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Histopathological investigation of metastasis in core needle axillary lymph node (ALN) biopsies is crucial for the prognosis and treatment planning of breast cancer patients. Biopsies are typically sliced and evaluated as two-dimensional (2D) images. Biopsy sampling errors and the limited view provided by 2D histology are leading factors contributing to false-negative results in the preoperative detection of metastatic lymph nodes and underestimation of metastatic foci. In this proof-of-concept study, we aim to explore the technical feasibility and the potential capacities of tridimensional (3D) X-ray micro-computed tomography imaging to expedite error detection, enhancement of histopathological accuracy, and precise measurement of metastatic lesion on ALN core needle biopsies of two breast cancer patients. Our self-developed micro-CT protocol uses eosin for the first time, a common histological dye, to enhance 3D architecture of ALNs. Performed analysis on the images of the ALN biopsies involves cancer tissue segmentation, swift biopsy evaluation, and measurement of the metastatic longest diameter and deposit volume. The eosin micro-CT protocol shows potential for an improved tumor deposit estimates, offering additional clinical value compared to standard 2D histology, however, further studies for validating this method are needed. [ABSTRACT FROM AUTHOR]

Background: To investigate the artifact sizes of four common breast clip-markers on a standard breast magnetic resonance imaging (MRI) protocol in an in vitro phantom model. Methods: Using 1.5-T and 3-T whole-body scanners with an 18-channel breast coil, artifact dimensions of four breast biopsy markers in an agarose-gel phantom were measured by two readers on images obtained with the following sequences: T2-weighted fast spin-echo short inversion time fat-suppressed inversion-recovery with magnitude reconstruction (T2-TIRM); T1-weighted spoiled gradient-echo with fat suppression (T1_FL3D), routinely used for dynamic contrast-enhanced imaging; diffusion-weighted imaging (DWI), including a readout segmented echo-planar imaging (RESOLVE-DWI) and echo-planar imaging sequence (EPI-DWI). After outlining the artifacts by freehand regions of interest, sagittal and lateral diameters in axial images were measured. Results: Interreader agreement for artifact size quantification was high, depending on the sequence (80.4–94.8%). Overall, the size, shape, and appearance of artifacts depended on clip type and MRI sequence. The artifact size ranged from 5.7 × 8.5 mm2 to 13.4 × 17.7 mm2 at 1.5 T and from 6.6 × 8.2 mm2 to 17.7 × 20.7 mm2 at 3 T. Clip artifacts were largest on EPI-DWI and RESOLVE-DWI (p ≤ 0.016). In three out of four clips, T2-TIRM showed the smallest artifact (p ≤ 0.002), while in one clip the artifact was smallest on T1_FL3D (p = 0.026). With the exception of one clip in the RESOLVE sequence, all clips showed a decrease in the artifact area from DWI to ADC images (p ≤ 0.037). Conclusion: Breast clip-marker MRI artifact appearances depend on clip type, field strength, and sequence and may reach a significant size, potentially obscuring smaller lesions and hindering accurate assessment of breast tumors. Relevance statement: Considerable variations in artifact size and characteristics across different breast clips, MRI sequences, and field strengths exist. Awareness of these artifacts and their characteristics is essential to ensure accurate interpretation of scans and appropriate treatment planning. Key Points: Awareness of breast clip artifacts is essential for accurate interpretation of MRI. The appearance of artifacts depends on breast clip type, field strength, and sequence. Clip-related artifacts might hinder the visibility of small lesions. [ABSTRACT FROM AUTHOR]

Background: This study aims to explore the accuracy of Convolutional Neural Network (CNN) models in predicting malignancy in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging (DCE-BMRI). Methods: A total of 273 benign lesions (benign group) and 274 malignant lesions (malignant group) were collected and randomly divided into a training set (246 benign and 245 malignant lesions) and a testing set (28 benign and 28 malignant lesions) in a 9:1 ratio. An additional 53 lesions from 53 patients were designated as the validation set. Five models—VGG16, VGG19, DenseNet201, ResNet50, and MobileNetV2—were evaluated. Model performance was assessed using accuracy (Ac) in the training and testing sets, and precision (Pr), recall (Rc), F1 score (F1), and area under the receiver operating characteristic curve (AUC) in the validation set. Results: The accuracy of VGG19 on the test set (0.96) is higher than that of VGG16 (0.91), DenseNet201 (0.91), ResNet50 (0.67), and MobileNetV2 (0.88). For the validation set, VGG19 achieved higher performance metrics (Pr 0.75, Rc 0.76, F1 0.73, AUC 0.76) compared to the other models, specifically VGG16 (Pr 0.73, Rc 0.75, F1 0.70, AUC 0.73), DenseNet201 (Pr 0.71, Rc 0.74, F1 0.69, AUC 0.71), ResNet50 (Pr 0.65, Rc 0.68, F1 0.60, AUC 0.65), and MobileNetV2 (Pr 0.73, Rc 0.75, F1 0.71, AUC 0.73). S4 model achieved higher performance metrics (Pr 0.89, Rc 0.88, F1 0.87, AUC 0.89) compared to the other four fine-tuned models, specifically S1 (Pr 0.75, Rc 0.76, F1 0.74, AUC 0.75), S2 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77), S3 (Pr 0.76, Rc 0.76, F1 0.73, AUC 0.75), and S5 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77). Additionally, S4 model showed the lowest loss value in the testing set. Notably, the AUC of S4 for BI-RADS 3 was 0.90 and for BI-RADS 4 was 0.86, both significantly higher than the 0.65 AUC for BI-RADS 5. Conclusions: The S4 model we propose has demonstrated superior performance in predicting the likelihood of malignancy in DCE-BMRI, making it a promising candidate for clinical application in patients with breast diseases. However, further validation is essential, highlighting the need for additional data to confirm its efficacy. [ABSTRACT FROM AUTHOR]

Background: Over the past decade, the adoption of screening programs, digital mammography, and magnetic resonance imaging (MRI) has increased early-stage breast cancer diagnosis rates. Mortality rates have decreased due to early detection and improved treatments, including personalized therapies. Accelerated partial-breast irradiation (APBI) is emerging as a convenient and effective treatment for some patients, with studies exploring its preoperative use. Preoperative APBI, especially with MRI guidance, offers improved tumor targeting and potentially reduced side effects. Magnetic Resonance Imaging-Guided Single-Fraction Pre-Operative Radiotherapy for Early-Stage Breast Cancer (RICE trial) aims to assess the feasibility and efficacy of MRI-guided single-dose radiotherapy (RT) for early-stage breast cancer. Methods: The RICE study is a prospective, single-arm study evaluating single-fraction preoperative, APBI treatment for patients with early-stage breast cancer using a magnetic resonance imaging linear accelerator (MRI linac). Eligible patients enrolled in this study will have a core biopsy to confirm estrogen receptor-positive and HER2-negative sub-type. RT planning will use a planning computed tomography (CT) co-registered with a MRI with the patient in either the supine or prone position. For the diagnostic workup, [18F] fluorodeoxyglucose positron emission tomography/CT ([18F] FDG PET/CT) and [18F] fluoroestradiol positron emission tomography/CT ([18F] FES PET/CT) will be performed prior to treatment. Thirty patients will receive a single ablative RT dose of 21 Gray to the tumor. Pre-treatment and post-treatment MRI scans will be acquired at baseline and 5 weeks post-RT respectively. Breast-conserving surgery will be scheduled for 6 weeks after APBI treatment using the MRI linac. The primary study endpoint is the successful administration of a single fraction of preoperative breast RT under the guidance of an MRI linac. Secondary endpoints include evaluating the utility of MRI, [18F] FDG PET/CT, and [18F] FES PET/CT as a non-invasive method for assessing treatment response in patients undergoing single-fraction preoperative APBI. Conclusion: The RICE trial represents a significant step in breast cancer treatment, offering insights that could lead to treatment protocols with minimized RT appointments and enhanced patient outcomes. Trial registration: This trial is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR). Registered 31st of May 2021. Registration number: ACTRN12621000659808. [ABSTRACT FROM AUTHOR]

PURPOSE: Contrast Enhancement Magnetic Resonance (CEMR) and Contrast-Enhanced Mammography (CEM) are important diagnostic tools to evaluate breast cancer patients, and both are objects of interest in the literature. The purpose of this systematic review was to select publications from the last ten years in order to evaluate the literature contributions related to the frequency of contrast agents used, administration techniques and the presence of adverse reactions. METHODS: We have selected, according to the PRISMA statement, publications reviewed on Pub Med in the period from 1 January 2012 to 31 December 2022. The search engine was activated using the following keywords: "CESM", "CEM", "CEDM", "Contrast mammography" for CEM, "DCE-MRI", "Contrast Enhancement MRI" for CEMR, excluding reviews, book chapters and meta-analyses. From the total number of publications, we made a preliminary selection based on titles and abstracts and excluded all articles published in languages other than English and all experimental studies performed on surgical specimen or animal population, as well as all articles for which the extended version was not available. Two readers evaluated all the articles and compiled a pre-compiled form accordingly. RESULTS: After a preliminary collection of 571 CEM publications, 118 articles were selected, relating to an overall population of 21,178 patients. From a total of 3063 CEMR publications, 356 articles relating to an overall population of 45,649 patients were selected. The most used contrast agents are Iohexol for CEM (39.83%) and Gadopentetic acid (Gd-DTPA) for CEMR (32.5%). Regarding the CEM contrast administration protocol, in 84.7% of cases a dose of 1.5 mL/kg was used with an infusion rate of 2–3 mL/s. Regarding the CEMR infusion protocol, in 71% of cases a dose of 1 mmol/kg was used at an infusion rate of 2–4 mL/s. Twelve out of 118 CEM articles reported allergic reactions, involving 29 patients (0.13%). In DCE-MRI, only one out of 356 articles reported allergic reactions, involving two patients (0.004%). No severe reactions were observed in either cohort of exams. CONCLUSIONS: CEM and CEMR are essential contrast methods to evaluate breast diseases. However, from the literature analysis, although there are preferences on the uses of the contrast agent (Iohexol for CESM, G-DTPA for CEMR), a wide range of molecules are still used in contrast methods, with different administration protocols. Based on the collected data, it is possible to state that both methods are safe, and no severe reactions were observed in our evaluation. [ABSTRACT FROM AUTHOR]

Objectives: To identify preoperative breast MR imaging and clinicopathological variables related to recurrence and develop a risk prediction model for recurrence in young women with breast cancer treated with upfront surgery. Methods: This retrospective study analyzed 438 consecutive women with breast cancer aged 35 years or younger between January 2007 and December 2016. Breast MR images before surgery were independently reviewed by breast radiologists blinded to patient outcomes. The clinicopathological data including patient demographics, clinical features, and tumor characteristics were reviewed. Univariate and multivariate logistic regression analyses were used to identify the independent factors associated with recurrence. The risk prediction model for recurrence was developed, and the discrimination and calibration abilities were assessed. Results: Of 438 patients, 95 (21.7%) developed recurrence after a median follow-up of 65 months. Tumor size at MR imaging (HR = 1.158, p = 0.006), multifocal or multicentric disease (HR = 1.676, p = 0.017), and peritumoral edema on T2WI (HR = 2.166, p = 0.001) were identified as independent predictors of recurrence, while adjuvant endocrine therapy (HR = 0.624, p = 0.035) was inversely associated with recurrence. The prediction model showed good discrimination ability in predicting 5-year recurrence (C index, 0.707 in the development cohort; 0.686 in the validation cohort) and overall recurrence (C index, 0.699 in the development cohort; 0.678 in the validation cohort). The calibration plot demonstrated an excellent correlation (concordance correlation coefficient, 0.903). Conclusion: A prediction model based on breast MR imaging and clinicopathological features showed good discrimination to predict recurrence in young women with breast cancer treated with upfront surgery, which could contribute to individualized risk stratification. Clinical relevance statement: Our prediction model, incorporating preoperative breast MR imaging and clinicopathological features, predicts recurrence in young women with breast cancer undergoing upfront surgery, facilitating personalized risk stratification and informing tailored management strategies. Key Points: Younger women with breast cancer have worse outcomes than those diagnosed at more typical ages. The described prediction model showed good discrimination performance in predicting 5-year and overall recurrence. Incorporating better risk stratification tools in this population may help improve outcomes. [ABSTRACT FROM AUTHOR]

Summary Imaging research pathways focus increasingly on the development of individualised approaches to breast cancer detection, diagnosis and management. Detection of breast cancer with X‐ray mammography may fail in some cancer subtypes with limited changes in morphology/tissue density and in women with dense breasts. International organisations offer recommendations for contrast‐enhanced breast imaging, as it provides superior sensitivity for screening, local staging and assessment of neoadjuvant treatment response, when compared with standard X‐ray mammography (including tomosynthesis) and breast ultrasound. Arguably, the evidence base is stronger for contrast‐enhanced MRI (CE‐MRI). Unfortunately, patient access to breast MRI in rural and remote areas is limited by practical limitations and equipment licensing restrictions. Moreover, breast MRI is an expensive test, likely to be out of reach for many women. Contrast‐enhanced mammography (CEM) offers an attractive alternative to improve patient access to functional breast imaging. It is a new type of digital, dual energy X‐ray mammography that can be performed on most modern units, following a relatively inexpensive hard‐ and software upgrade. In this paper, we review the rapidly accumulating evidence that CEM can provide similar diagnostic accuracy to CE‐MRI, though at a significantly lower cost and offering greater comfort to the patient. The adoption of CEM can help meet the anticipated increased demand for CE‐MRI. [ABSTRACT FROM AUTHOR]

Background: To assess the reliability and comprehensibility of breast radiology reports simplified by artificial intelligence using the large language model (LLM) ChatGPT-4o. Methods: A radiologist with 20 years' experience selected 21 anonymized breast radiology reports, 7 mammography, 7 breast ultrasound, and 7 breast magnetic resonance imaging (MRI), categorized according to breast imaging reporting and data system (BI-RADS). These reports underwent simplification by prompting ChatGPT-4o with "Explain this medical report to a patient using simple language". Five breast radiologists assessed the quality of these simplified reports for factual accuracy, completeness, and potential harm with a 5-point Likert scale from 1 (strongly agree) to 5 (strongly disagree). Another breast radiologist evaluated the text comprehension of five non-healthcare personnel readers using a 5-point Likert scale from 1 (excellent) to 5 (poor). Descriptive statistics, Cronbach's α, and the Kruskal–Wallis test were used. Results: Mammography, ultrasound, and MRI showed high factual accuracy (median 2) and completeness (median 2) across radiologists, with low potential harm scores (median 5); no significant group differences (p ≥ 0.780), and high internal consistency (α > 0.80) were observed. Non-healthcare readers showed high comprehension (median 2 for mammography and MRI and 1 for ultrasound); no significant group differences across modalities (p = 0.368), and high internal consistency (α > 0.85) were observed. BI-RADS 0, 1, and 2 reports were accurately explained, while BI-RADS 3–6 reports were challenging. Conclusion: The model demonstrated reliability and clarity, offering promise for patients with diverse backgrounds. LLMs like ChatGPT-4o could simplify breast radiology reports, aid in communication, and enhance patient care. Relevance statement: Simplified breast radiology reports generated by ChatGPT-4o show potential in enhancing communication with patients, improving comprehension across varying educational backgrounds, and contributing to patient-centered care in radiology practice. Key Points: AI simplifies complex breast imaging reports, enhancing patient understanding. Simplified reports from AI maintain accuracy, improving patient comprehension significantly. Implementing AI reports enhances patient engagement and communication in breast imaging. [ABSTRACT FROM AUTHOR]

Background: Annual breast magnetic resonance imaging (MRI) is highly recommended to assist mammography for women at high risk of breast cancer (BC). This study explored the knowledge, attitude, and willingness toward breast MRI screening among women at high risk of BC. Methods: This cross-sectional study enrolled women at high risk of BC between August 2022 and January 2023 in Beijing, China. A structural equation model (SEM) was used to analyze the relationships among knowledge, attitude, and willingness. Results: A total of 596 questionnaires were collected, and 412 questionnaires (69.13%) were valid. The women's knowledge and attitude scores were 7.75 ± 2.79 (possible range: 0–12) and 48.53 ± 6.31 (possible range: 13–65). Among the women, 297 (72.09%) were willing to undergo regular breast MRI screening. The SEM showed that knowledge had direct effect on attitude [β = 0.77, 95% CI: (0.57, 0.98), P < 0.001], the attitude had direct effect on willingness [β = 0.02, 95% CI: (0.01, 0.02), P < 0.001], knowledge had an indirect effect on willingness through attitude [β = 0.01, 95% CI: (0.01,0.02), P < 0.001], and the direct effect of knowledge on practice was not significant. Conclusions: The women at high risk of BC had insufficient knowledge and a relatively positive attitude toward breast MRI screening. Most of them were willing to undergo regular breast MRI screening. Advertising and public health education programs should be designed to improve their knowledge and attitude, therefore improving their willingness and practice. [ABSTRACT FROM AUTHOR]

Background: This pilot prospective study aimed to evaluate ultrasound-guided cryoablation of breast cancer (BC) by assessing: (i) technical efficacy as the presence of necrosis in surgical specimens and rate of complete tumor ablation; (ii) safety as incidence and severity of complications; and (iii) patients' satisfaction using a dedicated questionnaire. In addition, (iv) we tested the capability of magnetic resonance imaging (MRI) or contrast-enhanced mammography (CEM) to predict cryoablation efficacy. Methods: From 07/2022 to 01/2023, we enrolled 20 patients with early-stage BC scheduled for breast surgery. Ten of them, with a cryo-feasible cancer location, were sent to cryoablation (cryo-group) and ten to routine surgical practice (control group). Both groups underwent surgery and were asked to answer a satisfaction questionnaire. Results: Of eleven patients screened for cryoablation, only one refused to be treated at another hospital (acceptance rate 10/11, 91%). Surgery was quadrantectomy in 19 cases and mastectomy in 1. In the cryo-group, the procedure was completed and steatonecrosis was observed in 10/10 cases, with complete tumor ablation in nine of them. The post-procedural status was evaluated with MRI in five patients, with CEM in four patients, and with ultrasound in one patient who refused MRI and CEM. MRI or CEM correctly predicted complete cryoablation in eight patients and incomplete cryoablation in one patient. Patients in both groups did not have serious complications and responded positively to satisfaction questionnaires. Conclusion: Ultrasound-guided cryoablation of early-stage BC is well accepted by patients, effective, and safe. MRI and CEM were able to predict the procedure's technical efficacy. Trial registration: https://clinicaltrials.gov/study/NCT05727813 updated February 14, 2023. Relevance statement: Our pilot study showed that ultrasound-guided cryoablation is a promising nonsurgical alternative for treating early-stage BC. Key Points: Ultrasound-guided cryoablation was effective and safe in early BC patients. The procedure was well-tolerated, with low morbidity and high patient satisfaction. MRI and CEM predicted cryoablation efficacy, in accordance with histopathologic findings. Cryoablation can be considered a potential alternative to surgery in selected patients. [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 nipplesparing mastectomy and left-sided lumpectomy, with bilateral reconstruction. [ABSTRACT FROM AUTHOR]

The document discusses the challenges in treating breast cancer, particularly triple negative breast cancer (TNBC), due to limited identified targets and resistance to therapy. Various molecular targets, such as survivin and trace amine-associated receptors (TAARs), are explored for potential treatment options. The use of metal-organic frameworks (MOFs) for drug delivery and the importance of combinatorial therapies in targeting multiple molecular drivers are also highlighted. The document emphasizes the need for new directions in molecular targeting to improve breast cancer treatment outcomes. [Extracted from the article]

Objective: The aim of this retrospective study was to analyze the predictive capabilities of preoperative mammography, dynamic contrast-enhancedmagnetic resonance imaging (DCE-MRI), and diffusion-weighted imaging (DWI) in determining hormone receptor (HRc) status for pure ductal carcinoma in situ (DCIS) lesions. Materials and Methods: The study included a total of 79 patients who underwent preoperative mammography (MG) and MRI between December 2018 and December 2023 and were subsequently diagnosed with pure DCIS after surgery. The correlation between MG, DCE-MRI, and DWI features and estrogen receptor (ER) and progesterone receptor (PR) status was examined. Results: Among the lesions, 44 were double HRc-positive (ER and PR-positive), 13 were single HRc-positive (ER-positive and PR-negative or ERnegative and PR-positive) and 22 were double HRc-negative (ER and PR-negative). The presence of symptom (p = 0.029), the presence of comedo necrosis (p = 0.005) and high histological grade (p<0.001) were found to be associated with ER and PR negativity. Amorphous microcalcifications were more commonly observed in the double HRc-negative group, while linear calcifications were more prevalent in both double and single HRc-positive groups (p = 0.020). Non-mass enhancement (NME) with a linear distribution was significantly more common in double HRc-negative lesions (38%), and NME with a segmental distribution in both double (43%) and single (50%) receptor-positive lesions (p = 0.042). Evaluation of DWI findings revealed that a higher lesion-to-normal breast parenchyma apparent diffusion coefficient (ADC) ratio statistically increased the probability of HRc positivity (p = 0.033). Conclusion: Certain clinicopathological, mammography, and MRI features, along with the lesion-to-normal breast parenchyma ADC ratio, can serve as predictors for HRc status in DCIS lesions. [ABSTRACT FROM AUTHOR]

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

Objectives: To evaluate the use of a commercial artificial intelligence (AI)–based mammography analysis software for improving the interpretations of breast ultrasound (US)-detected lesions. Methods: A retrospective analysis was performed on 1109 breasts that underwent both mammography and US-guided breast biopsy. The AI software processed mammograms and provided an AI score ranging from 0 to 100 for each breast, indicating the likelihood of malignancy. The performance of the AI score in differentiating mammograms with benign outcomes from those revealing cancers following US-guided breast biopsy was evaluated. In addition, prediction models for benign outcomes were constructed based on clinical and imaging characteristics with and without AI scores, using logistic regression analysis. Results: The AI software had an area under the receiver operating characteristics curve (AUROC) of 0.79 (95% CI, 0.79–0.82) in differentiating between benign and cancer cases. The prediction models that did not include AI scores (non-AI model), only used AI scores (AI-only model), and included AI scores (integrated model) had AUROCs of 0.79 (95% CI, 0.75–0.83), 0.78 (95% CI, 0.74–0.82), and 0.85 (95% CI, 0.81–0.88) in the development cohort, and 0.75 (95% CI, 0.68–0.81), 0.82 (95% CI, 0.76–0.88), and 0.84 (95% CI, 0.79–0.90) in the validation cohort, respectively. The integrated model outperformed the non-AI model in the development and validation cohorts (p < 0.001 for both). Conclusion: The commercial AI-based mammography analysis software could be a valuable adjunct to clinical decision-making for managing US-detected breast lesions. Clinical relevance statement: The commercial AI-based mammography analysis software could potentially reduce unnecessary biopsies and improve patient outcomes. Key Points: • Breast US has high rates of false-positive interpretations. • A commercial AI-based mammography analysis software could distinguish mammograms having benign outcomes from those revealing cancers after US-guided breast biopsy. • A commercial AI-based mammography analysis software may improve interpretations for breast US-detected lesions. [ABSTRACT FROM AUTHOR]

Objective: This study aims to determine whether persistent T1-weighted lesions signify a complete pathological response (pCR) in breast cancer patients treated with neoadjuvant chemotherapy and surgery, and to evaluate their correlation with imaging responses on MRI. Materials and methods: A retrospective review was conducted on data from breast cancer patients treated between January 2011 and December 2018. Patients who underwent breast MRI and pre- and post-neoadjuvant chemotherapy followed by surgery were included. Those with distant metastasis, no planned surgery, pre-surgery radiation, ineligibility for neoadjuvant chemotherapy, or unavailable surgical pathology were excluded. Groups with and without persistent T1-weighted lesions were compared using the chi-square test for categorical variables and the Student t test or Wilcox rank sum test for continuous variables. Univariate logistic regression was used to evaluate the association of the final pathological response with the presence of T1-persistent lesion and other characteristics. Results: Out of 319 patients, 294 met the inclusion criteria (breast cancer patients treated with neoadjuvant chemotherapy and subsequent surgery); 157 had persistent T1 lesions on post-chemotherapy MRI and 137 did not. A persistent T1 lesion indicated reduced likelihood of complete pathological response (14% vs. 39%, p < 0.001) and imaging response (69% vs. 93%, p < 0.001). Multivariable analysis confirmed these findings: OR 0.37 (95% CI 0.18–0.76), p = 0.007. No other characteristics correlated with T1 residual lesions. Conclusion: Persistent T1-weighted lesions without associated abnormal enhancement on post-treatment breast MRI correlate with lower complete pathological and imaging response rates. Clinical relevance statement: The study underscores the importance of persistent T1-weighted lesions on breast MRI as vital clinical markers, being inversely related to a complete pathological response following neoadjuvant chemotherapy; they should be a key factor in guiding post-neoadjuvant chemotherapy treatment decisions. Key Points: • Persistent T1 lesions on post-chemotherapy breast MRI indicate a reduced likelihood of achieving a complete pathological response (14% vs. 39%, p < 0.001) and imaging response (69% vs. 93%, p < 0.001). • Through multivariable analysis, it was confirmed that the presence of a persistent T1 lesion on breast MRI post-chemotherapy is linked to a decreased likelihood of complete pathological response, with an odds ratio (OR) of 0.37 (95% CI 0.18–0.76; p = 0.007). • In addition to the convention of equating the absence of residual enhancement to complete imaging response, our results suggest that the presence or absence of residual T1 lesions should also be considered. [ABSTRACT FROM AUTHOR]

Background: Siamese network (SN) using longitudinal DCE‐MRI for pathologic complete response (pCR) identification lack a unified approach to phases selection. Purpose: To identify pCR in early‐stage NAC, using SN with longitudinal DCE‐MRI and introducing IPS for phases selection. Study Type: Multicenter, longitudinal. Population: Center A: 162 female patients (50.63 ± 8.41 years) divided 7:3 into training and internal validation cohorts. Center B: 61 female patients (50.08 ± 7.82 years) were used as an external validation cohort. Field Strength/Sequence: Center A: single vendor 3.0 T with a compressed‐sensing volume interpolated breath‐hold examination sequence. Center B: single vendor 1.5 T with volume interpolated breath‐hold examination sequence. Assessment: Patients underwent DCE‐MRI before and after two NAC cycles, with tumor regions of interest (ROI) manually delineated. Histopathology was the reference for pCR identification. Models developed included a clinical one, four SN models based on IPS‐selected phases, and integrated models combining clinical and SN features. Statistical Tests: Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The DeLong test was used to compare AUCs. Net reclassification improvement and integrated discrimination improvement (IDI) tests were employed for performance comparison. P < 0.05 was considered significant. Results: In internal and external validation cohorts, the clinical model showed AUCs of 0.760 and 0.718. SN and integrated models, with increasing phases via IPS, achieved AUCs ranging from 0.813 to 0.951 and 0.818 to 0.922. Notably, SN‐3 and integrated‐3 and integrated‐4 outperformed the clinical model. However, input phases beyond 20% did not significantly enhance performance (IDI test: SN‐4 vs. SN‐3, P = 0.314 and 0.630; integrated‐4 vs. integrated‐3, P = 0.785 and 0.709). Data Conclusion: The longitudinal multiphase DCE‐MRI based on the SN demonstrates promise for identifying pCR in breast cancer. Evidence Level: 1 Technical Efficacy: Stage 4 [ABSTRACT FROM AUTHOR]

Treatment of highly aggressive triple‐negative breast cancer (TNBC) in the clinic is challenging. Here, a liposome nanodrug (LP@PFH@HMME) integrating imaging agents and therapeutic agents for bimodal imaging‐guided sonodynamic therapy (SDT) is developed, which boosted immunogenicity to enable potent immunotherapy via immune checkpoint blockade (ICB) in TNBC. In the acidic tumor microenvironment (TME), LP@PFH@HMME undergoes "nano‐to‐micro" transformation due to a pH‐responsive lipid fusion, which makes droplets much more sensitive to ultrasound (US) in contrast‐enhanced ultrasound (CEUS) and SDT studies. The nanodrug demonstrates robust bimodal imaging ability through fluorine‐19 magnetic resonance imaging (19F MRI) and CEUS bimodal imaging, and it exhibits excellent solubility in aqueous solution with relatively high 19F content and desirable long transverse relaxation time (T2 = 1.072 s), making it suitable for high‐performance 19F MRI, in addition to effective accumulation of nanodrugs after tail vein injection. Thus, 19F MRI/CEUS dual imaging is achievable to show adequate time points for US irradiation of tumor sites to induce highly effective SDT, which produces abundant reactive oxygen species (ROS) triggering immunogenic cell death (ICD) to assist ICB‐based immunotherapy. The combination treatment design of sonodynamic therapy with immunotherapy effectively inhibited TNBC growth and recurrence, highlighting the promise of multifunctional nanodrugs in treating TNBC. [ABSTRACT FROM AUTHOR]

Introduction: The purpose of the study is to assess the role of preoperative magnetic resonance (MR) imaging on the surgical management of invasive lobular carcinoma (ILC) and to evaluate whether breast density and background parenchymal enhancement (BPE) influence surgical treatment. Methods: This retrospective study was conducted on 56 patients who were diagnosed with ILC between 2014 and 2020. All patients had mammogram and ultrasound. Preoperative MRI was available in 34 patients. Age, menopausal status, breast density, BPE, multifocality/multicentricity and surgical treatment were collected. Results: Mean pathological tumour size was 36.4 mm (range 5–140 mm). Dense breasts had larger tumours compared to non‐dense breasts (P = 0.072). Of the 34 patients with MRI, 6 opted for mastectomy. Of the remaining 28 cases, MRI findings upgraded surgery to mastectomy in 54% (15/28) because mammogram/ultrasound underestimated tumour extent in 25% (7/28), or multifocal/multicentric disease was identified in 29% (8/28). Tumour size was underestimated by MRI in 7% (2/28). In the non‐MRI subgroup, 64% (14/22) of patients underwent breast‐conserving surgery, but 29% of them (4/14) required a second‐stage mastectomy due to extensive margin involvement. There was no difference in mastectomy rate between patients with MRI (62%) and without MRI (55%) (P = 0.061). Tumour size correlation between MRI and histopathology demonstrated an excellent intraclass correlation coefficient (P < 0.001). Surgical treatment recommendation was not significantly impacted by breast density or BPE. Conclusion: Breast MRI improves surgical management of patients with ILC in providing additional diagnostic information often missed with standard imaging modalities, and without increasing mastectomy rate. Surgical treatment is not impacted by breast density or BPE. [ABSTRACT FROM AUTHOR]

Breast cancer is a common cancer in Australia, affecting one in seven women. Identifying women at moderate or high risk is crucial for personalized surveillance and risk-reductive interventions. Risk factors include genetic mutations, family history, and personal history. Tools like iPrevent and CanRisk can help determine individual risk. Risk-reductive strategies include lifestyle changes, medication, and surgery. Ashkenazi Jewish women have a higher risk of carrying BRCA1 or BRCA2 gene mutations. The article explores breast cancer reconstruction surgery options and risk-reductive interventions. Patients should understand that these procedures do not eliminate future breast cancer risk. Medications like selective estrogen receptor modulators and aromatase inhibitors can also be used. Tailored surveillance and risk-reductive strategies are important for patients at moderate or high risk. [Extracted from the article]

Background: Neoadjuvant chemotherapy (NAC) is currently used for treating breast cancer in selected cases. Our study aims to evaluate the role of automated breast ultrasound (ABUS) in the assessment of response to NAC and compare the ABUS results with MRI. Methods: A total of 52 consecutive patients were included in this study. ABUS and MRI sensitivity (SE), specificity (SP), diagnostic accuracy (DA), positive predictive value (PPV), and negative predictive value (NPV) were calculated and represented using Area Under ROC Curve (ROC) analysis, searching for any significant difference (p < 0.05). The McNemar test was used searching for any significant difference in terms of sensitivity by comparing the ABUS and MRI results. The inter-observer agreement between the readers in evaluating the response to NAC for both MRI and ABUS was calculated using Cohen's kappa k coefficient. Results: A total of 35 cases of complete response and 17 cases of persistent disease were found. MRI showed SE, SP, DA, PPV, and NPV values of 100%, 88%, 92%, 81%, and 100%, respectively, with an AUC value of 0.943 (p < 0.0001). ABUS showed SE, SP, DA, PPV, and NPV values of 88%, 94%, 92%, 89%, and 94%, respectively, with an AUC of 0.913 (p < 0.0001). The McNemar test revealed no significant difference (p = 0.1250). The inter-observer agreement between the two readers in evaluating the response to NAC for MRI and ABUS was, respectively, 0.88 and 0.89. Conclusions: Automatic breast ultrasound represents a new accurate, tri-dimensional and operator-independent tool for evaluating patients referred to NAC. [ABSTRACT FROM AUTHOR]

Background: Invasive lobular carcinoma (ILC) is challenging to stage accurately using mammography (MG) and ultrasound (US) with undiagnosed ipsilateral and contralateral cancer resulting in poor patient outcomes including return to surgery. Our institution employs routine staging breast MRI in ILC for this reason. However, increased time for further imaging/biopsies contributes to patient anxiety and potentially delays definite management. We aimed to quantify the frequency of staging MRI‐detected additional lesions requiring biopsy or follow‐up, the added cancer detection rate and MRI prompted change in surgical management. Methods: An observational study on staging breast MRI for newly diagnosed ILC at a tertiary Western Australian hospital from January 2019 to August 2022. Standardized 3T MRI protocol was performed, double read by unblinded fellowship‐trained radiologists. Histopathology from biopsy, surgery, or first annual surveillance was the reference standard for additional MRI‐detected lesions. Results: One hundred ten MRI studies demonstrated 49 (45%) patients had at least one additional clinically significant MRI‐detected lesion. Thirty‐one patients had an additional ipsilateral lesion detected, of which 18 (58%) proved malignant; 14 (45%) multifocal and 4 (13%) multicentric ILC. Additional work‐up of MRI‐detected lesions averaged a 9‐day delay to definitive surgery compared to patients with a negative or definitively benign MRI. MRI changed surgical planning in 11 of 110 cases from breast conservation surgery (BCS) to mastectomy and there were two contralateral cancers diagnosed. BCS reoperation rate was 11%. Conclusion: Staging MRI for ILC identifies clinically significant lesions in nearly half of patients, predominantly ipsilateral multifocal disease, without significant delay to definitive surgery. [ABSTRACT FROM AUTHOR]

Purpose: Breast cancer is the most common cancer in women. Developments in breast cancer treatment have extended the life expectancy of these patients, raising the issue of morbidity of breast cancer surgery, the major cause of which is axillary dissection. The aim of the present study was to establish the safety of sentinel node biopsy (SLNB) in patients with a clinically node-negative axilla after neoadjuvant chemotherapy (NACT). Methods: We recorded demographic data, as well as the findings of physical examination, imaging, and pathology before and after NACT. SLNB with indocyanine green + isosulfan blue and axillary dissection were performed and the surgical and pathology findings were recorded. Results: A sentinel lymph node was detected in 80 of 90 patients who underwent surgery. When ≥ three sentinel lymph nodes were removed as negative in the patient group with cN0 after treatment, we evaluated the axilla as being negative with an accuracy of 100%. Conclusions: Axillary lymph-node dissection may not be necessary for patients with cNO confirmed by physical and radiological examination using positron emission tomography (PET) computed tomography (CT), and breast magnetic resonance imaging (MRI) after NACT, if ≥ three negative SLNB are removed. Further studies are needed to confirm our findings. [ABSTRACT FROM AUTHOR]

Purpose: This study was conducted to investigate the effectiveness of strain elastography in guiding precise and sufficient tissue sampling for perihilar cholangiocarcinoma (CCA) biopsies. Methods: Our retrospective analysis included 23 liver biopsies conducted between March 2019 and July 2022 for suspected perihilar CCA. An experienced radiologist performed the biopsies via an ultrasound machine with elastography configuration. Tissue stiffness color maps were used for guiding when the biopsies were performed. Strain index value calculations were made by radiologists on recorded images. Results: Patient demographics revealed a mean age of 65.17 ± 9.25 years, with a gender distribution of six females and 17 males. Gray-scale examinations unveiled diverse echogenic characteristics in liver lesions. Elastography-guided biopsies demonstrated no need for repeats, while gray-scale biopsies necessitated re-biopsy in four patients, resulting in cholangiocarcinoma diagnosis (P = 0.037). Strain index values showcased strong inter- and intra-observer agreements (P < 0.001). Notably, no post-biopsy complications emerged in either study group. Conclusion: The diagnostic advantage of elastography, particularly in enhancing accuracy in challenging isoechoic lesions, was demonstrated, although the substantial overlap between strain index values of benign and malignant liver masses limits clinical usefulness of this technique. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications. Methods: We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images. After feature selection, three random forest models were built using the retained features, respectively. Significant clinical factors were integrated with these radiomics models to construct a multi-modality model. The multi-modality model was compared to radiologists' diagnoses on axillary ultrasound and MRI. It was also used to assist radiologists in making a secondary diagnosis on MRI. Results: The multi-modality model showed superior performance with AUCs of 0.964 in the training cohort, 0.916 in the internal validation cohort, and 0.892 in the external validation cohort. It surpassed single-modality models and radiologists' ALN diagnosis on MRI and axillary ultrasound in all validation cohorts. Additionally, the multi-modality model improved radiologists' MRI-based ALN diagnostic ability, increasing the average accuracy from 70.70 to 78.16% for radiologist A and from 75.42 to 81.38% for radiologist B. Conclusion: The multi-modality model can predict ALN metastasis of breast cancer accurately. Moreover, the artificial intelligence (AI) model also assisted the radiologists to improve their diagnostic ability on MRI. Clinical relevance statement: The multi-modality model based on both MRI and mammography images allows preoperative prediction of axillary lymph node metastasis in breast cancer patients. With the assistance of the model, the diagnostic efficacy of radiologists can be further improved. Key Points: • We developed a novel multi-modality model that combines MRI and mammography radiomics with clinical factors to accurately predict axillary lymph node (ALN) metastasis, which has not been previously reported. • Our multi-modality model outperformed both the radiologists' ALN diagnosis based on MRI and axillary ultrasound, as well as single-modality radiomics models based on MRI or mammography. • The multi-modality model can serve as a potential decision support tool to improve the radiologists' ALN diagnosis on MRI. [ABSTRACT FROM AUTHOR]

The early detection of breast cancer is essential for improving treatment outcomes, and recent advancements in artificial intelligence (AI), combined with image processing techniques, have shown great potential in enhancing diagnostic accuracy. This study explores the effects of various image processing methods and AI models on the performance of early breast cancer diagnostic systems. By focusing on techniques such as Wiener filtering and total variation filtering, we aim to improve image quality and diagnostic precision. The novelty of this study lies in the comprehensive evaluation of these techniques across multiple medical imaging datasets, including a DCE-MRI dataset for breast-tumor image segmentation and classification (BreastDM) and the Breast Ultrasound Image (BUSI), Mammographic Image Analysis Society (MIAS), Breast Cancer Histopathological Image (BreakHis), and Digital Database for Screening Mammography (DDSM) datasets. The integration of advanced AI models, such as the vision transformer (ViT) and the U-KAN model—a U-Net structure combined with Kolmogorov–Arnold Networks (KANs)—is another key aspect, offering new insights into the efficacy of these approaches in different imaging contexts. Experiments revealed that Wiener filtering significantly improved image quality, achieving a peak signal-to-noise ratio (PSNR) of 23.06 dB and a structural similarity index measure (SSIM) of 0.79 using the BreastDM dataset and a PSNR of 20.09 dB with an SSIM of 0.35 using the BUSI dataset. When combined filtering techniques were applied, the results varied, with the MIAS dataset showing a decrease in SSIM and an increase in the mean squared error (MSE), while the BUSI dataset exhibited enhanced perceptual quality and structural preservation. The vision transformer (ViT) framework excelled in processing complex image data, particularly with the BreastDM and BUSI datasets. Notably, the Wiener filter using the BreastDM dataset resulted in an accuracy of 96.9% and a recall of 96.7%, while the combined filtering approach further enhanced these metrics to 99.3% accuracy and 98.3% recall. In the BUSI dataset, the Wiener filter achieved an accuracy of 98.0% and a specificity of 98.5%. Additionally, the U-KAN model demonstrated superior performance in breast cancer lesion segmentation, outperforming traditional models like U-Net and U-Net++ across datasets, with an accuracy of 93.3% and a sensitivity of 97.4% in the BUSI dataset. These findings highlight the importance of dataset-specific preprocessing techniques and the potential of advanced AI models like ViT and U-KAN to significantly improve the accuracy of early breast cancer diagnostics. [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]

The objective of this study was to evaluate a high-resolution deep-learning (DL)-based diffusion-weighted imaging (DWI) sequence for breast magnetic resonance imaging (MRI) in comparison to a standard DWI sequence (DWIStd) at 1.5 T. It is a prospective study of 38 breast cancer patients, who were scanned with DWIStd and DWIDL. Both DWI sequences were scored for image quality, sharpness, artifacts, contrast, noise, and diagnostic confidence with a Likert-scale from 1 (non-diagnostic) to 5 (excellent). The lesion diameter was evaluated on b 800 DWI, apparent diffusion coefficient (ADC), and the second subtraction (SUB) of the contrast-enhanced T1 VIBE. SNR was also calculated. Statistics included correlation analyses and paired t-tests. High-resolution DWIDL offered significantly superior image quality, sharpness, noise, contrast, and diagnostic confidence (each p < 0.02)). Artifacts were significantly higher in DWIDL by one reader (M = 4.62 vs. 4.36 Likert scale, p < 0.01) without affecting the diagnostic confidence. SNR was higher in DWIDL for b 50 and ADC maps (each p = 0.07). Acquisition time was reduced by 22% in DWIDL. The lesion diameters in DWI b 800DL and Std and ADCDL and Std were respectively 6% lower compared to the 2nd SUB. A DL-based diffusion sequence at 1.5 T in breast MRI offers a higher resolution and a faster acquisition, including only minimally more artefacts without affecting the diagnostic confidence. [ABSTRACT FROM AUTHOR]

To explore the MRI characteristics and clinical outcome of the "very early" intrahepatic cholangiocarcinoma (iCCA) ≤2.0cm. Totally 213 pathologically confirmed iCCAs (44 ≤ 2.0cm and 169 of 2.0–5.0cm) from two institutes were included. Forty-four matching non-iCCA malignancies ≤2.0cm were also enrolled. Recurrence-free survival (RFS) was estimated and compared between iCCAs ≤2.0cm and 2.0–5.0cm. MRI features were analyzed and compared between iCCAs ≤2.0cm and 2.0–5.0cm, as well as between iCCAs ≤2.0cm and non-iCCAs ≤2.0cm. Univariate and multivariate regression analyses were performed to identify independent imaging features for discrimination. An MRI-based diagnostic model for iCCA ≤2.0cm was constructed by incorporating the independent imaging features. ICCAs ≤2.0cm had a significantly longer RFS than those of 2.0–5.0cm (log rank P =0.014). Imaging features of homogeneous signal (odds ratio (OR) = 6.677, P <0.001) and lack of vessel invasion (OR=7.56, P <0.001) were more frequently displayed in iCCAs ≤2.0cm compared to iCCAs of 2.0–5.0cm independently. In the small lesions ≤2.0cm, imaging features of progressive or persistent enhancement pattern (OR=27.78, P =0.002) and rim diffusion restriction (OR=5.70, P =0.027) were independent imaging features suggestive of iCCA over non-iCCA malignancy; their combination yielded an area under the curve value of 0.824, with a sensitivity of 97.73%. The "very early" iCCA ≤2.0cm was associated with a favorable outcome after surgery, it displayed different and relatively atypical imaging manifestations compared with those of 2.0–5.0cm. Furthermore, in the small lesions ≤ 2.0cm, MRI can be served as a useful non-invasive diagnostic tool for iCCA in clinical screening with high sensitivity. • The "very early" iCCA ≤2.0cm was associated with favorable outcome after surgery. • The "very early" iCCA displayed different and relatively atypical imaging features. • MRI can be a useful diagnostic tool for "very early" iCCA in clinical screening. [ABSTRACT FROM AUTHOR]

Objective: Although breast magnetic resonance imaging (MRI) is a valuable screening tool, breast MRI testing burden was associated with cancer worry and quality of life. We aimed to develop and validate the MRI‐related distress scale (MRI‐DS) to assess comprehensive distress specifically related to breast MRI. Methods: We enrolled women aged above 18 years, diagnosed breast cancer, had MRI examination at least one time, and who could speak and read Korean in phase I and enrolled women aged above 18 years, visited outpatient clinic of breast general surgery, had undergone MRI examination at least once, and could speak and read Korean in phase II. We excluded patients who had any physical or psychiatric conditions in both phases. We recruited from a tertiary university‐based hospital in South Korea between April and August 2023. Results: All 18 items had acceptable levels of item correlation (≥0.30) in the explanatory factor analysis with a four‐factor solution. The fit indices for the four‐factor solution model were good. The discriminant validity of the MRI‐DS had a moderate correlation with general anxiety or quality of life. In the known‐group analysis, those who reported MRI as the most burden breast examination had higher total scores. Conclusion: The validity of the MRI‐DS has been confirmed as a scale for measuring the specific distress caused by breast MRI. The MRI‐DS is recommended to health professional to communicate with patients with MRI. Clinical Implications: It can be used to assess the distress associated with MRI screening in breast cancer patients. Physician could use MRI‐DS to discuss the reasons for distress caused by breast MRI screening and to address specific sources of discomfort associated with it. [ABSTRACT FROM AUTHOR]

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Background: Breast MRI has been recommended as supplemental screening tool to mammography and breast ultrasound of breast cancer by international guidelines, but its long examination time and use of contrast material remains concerning. Purpose: To develop an unenhanced radiomics model with using non‐gadolinium based sequences for detecting breast cancer based on T2‐weighted (T2W) and diffusion‐weighted (DW) MRI. Study Type: Retrospective analysis followed by retrospective and prospective cohorts study. Population: 1760 patients: Of these, 1293 for model construction (n = 775 for training and 518 for validation). The remaining patients for model testing in internal retrospective (n = 167), internal prospective (n = 188), and external retrospective (n = 112) cohorts. Field Strength/Sequence: 3.0T MR scanners from two institution. T2WI, DWI, and first contrast‐enhanced T1‐weighted sequence. Assessment: AUCs in distinguishing breast cancer were compared between combined model with gadolinium agent sequence and unenhanced model. Subsequently, the AUCs in testing cohorts of unenhanced model was compared with two radiologists' diagnosis for this research. Finally, patient subgroup analysis in testing cohorts was performed based on clinical subgroups and different types of malignancies. Statistical Tests: Mann–Whitney U test, Kruskal‐Wallis H test, chi‐square test, weighted kappa test, and DeLong's test. Results: The unenhanced radiomics model performed best under Gaussian process (GP) classifiers (AUC: training, 0.893; validation, 0.848) compared to support vector machine (SVM) and logistic, showing favorable prediction in testing cohorts (AUCs, 0.818–0.840). The AUCs for the unenhanced radiomics model were not statistically different in five cohorts from those of the combined radiomics model (P, 0.317–0.816), as well as the two radiologists (P, 0.181–0.918). The unenhanced radiomics model was least successful in identifying ductal carcinoma in situ, whereas did not show statistical significance in other subgroups. Data Conclusion: An unenhanced radiomics model based on T2WI and DWI has comparable diagnostic accuracy to the combined model using the gadolinium agent. Level of Evidence: 4 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a dual-energy CT (DECT)-based model for noninvasively differentiating between benign and malignant breast lesions detected on DECT. Materials and methods: This study prospectively enrolled patients with suspected breast cancer who underwent dual-phase contrast-enhanced DECT from July 2022 to July 2023. Breast lesions were randomly divided into the training and test cohorts at a ratio of 7:3. Clinical characteristics, DECT-based morphological features, and DECT quantitative parameters were collected. Univariate analyses and multivariate logistic regression were performed to determine independent predictors of benign and malignant breast lesions. An individualized model was constructed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic ability of the model, whose calibration and clinical usefulness were assessed by calibration curve and decision curve analysis. Results: This study included 200 patients (mean age, 49.9 ± 11.9 years; age range, 22–83 years) with 222 breast lesions. Age, lesion shape, and the effective atomic number (Zeff) in the venous phase were significant independent predictors of breast lesions (all p < 0.05). The discriminative power of the model incorporating these three factors was high, with AUCs of 0.844 (95%CI 0.764–0.925) and 0.791 (95% CI 0.647–0.935) in the training and test cohorts, respectively. The constructed model showed a preferable fitting (all p > 0.05 by the Hosmer-Lemeshow test) and provided enhanced net benefits than simple default strategies within a wide range of threshold probabilities in both cohorts. Conclusion: The DECT-based model showed a favorable diagnostic performance for noninvasive differentiation between benign and malignant breast lesions detected on DECT. Critical relevance statement: The combination of clinical and morphological characteristics and DECT-derived parameter have the potential to identify benign and malignant breast lesions and it may be useful for incidental breast lesions on DECT to decide if further work-up is needed. Key Points: It is important to characterize incidental breast lesions on DECT for patient management. DECT-based model can differentiate benign and malignant breast lesions with good performance. DECT-based model is a potential tool for distinguishing breast lesions detected on DECT. [ABSTRACT FROM AUTHOR]

For patients presenting with prostate imaging reporting and data system (PI-RADS) 3/4 findings on magnetic resonance imaging (MRI) examinations, the standard recommendation typically involves undergoing a biopsy for pathological assessment to ascertain the nature of the lesion. This course of action, though essential for accurate diagnosis, invariably amplifies the psychological distress experienced by patients and introduces a host of potential complications associated with the biopsy procedure. However, [18F]DCFPyL PET/CT imaging emerges as a promising alternative, demonstrating considerable diagnostic efficacy in discerning benign prostate lesions from malignant ones. This study aims to explore the diagnostic value of [18F]DCFPyL PET/CT imaging for prostate cancer in patients with PI-RADS 3/4 lesions, assisting in clinical decision-making to avoid unnecessary biopsies. 30 patients diagnosed with PI-RADS 3/4 lesions through mpMRI underwent [18F]DCFPyL PET/CT imaging, with final biopsy pathology results as the "reference standard". Diagnostic performance was assessed through receiver operating characteristic (ROC) analysis, evaluating the diagnostic efficacy of molecular imaging PSMA (miPSMA) visual analysis and semi-quantitative analysis in [18F]DCFPyL PET/CT imaging. Lesions were assigned miPSMA scores according to the prostate cancer molecular imaging standardized evaluation criteria. Among the 30 patients, 13 were pathologically confirmed to have prostate cancer. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of visual analysis in [18F]DCFPyL PET/CT imaging for diagnosing PI-RADS 3/4 lesions were 61.5%, 88.2%, 80.0%, 75.0%, and 76.5%, respectively. Using SUVmax 4.17 as the optimal threshold, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for diagnosis were 92.3%, 88.2%, 85.7%, 93.8%, and 90.0%, respectively. The area under the ROC curve (AUC) for semi-quantitative analysis was 0.94, significantly higher than visual analysis at 0.80. [18F]DCFPyL PET/CT imaging accurately diagnosed benign lesions in 15 (50%) of the PI-RADS 3/4 patients. For patients with PI-RADS 4 lesions, the positive predictive value of [18F]DCFPyL PET/CT imaging reached 100%. [18F]DCFPyL PET/CT imaging provides potential preoperative prediction of lesion nature in mpMRI PI-RADS 3/4 patients, which may aid in treatment decision-making and reducing unnecessary biopsies. [ABSTRACT FROM AUTHOR]

Background: To analyse the signs of malignant tumours in breast magnetic resonance imaging (MRI) and further improve the imaging diagnosis level of malignant breast tumours. Materials and Methods: The plain and enhanced MRI data of 60 patients who visited our hospital between January 2011 and January 2021 were analysed retrospectively, with 118 lesions in total. Malignant breast tumour signs were assessed. Results: A total of 96 lesions were pathologically confirmed as breast cancer, with 76 cases of single breast and single lesion, 10 cases of double breast and single lesion, 6 cases of single breast and two lesions, and 4 cases of single breast and three lesions. Regarding morphology, 58 lesions were regular in appearance and 60 were irregular, of which 50 had a burr sign or sharp angle sign. Strengthening methods included 52 cases of homogeneous strengthening, 3 of annular strengthening and 2 of cluster strengthening. Conclusion: A burr margin, local skin thickening, depression, adhesion and axillary lymph node enlargement are reliable MRI signs of breast cancer. When combined with Breast Imaging Reporting and Data System grading, these signs can effectively differentiate between benign and malignant breast tumours and constitute a key reference value for the diagnosis of breast cancer. [ABSTRACT FROM AUTHOR]

Ectopic breast tissue, which results from incomplete regression of the mammary line during embryogenesis, is observed in 0.2%-6% of the population. Carcinoma development in ectopic breast tissue, especially in the abdominal or chest wall, is rare. Here we present the unusual case of a 38-year-old woman with invasive ductal carcinoma in the ectopic breast tissue on the left side of the chest wall and concurrent fibroadenoma in the ectopic breast tissue on the right side. We also describe the US and MR findings of these masses. [ABSTRACT FROM AUTHOR]

Purpose: Women with high breast density (HBD) carry an increased risk for breast cancer (BC). The aim of the study was to provide data on awareness and knowledge gaps among women with vs w/o HBD about BC risk factors (BCRFs), which is the basis for effective communication about screening. Patients and Methods: This was a web-based survey of 3000 women aged ≥ 30 and ≤ 70 from six countries. It comprised of 45 questions. T-tests and chi-square tests with False Discovery Rate adjustments were conducted as applicable, with significant differences reported at α=0.05. Results: Three-thousand women were included in the analysis, 733 (24.4%) had HBD. Overall, 39% of women were familiar with the concept of HBD in the context of BC. Thirty-one percent of women were aware of HBD as BCRF and for 24% of women HBD was personally applicable. A significantly higher proportion of women with HBD were aware of almost all BCRFs compared to women w/o HBD (p ≤ 0.05). Similarly, a significantly higher proportion of women with HBD have undergone screening procedures compared to women w/o HBD (p ≤ 0.05). Women with HBD were significantly better aware of basic facts about BC (p ≤ 0.05). A total of 1617 women underwent mammography, 904 ultrasound and 150 MRI during their last screening. The most relevant source of information about BC was the health care professional, as reported by 63% of women. Conclusion: Overall 39% of women were familiar with HBD as BCRF. Lack of BCRF awareness may contribute to delayed screenings, missed opportunities for early detection, and potentially poorer outcomes for individuals with dense breast tissue. Thus, this information should be communicated more widely. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate whether artifacts on contrast-enhanced (CE) breast MRI maximum intensity projections (MIPs) might already be forecast before gadolinium-based contrast agent (GBCA) administration during an ongoing examination by analyzing the unenhanced T1-weighted images acquired before the GBCA injection. Materials and methods: This IRB-approved retrospective analysis consisted of n = 2884 breast CE MRI examinations after intravenous administration of GBCA, acquired with n = 4 different MRI devices at different field strengths (1.5 T/3 T) during clinical routine. CE-derived subtraction MIPs were used to conduct a multi-class multi-reader evaluation of the presence and severity of artifacts with three independent readers. An ensemble classifier (EC) of five DenseNet models was used to predict artifacts for the post-contrast subtraction MIPs, giving as the input source only the pre-contrast T1-weighted sequence. Thus, the acquisition directly preceded the GBCA injection. The area under ROC (AuROC) and diagnostics accuracy scores were used to assess the performance of the neural network in an independent holdout test set (n = 285). Results: After majority voting, potentially significant artifacts were detected in 53.6% (n = 1521) of all breast MRI examinations (age 49.6 ± 12.6 years). In the holdout test set (mean age 49.7 ± 11.8 years), at a specificity level of 89%, the EC could forecast around one-third of artifacts (sensitivity 31%) before GBCA administration, with an AuROC = 0.66. Conclusion: This study demonstrates the capability of a neural network to forecast the occurrence of artifacts on CE subtraction data before the GBCA administration. If confirmed in larger studies, this might enable a workflow-blended approach to prevent breast MRI artifacts by implementing in-scan personalized predictive algorithms. Clinical relevance statement: Some artifacts in contrast-enhanced breast MRI maximum intensity projections might be predictable before gadolinium-based contrast agent injection using a neural network. Key Points: • Potentially significant artifacts can be observed in a relevant proportion of breast MRI subtraction sequences after gadolinium-based contrast agent administration (GBCA). • Forecasting the occurrence of such artifacts in subtraction maximum intensity projections before GBCA administration for individual patients was feasible at 89% specificity, which allowed correctly predicting one in three future artifacts. • Further research is necessary to investigate the clinical value of such smart personalized imaging approaches. [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance imaging of breasts often presents complex backgrounds. Breast tumors exhibit varying sizes, uneven intensity, and indistinct boundaries. These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation. Thus, we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms. Initially, the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs. Subsequently, we devise a fusion network incorporating multi-scale features and boundary attention mechanisms for breast tumor segmentation. We incorporate multi-scale parallel dilated convolution modules into the network, enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion techniques. Additionally, attention and boundary detection modules are included to augment the network's capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel domains. Furthermore, a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network's boundary maintenance capability through additional loss. The method was evaluated using breast data from 207 patients at Ruijin Hospital, resulting in a 6.64% increase in Dice similarity coefficient compared to the benchmark U-Net. Experimental results demonstrate the superiority of the method over other segmentation techniques, with fewer model parameters. [ABSTRACT FROM AUTHOR]

This study aimed to compare tumor lesion detectability and diagnostic accuracy of whole-body magnetic resonance imaging (WB-MRI) and radioiodine-labeled meta-iodo-benzylguanidine (mIBG) imaging techniques in patients with metastatic pheochromocytoma and paraganglioma (PPGL). This retrospective study included 13 patients had pheochromocytoma and 5 had paraganglioma, who were all suspected of having metastatic tumors. Each patient underwent WB-MRI and 123I-mIBG as a pretreatment screening for 131I-mIBG therapy. Two expert reviewers evaluated WB-MRI, 123I-mIBG images, and post-therapy 131I-mIBG images for the presence of metastatic lesions in the lungs, bones, liver, lymph nodes, and other organs. Diagnostic measures for detecting metastatic lesions, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and receiver operating characteristics (ROC)—area under the curve (AUC), were calculated for each imaging technique. We analyzed WB-MRI images for detecting metastatic lesions, which demonstrated sensitivity, specificity, accuracy, PPV, NPV, and AUC of 82%, 97%, 90%, 96%, 86%, and 0.92, respectively. These values were 83%, 95%, 89%, 94%, 86%, and 0.90 in 123I-mIBG images and 85%, 92%, 89%, 91%, 87%, and 0.91 in post-therapy 131I-mIBG images, respectively. Our results reveal the comparable diagnostic accuracy of WB-MRI to one of the mIBG images. [ABSTRACT FROM AUTHOR]

Tumour hypoxia is a known microenvironmental culprit for treatment resistance, tumour recurrence and promotion of metastatic spread. Despite the long-known existence of this factor within the tumour milieu, hypoxia is still one of the greatest challenges in cancer management. The transition from invasive and less reliable detection methods to more accurate and non-invasive ways to identify and quantify hypoxia was a long process that eventually led to the promising results showed by functional imaging techniques. Hybrid imaging, such as PET-CT, has the great advantage of combining the structural or anatomical image (offered by CT) with the functional or metabolic one (offered by PET). However, in the context of hypoxia, it is only the PET image taken after appropriate radiotracer administration that would supply hypoxia-specific information. To overcome this limitation, the development of the latest hybrid imaging systems, such as PET-MRI, enables a synergistic approach towards hypoxia imaging, with both methods having the potential to provide functional information on the tumour microenvironment. This study is designed as a systematic review of the literature on the newest developments of PET-MRI for the imaging of hypoxic cells in breast cancer. The analysis includes the affinity of various PET-MRI tracers for hypoxia in this patient group as well as the correlations between PET-specific and MRI-specific parameters, to offer a broader view on the potential for the widespread clinical implementation of this hybrid imaging technique. [ABSTRACT FROM AUTHOR]

Importance: Among all US women, breast cancer is the second most common cancer and the second most common cause of cancer death. In 2023, an estimated 43 170 women died of breast cancer. Non-Hispanic White women have the highest incidence of breast cancer and non-Hispanic Black women have the highest mortality rate. Objective: The USPSTF commissioned a systematic review to evaluate the comparative effectiveness of different mammography-based breast cancer screening strategies by age to start and stop screening, screening interval, modality, use of supplemental imaging, or personalization of screening for breast cancer on the incidence of and progression to advanced breast cancer, breast cancer morbidity, and breast cancer–specific or all-cause mortality, and collaborative modeling studies to complement the evidence from the review. Population: Cisgender women and all other persons assigned female at birth aged 40 years or older at average risk of breast cancer. Evidence Assessment: The USPSTF concludes with moderate certainty that biennial screening mammography in women aged 40 to 74 years has a moderate net benefit. The USPSTF concludes that the evidence is insufficient to determine the balance of benefits and harms of screening mammography in women 75 years or older and the balance of benefits and harms of supplemental screening for breast cancer with breast ultrasound or magnetic resonance imaging (MRI), regardless of breast density. Recommendation: The USPSTF recommends biennial screening mammography for women aged 40 to 74 years. (B recommendation) The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of screening mammography in women 75 years or older. (I statement) The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of supplemental screening for breast cancer using breast ultrasonography or MRI in women identified to have dense breasts on an otherwise negative screening mammogram. (I statement) This 2024 Recommendation Statement from the US Preventive Services Task Force recommends biennial screening mammography for women aged 40 to 74 years (B recommendation) and concludes that evidence is insufficient to assess the balance of benefits and harms of screening mammography in women 75 years or older (I statement) and of screening using ultrasonography or MRI in women with dense breasts on a negative mammogram (I statement). [ABSTRACT FROM AUTHOR]

Introduction: Magnetic resonance imaging (MRI)-guided wire localization can be applied to assist to remove suspected breast lesions accurately. This study aimed to evaluate the clinical application value of this technique in Chinese women. Methods: A total of 126 patients (131 lesions) who had underwent such technique in our hospital from April 2017 to June 2023 were enrolled. 1.5T MRI system and a wire localization device were used. Image characteristics, clinical features and postoperative pathology were collected and analyzed. Results: All of 126 patients (131 lesions) were successfully localized by MRI and excised for biopsy. There were 39 malignant lesions (29.77%) and 92 benign lesions (70.23%). There was no significant correlation between the morphology of DCE-MRI and the ratio of malignant lesions (P=0.763), while there was a statistical correlation between the BPE, TIC curve and the malignancy rate (P<0.05). All the lesions were assessed according to BI-RADS category of MRI (C4A=77, C4B=40, C4C=12, C5=2). The malignancy rates were as follows: 16.88% for 4A lesions (13/77), 37.50% for 4B lesions (15/40), 75.00% for 4C lesions (9/12) and 100% for 5 lesions (2/2). There was a significant correlation between the BI-RADS category and the incidence of benign-to-malignant lesions (P<0.001). Conclusion: MRI-guided wire localization can assist to remove suspected breast lesions early, safely and accurately. This technique makes up for the deficiency of X-ray and ultrasound, improves the accuracy of diagnosis and resection therapy in intraductal carcinoma and early invasive carcinoma, and helps to improve the the prognosis of breast cancer. [ABSTRACT FROM AUTHOR]

Background: Neoadjuvant chemotherapy (NACT) is a treatment option for breast cancer patients that allows for the assessment of tumor response during treatment. This information can be used to adjust treatment and improve outcomes. However, the optimal imaging modalities and parameters for assessing tumor response to NACT are not well established. Methods: This study included 173 breast cancer patients who underwent NACT. Patients were imaged with ultrasound (US), mammography (MMG), and magnetic resonance imaging (MRI) at baseline, after two cycles of NACT, and before breast surgery. US parameters included lesion morphology, Doppler variables, and elastography measurements. MMG and MRI were evaluated for the presence of nodules and tumor dimensions. The pathological response to NACT was determined using the residual cancer burden (RCB) classification. Results: The US parameter with the highest power for predicting pathological complete response (pCR) was shear wave elastography (SWE) maximum speed inside the tumor at baseline. For nonluminal tumors, the end diastolic velocity measured by US after two cycles of NACT showed the highest predictive value for pCR. Similarly, SWE maximum speed after two cycles of NACT had the highest discriminating power for predicting RCB-III in luminal tumors, while the same parameter measured at baseline was most predictive for nonluminal tumors. Conclusions: This study provides evidence that mid-treatment Doppler US and other imaging modalities can be used to predict the response to NACT in breast cancer patients. Functional parameters, such as blood flow velocities and SWE measurements, demonstrated superior predictive value for pCR, while morphological parameters had limited value. These findings have implications for personalized treatment strategies and may contribute to improved outcomes in the management of breast cancer. [ABSTRACT FROM AUTHOR]

Aim: To explore the performance of single or combined ultrasound (US) in diagnosing malignant breast lesions, and then compare the results with a magnetic resonance imaging (MRI) aspect. Material and methods: Patients’ demographics, tumor markers, and imaging examination were collected. Diagnostic models based on B-mode, color Doppler ultrasounds (CDU), strain elastography (SE), MRI, B-mode US + CDU, B-mode US + SE, and B-mode US + CDU + SE were developed using logistic regression analysis. The performance was assessed using the area under the curve (AUC) with 95% confidence intervals (CIs). Performance of MRI and B-mode US + CDU was compared using DeLong analysis. Results: For single imaging modality, MRI showed the best performance, with AUC of 0.938 (95%CI: 0.888-0.988). For combined US modalities, combination of B-mode US and CDU had the best performance, with AUC of 0.948 (95%CI: 0.877-1.000). There was no significant difference in the diagnostic performance between the combination of B-mode US and CDU and MRI (p>0.05). Conclusions: Our study found the performance of B-mode + CDU was comparable to MRI. Our findings suggested that the combination of B-mode US and CDU was recommended to diagnose malignant breast lesions in order to save time and expense and provide guidance to make decisions for a biopsy. [ABSTRACT FROM AUTHOR]

Clinical and pathologic characteristics of the invasive ductal carcinoma (IDC) presenting as a thick-walled breast cyst are little known. Three female patients were included in this report. A palpable, nontender breast lump was found in all cases. While mammography showed a hyperdense mass, ultrasonography demonstrated a thick-walled cystic mass. Magnetic resonance imaging clearly showed the cystic breast lesions with ring-like or irregular rim enhancement. A grade III IDC was confirmed in all cases. All IDCs but one were estrogen receptor negative, progesterone receptor negative, and human epidermal growth factor receptor 2 negative, with merely weak progesterone receptor positivity (5%) in one case. All cases underwent surgical management first and postoperative chemotherapy. Breast malignancy presenting as a thick-walled cystic mass could be a highly aggressive IDC, even triple-negative breast cancer. It is imperative for breast cancer-related practitioners to identify the potentially malignant cystic lesions timely and adopt appropriate management. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common malignancy diagnosed in the female population worldwide and the leading cause of death among perimenopausal women. Screening is essential, since earlier detection in combination with improvements in breast cancer treatment can reduce the associated mortality. The aim of this study was to review and compare the recommendations from published guidelines on breast cancer screening. A total of 14 guidelines on breast cancer screening issued between 2014 and 2022 were identified. A descriptive review of relevant guidelines by the World Health Organization (WHO), the U.S. Preventive Services Task Force (USPSTF), the American Cancer Society (ACS), the National Comprehensive Cancer Network (NCCN), the American College of Obstetricians and Gynecologists (ACOG), the American Society of Breast Surgeons (ASBrS), the American College of Radiology (ACR), the Task Force on Preventive Health Care (CTFPHC), the European Commission Initiative on Breast Cancer (ECIBC), the European Society for Medical Oncology (ESMO), the Royal Australian College of General Practitioners (RACGP) and the Japanese Journal of Clinical Oncology (JJCO) for women both at average and high-risk was carried out. There is a consensus among all the reviewed guidelines that mammography is the gold standard screening modality for average-risk women. For this risk group, most of the guidelines suggest annual or biennial mammographic screening at 40–74 years, while screening should particularly focus at 50–69 years. Most of the guidelines suggest that the age limit to stop screening should be determined based on the women's health status and life expectancy. For women at high-risk, most guidelines recommend the use of annual mammography or magnetic resonance imaging, while the starting age should be earlier than the average-risk group, depending on the risk factor. There is discrepancy among the recommendations regarding the age at onset of screening in the various high-risk categories. The development of consistent international practice protocols for the most appropriate breast cancer screening programs seems of major importance to reduce mortality rates and safely guide everyday clinical practice. [ABSTRACT FROM AUTHOR]