignant with p-value 0.05. Restricted diffusion value was detected in 75% of malignant cases with p-value

Abstract Background This study investigated the feasibility of sentinel lymph node biopsy (SLNB) after neoadjuvant systemic therapy (NAST) in patients with initially high nodal burden. Methods In the multicenter retrospective cohort, 388 individuals with cN1–3 breast cancer who underwent NAST and had SLNB followed by completion axillary lymph node dissection were included. In an external validation cohort, 267 patients with HER2+ or triple-negative breast cancer (TNBC) meeting similar inclusion criteria were included. Primary outcome was the false-negative rates (FNRs) of SLNB according to the MRI response and subtypes. We defined complete MRI responders as patients who experienced disappearance of suspicious features in the breast and axilla after NAST. Results In the multicenter retrospective cohort, 130 (33.5%) of 388 patients were of cN2-3, and 55 (14.2%) of 388 patients showed complete MRI responses. In hormone receptor-positive HER2− (n = 207), complete and non-complete responders had a high FNRs (31.3% [95% CI 8.6–54.0] and 20.9% [95% CI 14.1–27.6], respectively). However, in HER2+ or TNBC (n = 181), the FNR of complete MRI responders was 0% (95% CI 0–0), whereas that of non-complete responders was 33.3% (95% CI 20.8–45.9). When we validated our findings in the external cohort with HER2+ or TNBC (n = 267), of which 34.2% were cN2-3, the FNRs of complete were 7.1% (95% CI 0–16.7). Conclusions Our findings suggest that SLNB can be a reliable option for nodal status evaluation in selected patients who have responded well to NAST, especially in HER2+ and TNBC patients who show a complete MRI response.

Bilateral breast cancer in males is an exceedingly rare diagnosis. In this case report, we will discuss the ultrafast sequence findings of a bilateral male breast cancer with different subtypes on his staging dynamic contrast enhanced (DCE) MRI with ultrafast technique. A 94-year-old male presented with bilateral palpable complaints in his breasts. Diagnostic mammography and ultrasound images demonstrated bilateral irregular masses with nipple retraction. Biopsies were performed and the histopathologic examination revealed invasive breast carcinoma of no special type in 1 breast and invasive micropapillary carcinoma in the other breast. Staging MRI with ultrafast sequence showed significant enhancement differences between 2 different subtypes, correlating with the different levels of tumor aggressiveness. Different ultrafast metrics, such as time-to-enhancement and maximum slope, may help to differentiate between several subtypes of breast cancer and serve as prognostic indicators. This case report discusses the application of ultrafast sequence in predicting breast cancer subtypes in a male patient with bilateral disease.

Abstract Background Early hormone-positive breast cancers typically have favorable outcomes, yet long-term surveillance is crucial due to the risk of late recurrences. While many studies associate MMP-11 expression with poor prognosis in breast cancer, few focus on early-stage cases. This study explores MMP-11 as an early prognostic marker in hormone-positive breast cancers. Methods In this retrospective study, 228 women with early hormone-positive invasive ductal carcinoma, treated surgically between 2011 and 2016, were included. MMP-11 expression was measured by immunohistochemistry, and its association with clinical and MRI data was analyzed. Results Among the patients (aged 31–89, median 60, with average tumor size of 15.7 mm), MMP-11 staining was observed in half of the cases. This positivity correlated with higher uPA levels and tumor grade but not with nodal status or size. Furthermore, MMP-11 positivity showed specific associations with MRI features. Over a follow-up period of 6.5 years, only 12 oncological events occurred. Disease-free survival was linked to Ki67 and MMP-11. Conclusion MMP-11, primarily present in tumor-surrounding stromal cells, correlates with tumor grade and uPA levels. MMP-11 immunohistochemical score demonstrates a suggestive trend in association with disease-free survival, independent of Ki67 and other traditional prognostic factors. This highlights the potential of MMP-11 as a valuable marker in managing early hormone-positive breast cancer.

Objectives: To examine the background parenchymal enhancement (BPE) levels in peri-menopausal breast MRI compared with pre- and post-menopausal breast MRI. Methods: This study included 562 patients (55.8±12.3 years) who underwent contrast-enhanced dynamic breast MRI between 2011 and 2015 for clinical indications. We evaluated the BPE level, amount of fibroglandular tissue (FGT), and social and clinical variables. The inter-reader agreement for the amount of FGT and the BPE level was evaluated using interclass correlation coefficients. Associations between the BPE level and body mass index (BMI), ages of menarche and menopause, childbirth history, number of children, and the amount of FGT were determined using Spearman’s correlation coefficients or Mann-Whitney U-test. Pearson’s χ2 test was used to assess the difference in the frequency of BPE categories among the age-groups. Results: The inter-reader agreement was 0.864 for the amount of FGT and 0.840 for the BPE level, both indicating almost perfect agreement. The BPE level showed a weak positive correlation with the amount of FGT (Spearman’s ρ=0.271, P

PurposeDynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy.Experimental designBreast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint.ResultsTen patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC.ConclusionThe automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.

Purpose: Preoperative breast MRI has been recommended at our center since 2016 for invasive lobular carcinoma and cancers in dense breasts. This study examined how preoperative breast MRI impacted surgical timing and outcomes for patients with newly diagnosed breast cancer. Methods: Retrospective single-center study of consecutive women diagnosed with new breast cancer between June 1, 2019, and March 1, 2021, in whom preoperative breast MRI was recommended. MRI, tumour histology, breast density, post-MRI biopsy, positive predictive value of biopsy (PPV3), surgery, and margin status were recorded. Time from diagnosis to surgery was compared using t -tests. Results: There were 1054 patients reviewed, and 356 were included (mean age 60.9). Of these, 44.4% (158/356) underwent preoperative breast MRI, and 55.6% (198/356) did not. MRI referral was more likely for invasive lobular carcinoma, multifocal disease, and younger patients. Following preoperative MRI, 29.1% (46/158) patients required additional breast biopsies before surgery, for a PPV3 of 37% (17/46). The time between biopsy and surgery was 55.8 ± 21.4 days for patients with the MRI, compared to 42.8 ± 20.3 days for those without (P <.00001). MRI was not associated with the type of surgery (mastectomy vs breastconserving surgery) (P =.44) or rate of positive surgical margins (P =.52). Conclusion: Among patients who underwent preoperative breast MRI, we observed significant delays to surgery by almost 2 weeks. When preoperative MRI is requested, efforts should be made to mitigate associated delays. [ABSTRACT FROM AUTHOR]

Background: Non-mass enhancement (NME) seen on dynamic contrast enhanced breast MRI (DCE-MRI) may be caused by benign, high risk or malignant lesions. Making a clear distinction between these lesions is challenging due to the significant overlap in their imaging appearance. Our study aims to assess the various patterns of distribution, internal enhancement patterns (IEPs) and kinetics of NME using the BI-RADS lexicon fifth edition with histopathologic correlation to aid in making a more confident recommendation regarding clinical management. Results: Sixty-six female patients with NME on DCE-MRI were included. Thirty-four lesions (51.5%) were histopathologically proven to be benign and 32 (48.5%) were malignant. Segmental distribution was the most common pattern and was found in 22 cases (33.3%), 14 of them were malignant with p-value < 0.05. Linear distribution was reported in 14 cases, (21.2%), five of which were malignant, with p-value > 0.05. Thirteen cases (19.7%) had focal distribution, only two of them were malignant with p-value < 0.05. Twelve cases (18.2%) were of regional distribution, seven of which were malignant. Multiregional and diffuse distribution were the least common and were found in 3% and 4.5% of cases respectively. As for the enhancement pattern, 30 cases (45.5%) had heterogeneous enhancement. Nineteen of which were malignant with a p-value < 0.05. Clumped enhancement was found in 24 cases (36.4%); 12 cases were found to be malignant. Nine cases (13.6%) were of homogeneous enhancement, all of them were benign and three cases (4.5%) were of clustered ring enhancement with p-value > 0.05. Restricted diffusion value was detected in 75% of malignant cases with p-value < 0.05. In terms of kinetic curve, the most frequent curve was found to be type II plateau curve (26 cases, 39.4%), 15 cases were of benign pathology and the other 11 cases were proven to be malignant. Followed by type III washout curve which was detected in 25 cases (37.9%), 20 cases were malignant and five cases were benign. And type I persistent curve was found in 15 cases (22.7%); 14 cases were histopathologically proven to be benign, and only one case was of malignant pathology, with a total p-value < 0.05. Conclusions: Our study found that the most common distribution pattern was segmental distribution, being statistically significant with p-value < 0.05, being more common among malignant lesions. As for the enhancement pattern, heterogeneous enhancement was the most common pattern, mainly detected in malignant lesions, with p-value < 0.05. The most common type of kinetic curve was type II curve. [ABSTRACT FROM AUTHOR]

Image quality assessment of magnetic resonance imaging (MRI) data is an important factor not only for conventional diagnosis and protocol optimization but also for fairness, trustworthiness, and robustness of artificial intelligence (AI) applications, especially on large heterogeneous datasets. Information on image quality in multi-centric studies is important to complement the contribution profile from each data node along with quantity information, especially when large variability is expected, and certain acceptance criteria apply. The main goal of this work was to present a tool enabling users to assess image quality based on both subjective criteria as well as objective image quality metrics used to support the decision on image quality based on evidence. The evaluation can be performed on both conventional and dynamic MRI acquisition protocols, while the latter is also checked longitudinally across dynamic series. The assessment provides an overall image quality score and information on the types of artifacts and degrading factors as well as a number of objective metrics for automated evaluation across series (BRISQUE score, Total Variation, PSNR, SSIM, FSIM, MS-SSIM). Moreover, the user can define specific regions of interest (ROIs) to calculate the regional signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thus individualizing the quality output to specific use cases, such as tissue-specific contrast or regional noise quantification. [ABSTRACT FROM AUTHOR]

Background: Background parenchymal enhancement (BPE) is an established breast cancer risk factor. However, the relationship between BPE levels and breast cancer risk stratification remains unclear. Purpose: To evaluate the clinical relationship between BPE levels and breast cancer risk with covariate adjustments for age, ethnicity, and hormonal status. Study Type: Retrospective. Population: 954 screening breast MRI datasets representing 721 women divided into four cohorts: women with pathogenic germline breast cancer (BRCA) mutations (Group 1, N = 211), women with non‐BRCA germline mutations (Group 2, N = 60), women without high‐risk germline mutations but with a lifetime breast cancer risk of ≥20% using the Tyrer‐Cuzick model (Group 3, N = 362), and women with <20% lifetime risk (Group 4, N = 88). Field Strength/Sequence: 3 T/axial non‐fat‐saturated T1, short tau inversion recovery, fat‐saturated pre‐contrast, and post‐contrast T1‐weighted images. Assessment: Data on age, body mass index, ethnicity, menopausal status, genetic predisposition, and hormonal therapy use were collected. BPE levels were evaluated by two breast fellowship‐trained radiologists independently in accordance with BI‐RADS, with a third breast fellowship‐trained radiologist resolving any discordance. Statistical Tests: Propensity score matching (PSM) was utilized to adjust covariates, including age, ethnicity, menopausal status, hormonal treatments, and prior bilateral oophorectomy. The Mann–Whitney U test, chi‐squared test, and univariate and multiple logistic regression analysis were performed, with an odds ratio (OR) and corresponding 95% confidence interval. Weighted Kappa statistic was used to assess inter‐reader variation. A P value <0.05 indicated a significant result. Results: In the assessment of BPE, there was substantial agreement between the two interpreting radiologists (κ = 0.74). Patient demographics were not significantly different between patient groups after PSM. The BPE of Group 1 was significantly lower than that of Group 4 and Group 3 among premenopausal women. In estimating the BPE level, the OR of gene mutations was 0.35. Data Conclusion: Adjusting for potential confounders, the BPE level of premenopausal women with BRCA mutations was significantly lower than that of non‐high‐risk women. Level of Evidence: 3 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Background: Approximately 8% of breast cancers originate within the central ducts near the nipple-areolar complex(NAC), which has revealed higher rates of occult involvement than previously thought. Precise preoperative imaging to assess NAC and subjacent tumor involvement has become critical to identify candidates for nipple-sparing mastectomy (NSM) while ensuring oncological safety. The aim of our study is to determine whether Magnetic Resonance Imaging (MRI) could be used for the detection of subtle neoplastic infiltration. Method: Electronic databases including PubMed, Scopus, Web of Science, and EMBASE were systematically searched to identify all relevant studies published before 2024 on diagnostic performance of MRI in malignant extension to the nipple. The keywords included MRI, NAC, breast cancer, NSM, True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN) were extracted for analysis. Finally, 5 articles were selected for our meta-analysis. STATA version 15 was used to analysis data. Result: Our meta-analysis showed a pooled sensitivity of 85%(95% CI: 77%-91%)with low heterogeneity(I2 = 00.00%), and a pooled specificity of 82% (95% CI: 49%-95%), with significant heterogeneity(I2 = 98%). The pooled positive diagnostic likelihood ratio(pDLR) was 4.6(95% CI: 1.3-16.6, I2 = 96.66%), and the negative diagnostic likelihood ratio(nDLR) was 0.18(95% CI: 0.10-0.33, I2 = 50.06%). Conclusion: Diagnostic MRI characteristics such as NAC enhancement, non-mass enhancement (NME) type, mass size (greater than 20 mm), and tumor-to-nipple distance (TND) were assocaited with the presence of NAC involement. Because of high diagnostic accuracy, MRI imaging can be used as a helpful tool for NAC diagnosis. [ABSTRACT FROM AUTHOR]

With increasing spatial resolution, diffusion-weighted imaging (DWI) may be suitable for morphologic lesion characterization in breast MRI – an area that has traditionally been occupied by dynamic contrast-enhanced imaging (DCE). This investigation compared DWI with b values of 800 and 1600 s/mm2 to DCE for lesion morphology assessment in high-resolution breast MRI at 3 Tesla. Multiparametric breast MRI was performed in 91 patients with 93 histopathologically proven lesions (31 benign, 62 malignant). Two radiologists independently evaluated three datasets per patient (DWI b800 ; DWI b1600 ; DCE) and assessed lesion visibility and BIRADS morphology criteria. Diagnostic accuracy was compared among readers and datasets using Cochran's Q test and pairwise post-hoc McNemar tests. Bland-Altman analyses were conducted for lesion size comparisons. Discrimination of carcinomas was superior compared to benign findings in both DWI b800 and DWI b1600 (p < 0.001) with no b value-dependent difference. Similarly, assessability of mass lesions was better than of non-mass lesions, irrespective of b value (p < 0.001). Intra-reader reliability for the analysis of morphologic BIRADS criteria among DCE and DWI datasets was at least moderate (Fleiss κ ≥0.557), while at least substantial inter-reader agreement was ascertained over all assessed categories (κ ≥0.776). In pairwise Bland-Altman analyses, the measurement bias between DCE and DWI b800 was 0.7 mm, whereas the difference between DCE and DWI b1600 was 2.8 mm. DWI b1600 allowed for higher specificity than DCE (p = 0.007/0.062). DWI can be employed for reliable morphologic lesion characterization in high-resolution breast MRI. High b values increase diagnostic specificity, while lesion size assessment is more precise with standard 800 s/mm2 images. [ABSTRACT FROM AUTHOR]

Background Early hormone-positive breast cancers typically have favorable outcomes, yet long-term surveillance is crucial due to the risk of late recurrences. While many studies associate MMP-11 expression with poor prognosis in breast cancer, few focus on early-stage cases. This study explores MMP-11 as an early prognostic marker in hormonepositive breast cancers. Methods In this retrospective study, 228 women with early hormone-positive invasive ductal carcinoma, treated surgically between 2011 and 2016, were included. MMP-11 expression was measured by immunohistochemistry, and its association with clinical and MRI data was analyzed. Results Among the patients (aged 31–89, median 60, with average tumor size of 15.7 mm), MMP-11 staining was observed in half of the cases. This positivity correlated with higher uPA levels and tumor grade but not with nodal status or size. Furthermore, MMP-11 positivity showed specific associations with MRI features. Over a follow-up period of 6.5 years, only 12 oncological events occurred. Disease-free survival was linked to Ki67 and MMP-11. Conclusion MMP-11, primarily present in tumor-surrounding stromal cells, correlates with tumor grade and uPA levels. MMP-11 immunohistochemical score demonstrates a suggestive trend in association with disease-free survival, independent of Ki67 and other traditional prognostic factors. This highlights the potential of MMP-11 as a valuable marker in managing early hormone-positive breast cancer. [ABSTRACT FROM AUTHOR]

Contrast-enhanced breast magnetic resonance imaging (MRI) is currently recommended as a screening tool for high-risk women and has been advocated for women with radiologically dense breast tissue. While breast MRI is acknowledged for its high sensitivity (with an exception for lower-grade ductal carcinoma in situ (DCIS) where emerging techniques like diffusion-weighted imaging offer improvement), its limitations include sensitivity to hormonal changes and a relatively high false-positive rate, potentially leading to overdiagnosis, increased imaging uncertainty, and unnecessary biopsies. These factors can exacerbate patient anxiety and impose additional costs. Molecular imaging with breast-targeted Positron Emission Tomography (PET) has shown the capability to detect malignancy independent of breast density and hormonal changes. Furthermore, breast-targeted PET has shown higher specificity when compared with MRI. However, traditional PET technology is associated with high radiation dose, which can limit its widespread use particularly in repeated studies or for undiagnosed patients. In this case report, we present a clinical application of low-dose breast imaging utilizing a breast-targeted PET camera (Radialis PET imager, Radialis Inc). The case involves a 33-year-old female patient who had multiple enhanced lesions detected on breast MRI after surgical removal of a malignant phyllodes tumor from the right breast. A benign core biopsy was obtained from the largest lesion seen in the left breast. One month after the MRI, 18F-fluorodeoxyglucose (18F-FDG) PET imaging session was performed using the Radialis PET Imager. Although the Radialis PET Imager has proven high count sensitivity and the capability to detect breast lesions with low metabolic activity (at a dose similar to mammography), no areas of increased 18F-FDG uptake were visualized in this particular case. The patient underwent a right-sided nipple-sparing mastectomy and left-sided lumpectomy, with bilateral reconstruction. The excised left breast tissue was completely benign, as suggested by both core biopsy and the PET results. The case presented highlights a promising clinical application of low-dose breast-targeted PET imaging to mitigate the uncertainty associated with MRI while keeping radiation doses within the safe range typically used in X-ray mammography.

Objective: Little is known about womens confidence in their breast cancer screening. We sought to characterize breast cancer screening confidence by imaging modality and clinically assessed breast density. Materials and Methods: We undertook a cross-sectional survey of women ages 40-74 years who received digital mammography (DM), digital breast tomosynthesis (DBT), and/or breast magnetic resonance imaging (MRI) with a normal screening exam in the prior year. The main outcome was womens confidence (Very, Somewhat, A little, Not at all) in their breast cancer screening detecting any cancer. Multivariable logistic regression identified correlates of being very confident in breast cancer screening by screening modality group: Group 1) DM vs. DBT and Group 2) DM or DBT alone vs. with supplemental MRI. Results: Overall, 2329 of 7439 (31.3%) invitees participated, with 30%-61% being very confident in their screening across modality and density subgroups. Having dense versus nondense breasts was associated with lower odds of being very confident (Group 1: odds ratio [OR]: 0.58; 95% confidence interval [CI]: 0.46-0.79; Group 2: OR: 0.56; 95% CI: 0.40-0.79). There were no differences by modality within Group 1, but for Group 2, women undergoing MRI had higher odds of being very confident (OR: 1.69; 95% CI: 1.21-2.37). Other correlates of greater screening confidence were as follows: Group 1-being offered a screening test choice and cost not influencing modality received, and Group 2-decision satisfaction and worry. Conclusions: Women with dense breasts had lower screening confidence regardless of screening modality and those undergoing MRI had higher confidence regardless of density. The importance of informing women about screening options is underscored by observed associations between screening choice, decision satisfaction, and screening confidence. ClinicalTrials.gov: NCT02980848.

BACKGROUND: Background parenchymal enhancement (BPE) is assessed on breast MRI reports as mandated by the Breast Imaging Reporting and Data System (BI-RADS) but is prone to inter and intrareader variation. Semiautomated and fully automated BPE assessment tools have been developed but none has surpassed radiologist BPE designations. PURPOSE: To develop a deep learning model for automated BPE classification and to compare its performance with current standard-of-care radiology report BPE designations. STUDY TYPE: Retrospective. POPULATION: Consecutive high-risk patients (i.e. >20% lifetime risk of breast cancer) who underwent contrast-enhanced screening breast MRI from October 2013 to January 2019. The study included 5224 breast MRIs, divided into 3998 training, 444 validation, and 782 testing exams. On radiology reports, 1286 exams were categorized as high BPE (i.e., marked or moderate) and 3938 as low BPE (i.e., mild or minimal). FIELD STRENGTH/SEQUENCE: A 1.5 T or 3 T system; one precontrast and three postcontrast phases of fat-saturated T1-weighted dynamic contrast-enhanced imaging. ASSESSMENT: Breast MRIs were used to develop two deep learning models (Slab artificial intelligence (AI); maximum intensity projection [MIP] AI) for BPE categorization using radiology report BPE labels. Models were tested on a heldout test sets using radiology report BPE and three-reader averaged consensus as the reference standards. STATISTICAL TESTS: Model performance was assessed using receiver operating characteristic curve analysis. Associations between high BPE and BI-RADS assessments were evaluated using McNemars chi-square test (α* = 0.025). RESULTS: The Slab AI model significantly outperformed the MIP AI model across the full test set (area under the curve of 0.84 vs. 0.79) using the radiology report reference standard. Using three-reader consensus BPE labels reference standard, our AI model significantly outperformed radiology report BPE labels. Finally, the AI model was significantly more likely than the radiologist to assign high BPE to suspicious breast MRIs and significantly less likely than the radiologist to assign high BPE to negative breast MRIs. DATA CONCLUSION: Fully automated BPE assessments for breast MRIs could be more accurate than BPE assessments from radiology reports. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 3.

Abstract Introduction Breast magnetic resonance imaging (MRI) is increasingly being used for staging of patients with breast cancer due to its high sensitivity in detecting additional cancers (ACs). However, the clinical impact of diagnosing and treating these cancers remains unclear. Methods A retrospective study was undertaken of patients with newly diagnosed breast cancer who underwent staging MRI at The American University of Beirut Medical Centre (AUBMC) between 2012 and 2020. Pathology reports and breast MRI examinations were reviewed. Eighteen breast cancer patients with 19 pathology‐proven index cancers (ICs) and 19 pathology‐proven MRI‐detected ACs were included. Chi‐square and Fisher's exact tests for categorical variables and Wilcoxon signed rank test for numerical variables were used to compare ICs to ACs. Results The ICs consisted of four ductal carcinoma in situ (DCIS), 13 invasive ductal carcinomas (IDC), of which five with associated DCIS, and two invasive lobular carcinomas, (ILC) of which one with associated DCIS. ACs comprised 12 DCIS, five IDC, two with associated DCIS and two ILC, one with associated DCIS. Interval cancers were more frequently invasive whereas ACs were more frequently in situ (P = 0.021). ACs were more frequently nuclear grade 2 (P = 0.009). There was no statistically significant difference between ICs and ACs in lesion type (P = 0.062), shape (P = 0.073), initial enhancement (P = 1), delayed enhancement (P = 0.732), hormonal receptor profile (P = 0.68) and Ki67 (P = 0.388). Among ACs, ten (53%) were larger than 10 mm of which five (26%) were invasive cancers, and five (26%) were larger than the ICs. Conclusions ACs detected by breast MRI were more likely to be in situ and to show a nuclear grade 2. Although not reaching statistical significance, some ACs tend to be clinically significant by their type, size or nuclear grade. The impact on clinical management remains to be determined.

PURPOSE: We evaluated self-report of decision quality and regret with breast cancer surgical treatment by pre-operative breast MRI use in women recently diagnosed with breast cancer. METHODS: We conducted a survey with 957 women aged 18 + with stage 0-III breast cancer identified in the Breast Cancer Surveillance Consortium. Participants self-reported receipt of pre-operative breast MRI. Primary outcomes were process measures in the Breast Cancer Surgery Decision Quality Instrument (BCS-DQI) (continuous outcome) and Decision Regret Scale (dichotomized outcome as any/none). Generalized estimating equations with linear and logit link were used to estimate adjusted associations between breast MRI and primary outcomes. All analyses were also stratified by breast density. RESULTS: Survey participation rate was 27.9% (957/3430). Study population was primarily > 60 years, White, college educated, and diagnosed with early-stage breast cancer. Pre-operative breast MRI was reported in 46% of women. A higher proportion of women who were younger age (

Cryoablation is a minimally invasive technique currently employed in breast cancer care, that uses freeze and thaw cycles to treat benign breast lesions, small breast cancers or focal sites of metastatic disease in patients not eligible for surgery. The final goal of this procedure is to destroy breast cancer cells using extreme cold. In addition, several studies have shown that this technique seems to have an enhancing effect on the immune response, especially by increasing the expression of tumor neoantigens specific to tumor cells, which are then attacked and destroyed. Exploiting this effect, cryoablation in combination with immunotherapy could be the key to treating early-stage breast cancers or patients who are unsuitable for surgery. According to some recent studies, there are other potential tools that could be used to enhance the therapeutic effect of cryoablation, such as FE3O4 nanoparticles or the manipulation of aquaporin expression. The aim of this narrative review is to summarize the current evidence regarding the use, indications, advantages and disadvantages of cryoablation in the treatment of breast cancer. [ABSTRACT FROM AUTHOR]

Background: Deep learning (DL) have been reported feasible in breast MRI. However, the effectiveness of DL method in mpMRI combinations for breast cancer detection has not been well investigated. Purpose: To implement a DL method for breast cancer classification and detection using feature extraction and combination from multiple sequences. Study Type: Retrospective. Population: A total of 569 local cases as internal cohort (50.2 ± 11.2 years; 100% female), divided among training (218), validation (73) and testing (278); 125 cases from a public dataset as the external cohort (53.6 ± 11.5 years; 100% female). Field Strength/Sequence: T1‐weighted imaging and dynamic contrast‐enhanced MRI (DCE‐MRI) with gradient echo sequences, T2‐weighted imaging (T2WI) with spin‐echo sequences, diffusion‐weighted imaging with single‐shot echo‐planar sequence and at 1.5‐T. Assessment: A convolutional neural network and long short‐term memory cascaded network was implemented for lesion classification with histopathology as the ground truth for malignant and benign categories and contralateral breasts as healthy category in internal/external cohorts. BI‐RADS categories were assessed by three independent radiologists as comparison, and class activation map was employed for lesion localization in internal cohort. The classification and localization performances were assessed with DCE‐MRI and non‐DCE sequences, respectively. Statistical Tests: Sensitivity, specificity, area under the curve (AUC), DeLong test, and Cohen's kappa for lesion classification. Sensitivity and mean squared error for localization. A P‐value <0.05 was considered statistically significant. Results: With the optimized mpMRI combinations, the lesion classification achieved an AUC = 0.98/0.91, sensitivity = 0.96/0.83 in the internal/external cohorts, respectively. Without DCE‐MRI, the DL‐based method was superior to radiologists' readings (AUC 0.96 vs. 0.90). The lesion localization achieved sensitivities of 0.97/0.93 with DCE‐MRI/T2WI alone, respectively. Data Conclusion: The DL method achieved high accuracy for lesion detection in the internal/external cohorts. The classification performance with a contrast agent‐free combination is comparable to DCE‐MRI alone and the radiologists' reading in AUC and sensitivity. Evidence Level: 3. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]

Objective To evaluate the diagnostic performance outcomes of a breast MRI screening program in high-risk women without prior history of breast cancer. Methods Retrospective cohort study of 1 405 consecutive screening breast MRI examinations in 681 asymptomatic women with high risk of breast cancer without prior history of breast cancer from January 1, 2015, to December 31, 2019. Outcomes (sensitivity, specificity, positive predictive value, negative predictive value, false-negative rate [FNR], cancer detection rate [CDR]) and characteristics of cancers were determined based on histopathology or 12-month follow-up. MRI examinations performed, BI-RADS assessments, pathology outcomes, and CDRs were analyzed overall and by age decade. Results in incidence screening round (MRI in last 18 months) and nonincidence round were compared. Results Breast MRI achieved CDR 20/1000, sensitivity 93.3% (28/30), and specificity 83.4% (1 147/1375). Twenty-eight (28/1 405, CDR 20/1000) screen-detected cancers were identified: 18 (64.3%, 18/28) invasive and 10 (35.7%, 10/28) ductal carcinoma in situ. Overall, 92.9% (26/28) of all cancers were stage 0 or 1 and 89.3% (25/28) were node negative. All 14 incidence screening round malignancies were stage 0 or 1 with N0 disease. Median size for invasive carcinoma was 8.0 mm and for ductal carcinoma in situ was 9.0 mm. There were two false-negative exams for an FNR 0.1% (2/1 405). Conclusion High-risk screening breast MRI was effective at detecting early breast cancer and associated with favorable outcomes. [ABSTRACT FROM AUTHOR]

Background: Economic evaluations have become an accepted methodology for decision makers to allocate resources in healthcare systems. Particularly in screening, where short-term costs are associated with long-term benefits, and adverse effects of screening intermingle, cost-effectiveness analyses provide a means to estimate the economic value of screening. Purpose: To introduce the methodology of economic evaluations and to review the existing evidence on cost-effectiveness of MR-based breast cancer screening. Materials and methods: The various concepts and techniques of economic evaluations critical to the interpretation of cost-effectiveness analyses are briefly introduced. In a systematic review of the literature, economic evaluations from the years 2000-2022 are reviewed. Results: Despite a considerable heterogeneity in the reported input variables, outcome categories and methodological approaches, cost-effectiveness analyses report favorably on the economic value of breast MRI screening for different risk groups, including both short- and long-term costs and outcomes. Conclusion: Economic evaluations indicate a strongly favorable economic value of breast MRI screening for women at high risk and for women with dense breast tissue. [ABSTRACT FROM AUTHOR]

Introduction: Breast magnetic resonance imaging (MRI) is increasingly being used for staging of patients with breast cancer due to its high sensitivity in detecting additional cancers (ACs). However, the clinical impact of diagnosing and treating these cancers remains unclear. Methods: A retrospective study was undertaken of patients with newly diagnosed breast cancer who underwent staging MRI at The American University of Beirut Medical Centre (AUBMC) between 2012 and 2020. Pathology reports and breast MRI examinations were reviewed. Eighteen breast cancer patients with 19 pathology‐proven index cancers (ICs) and 19 pathology‐proven MRI‐detected ACs were included. Chi‐square and Fisher's exact tests for categorical variables and Wilcoxon signed rank test for numerical variables were used to compare ICs to ACs. Results: The ICs consisted of four ductal carcinoma in situ (DCIS), 13 invasive ductal carcinomas (IDC), of which five with associated DCIS, and two invasive lobular carcinomas, (ILC) of which one with associated DCIS. ACs comprised 12 DCIS, five IDC, two with associated DCIS and two ILC, one with associated DCIS. Interval cancers were more frequently invasive whereas ACs were more frequently in situ (P = 0.021). ACs were more frequently nuclear grade 2 (P = 0.009). There was no statistically significant difference between ICs and ACs in lesion type (P = 0.062), shape (P = 0.073), initial enhancement (P = 1), delayed enhancement (P = 0.732), hormonal receptor profile (P = 0.68) and Ki67 (P = 0.388). Among ACs, ten (53%) were larger than 10 mm of which five (26%) were invasive cancers, and five (26%) were larger than the ICs. Conclusions: ACs detected by breast MRI were more likely to be in situ and to show a nuclear grade 2. Although not reaching statistical significance, some ACs tend to be clinically significant by their type, size or nuclear grade. The impact on clinical management remains to be determined. [ABSTRACT FROM AUTHOR]

Background: While several methods have been proposed for automated assessment of breast‐cancer response to neoadjuvant chemotherapy on breast MRI, limited information is available about their performance across multiple institutions. Purpose: To assess the value and robustness of deep learning‐derived volumes of locally advanced breast cancer (LABC) on MRI to infer the presence of residual disease after neoadjuvant chemotherapy. Study Type: Retrospective. Subjects: Training cohort: 102 consecutive female patients with LABC scheduled for neoadjuvant chemotherapy (NAC) from a single institution (age: 25–73 years). Independent testing cohort: 55 consecutive female patients with LABC from four institutions (age: 25–72 years). Field Strength/Sequence: Training cohort: single vendor 1.5 T or 3.0 T. Testing cohort: multivendor 3.0 T. Gradient echo dynamic contrast‐enhanced sequences. Assessment: A convolutional neural network (nnU‐Net) was trained to segment LABC. Based on resulting tumor volumes, an extremely randomized tree model was trained to assess residual cancer burden (RCB)‐0/I vs. RCB‐II/III. An independent model was developed using functional tumor volume (FTV). Models were tested on an independent testing cohort and response assessment performance and robustness across multiple institutions were assessed. Statistical Tests: The receiver operating characteristic (ROC) was used to calculate the area under the ROC curve (AUC). DeLong's method was used to compare AUCs. Correlations were calculated using Pearson's method. P values <0.05 were considered significant. Results: Automated segmentation resulted in a median (interquartile range [IQR]) Dice score of 0.87 (0.62–0.93), with similar volumetric measurements (R = 0.95, P < 0.05). Automated volumetric measurements were significantly correlated with FTV (R = 0.80). Tumor volume‐derived from deep learning of DCE‐MRI was associated with RCB, yielding an AUC of 0.76 to discriminate between RCB‐0/I and RCB‐II/III, performing similar to the FTV‐based model (AUC = 0.77, P = 0.66). Performance was comparable across institutions (IQR AUC: 0.71–0.84). Data Conclusion: Deep learning‐based segmentation estimates changes in tumor load on DCE‐MRI that are associated with RCB after NAC and is robust against variations between institutions. Evidence Level: 2. Technical Efficacy: Stage 4. [ABSTRACT FROM AUTHOR]

Purpose of Review: Surveillance imaging improves survival in women with a personal history of breast cancer (PHBC); however, imaging strategies vary in this population. This review evaluated current literature and guidelines regarding imaging modalities and approaches in women with PHBC. Recent Findings: The consensus among major breast societies is that annual surveillance mammography is recommended in all women with PHBC. Noting limitations of mammography, supplemental imaging modalities have emerged and are increasingly utilized by practices, providing the benefit of incremental cancer detection at the expense of potential additional false-positive workups. Summary: Annual surveillance mammography is associated with improved outcomes among women with PHBC. Accurate risk assessment and careful patient selection are needed when considering supplemental imaging, in order to maximize benefits, limit false-positive workups, and appropriately utilize limited resources. [ABSTRACT FROM AUTHOR]