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Benign breast tumors are a nonthreatening condition defined as abnormal cell growth within the breast without the ability to invade nearby tissue. However, benign lesions hold valuable biological information that can lead us toward better understanding of tumor biology. In this study, we have used two pathway analysis algorithms, Pathifier and gene set variation analysis (GSVA), to identify biological differences between normal breast tissue, benign tumors and malignant tumors in our clinical dataset. Our results revealed that one‐third of all pathways that were significantly different between benign and malignant tumors were immune‐related pathways, and 227 of them were validated by both methods and in the METABRIC dataset. Furthermore, five of these pathways (all including genes involved in cytokine and interferon signaling) were related to overall survival in cancer patients in both datasets. The cellular moieties that contribute to immune differences in malignant and benign tumors were analyzed using the deconvolution tool, CIBERSORT. The results showed that levels of some immune cells were specifically higher in benign than in malignant tumors, and this was especially the case for resting dendritic cells and follicular T‐helper cells. Understanding the distinct immune profiles of benign and malignant breast tumors may aid in developing noninvasive diagnostic methods to differentiate between them in the future. [ABSTRACT FROM AUTHOR]

Cancer is a critical global public health problem with meager median survival. It is therefore quite essential to detect this disease at an early stage to improve diagnostic results and consequently avoid serious complications. For this purpose, various researchers have implemented automated methods with the use of different medical imaging modalities. Accordingly, the expansion of deep learning techniques grants opportunities to enhance diagnosis, cure, and prevention. In this study, a diagnostic system for accurate classification of ultrasound breast abnormalities based on the powerful ResNet-50 CNN is proposed with the aim of providing early detection of breast cancer decease. The contribution of this work lies in the novel approach taken to improve the performance of the ResNet50 model in the classification of ultrasound breast cancer images. Transfer learning allows for the model to leverage pre-existing knowledge, while the application of data augmentation techniques enhances the diversity and quality of the training data. Additionally, the optimization of the batch size as a hyperparameter ensures that the model is able to effectively learn from the training data, leading to improved accuracy and efficiency in the classification process. This approach is crucial in the early detection and treatment of breast cancer. Quantitative and qualitative evaluations have been detailed in this study using Breast Ultrasound Dataset BUSI. Our presented work shows interesting results in terms of accuracy, specificity, sensitivity, and AUC which exceed the performance of other compared works. Moreover, the proposed method helps boost the clinical diagnosis of breast cancer. It may integrate a radiologist network, allowing them to constantly follow up on the patient's medical history. [ABSTRACT FROM AUTHOR]

Substantial progress in the use of chemo-photodynamic nano-drug delivery systems (nano-DDS) for the treatment of the malignant breast cancer has been achieved. The inability to customize precise nanostructures, however, has limited the therapeutic efficacy of the prepared nano-DDS to date. Here, we report a structurally defined tandem-responsive chemo-photosensitive co-nanoassembly to eliminate primary breast tumor and prevent lung metastasis. This both-in-one co-nanoassembly is prepared by assembling a biocompatible photosensitive derivative (pheophorbide-diphenylalanine peptide, PPA-DA) with a hypoxia-activated camptothecin (CPT) prodrug [(4-nitrophenyl) formate camptothecin, N-CPT]. According to computational simulations, the co-assembly nanostructure is not the classical core-shell type, but consists of many small microphase regions. Upon exposure to a 660 nm laser, PPA-DA induce high levels of ROS production to effectively achieve the apoptosis of normoxic cancer cells. Subsequently, the hypoxia-activated N-CPT and CPT spatially penetrate deep into the hypoxic region of the tumor and suppress hypoxia-induced tumor metastasis. Benefiting from the rational design of the chemo-photodynamic both-in-one nano-DDS, these nanomedicines exhibit a promising potential in the inhibition of difficult-to-treat breast tumor metastasis in patients with breast cancer. [Display omitted] [ABSTRACT FROM AUTHOR]

Considering the difficulty in detecting primary breast cancers using whole-body positron emission tomography (WBPET) owing to its limited spatial resolution, we aimed to evaluate the detectability of breast cancer by ring-type dedicated breast PET (DbPET) on the World Health Organization (WHO) histological classification in comparison with WBPET. A total of 938 patients with breast cancer underwent WBPET and ring-type DbPET, and 1021 lesions were histologically assessed based on the WHO classification of tumors of the breast. The findings of WBPET and DbPET were retrospectively evaluated and compared. The size-related sensitivity of DbPET was superior to that of WBPET for subcentimetric tumors (81.9% vs. 52.4%, P < 0.001). The histological distribution was as follows: 11 lobular carcinoma in situ, 158 ductal carcinoma in situ, 738 infiltrating duct carcinoma not otherwise specified (NOS), 12 lobular carcinoma NOS, 40 mucinous adenocarcinoma, 13 tubular carcinoma, 36 invasive breast carcinoma others, and 13 papillary neoplasms. WBPET had low sensitivity for lobular carcinoma in situ, ductal carcinoma in situ, lobular carcinoma NOS, mucinous adenocarcinoma, and tubular carcinoma. DbPET showed improved sensitivity for all the above except lobular and tubular carcinoma. The maximum standardized uptake values (SUVmax) of DbPET were significantly higher than those of WBPET for histological types, excluding lobular carcinoma in situ. The SUVmax of papillary neoplasms was high regardless of low-grade histology and Ki-67 labeling index. DBPET was found to have high sensitivity and SUVmax values for all histologic types that showed low sensitivity of detection on WBPET, except lobular carcinoma in situ. • Detection of primary breast cancer by dedicated breast PET was evaluated based on the WHO classification. • Dedicated breast PET showed high sensitivity and SUVmax values for all breast cancer histologic types that showed a low sensitivity of detection on whole-body PET, except lobular carcinoma in situ. • Dedicated breast PET findings represent histology-specific features. [ABSTRACT FROM AUTHOR]

Background: Today, breast cancer is the most diagnosed cancer worldwide. There are many different clinical presentations, radiological characteristics, and histological types of breast cancer. HER2 is overexpressed in a significant number of breast cancer cases reaching 20% of all breast cancers, and its overexpression is seen directly proportionate with a poor outcome and prognosis. Methods: We started this cross-sectional research from January 2022–December 2023 on 202 breast cancer patients who had 220 lesions. The molecular subtypes of the different lesions were determined in all the included cases. Magnetic resonance imaging (MRI) studies were conducted in all included cases. The MRI parameters included conventional MRI, diffusion-weighted analysis, and dynamic post-contrast T1-weighted imaging. Results: The prevalence of irregular margins (P < 0.001), linear and segmental distribution (P = 0.044), heterogeneous pattern (P < 0.001), and type 2 curve was statistically significantly higher in the HER2-positive lesions. Nipple infiltration incidence showed statistically significant elevation in the HER2-positive lesions (P = 0.017). The lesions' ADC and perilesional ADC in the HER2-positive lesions were also statistically significantly elevated. The best cutoff point of ADC to detect lesions with positive HER2 expression was > 0.885 × 10–3 mm2/s, with 65.7% sensitivity and 60% specificity, with a statistically significant value (p = 0.005). Conclusions: Magnetic resonance imaging of breast imaging is a promising noninvasive method for identifying breast tumors with the HER2 molecular subtype. Combining various radiological features by MRI may provide a conclusion for recognizing positive HER2 lesions. [ABSTRACT FROM AUTHOR]

The NanoString GeoMx digital spatial profiling is a new multiplexed platform that quantifies the abundance of tumor‐ and immune‐related proteins in a spatially resolved manner. We performed DSP for the simultaneous assessment of 52 analytes within spatially resolved tissue compartments defined by pan‐cytokeratin expression. We compared protein targets between 94 African American/Black and 65 European American/White cases, tumor and stromal tissue compartments, estrogen receptor alpha (ER)‐positive and ER‐negative cases, and explored potential biomarkers of survival. Of 33 analytes with robust signal for analysis, results were highly replicable. For a subset of markers, correlative analyses between DSP analytes and traditional immunohistochemistry scores revealed moderate to very strong associations between the two platforms. Similarly, DSP analytes and gene expression scores were concordant for 21 of 25 markers with overlap between the two datasets. Several analytes varied by ER status, and across the 25 immune markers surveyed, 14 had a significant inverse association with ER expression. B7 homolog 3 (B7‐H3; encoded by CD276) was the only analyte to show a significant difference by race, being lower in both the tumor and stromal compartments in Black women. DSP markers that were associated with survival included CD8, CD25, CD56, CD127, EpCAM, ER, Ki‐67, and STING. We conclude that DSP is an efficient tool for screening tumor‐ and immune‐related markers in a simultaneous fashion and yields results that are concordant with established immune profiling assays. DSP immune analytes were inversely associated with ER expression, in agreement with a substantial body of previous work that documents higher immune infiltration in ER‐negative breast cancers. This technology revealed that scores of the B7‐H3 protein were significantly lower in breast cancers from Black women compared with White women, an intriguing finding that requires replication in independent and racially diverse female populations. [ABSTRACT FROM AUTHOR]

TP53 gene mutations are very common in human cancer. While such mutations abrogate the tumor suppressive activities of the wild‐type (wt) p53 protein, some of them also endow the mutant (mut) protein with oncogenic gain of function (GOF), facilitating cancer progression. Yet, p53 may acquire altered functionality even without being mutated; in particular, experiments with cultured cells revealed that wtp53 can be rewired to adopt mut‐like features in response to growth factors or cancer‐mimicking genetic manipulations. To assess whether such rewiring also occurs in human tumors, we interrogated gene expression profiles and pathway deregulation patterns in the METABRIC breast cancer (BC) dataset as a function of TP53 gene mutation status. Harnessing the power of machine learning, we optimized a gene expression classifier for ER+Her2‐ patients that distinguishes tumors carrying TP53 mutations from those retaining wt TP53. Interestingly, a small subset of wt TP53 tumors displayed gene expression and pathway deregulation patterns markedly similar to those of TP53‐mutated tumors. Moreover, similar to TP53‐mutated tumors, these 'pseudomutant' cases displayed a signature for enhanced proliferation and had worse prognosis than typical wtp53 tumors. Notably, these tumors revealed upregulation of genes which, in BC cell lines, were reported to be positively regulated by p53 GOF mutants. Thus, such tumors may benefit from mut p53‐associated activities without having to accrue TP53 mutations. [ABSTRACT FROM AUTHOR]

Sacituzumab govitecan, targeting trophoblast cell-surface antigen 2 (TROP2), is approved for the treatment of triple-negative and hormone receptor-positive/HER2-negative breast cancers. However, detailed studies comparing TROP2 protein expression in the different molecular subtypes of breast cancer are limited, and definitive evidence supporting the use of TROP2 as a biomarker for predicting response to this agent in patients with breast cancer is currently lacking. To compare the expression of TROP2 in the different molecular subtypes of breast cancer. Immunohistochemical staining for TROP2 was performed on 94 therapy-naive primary invasive breast carcinomas, including 25 luminal A-like, 25 luminal B-like, 19 HER2-like, and 25 triple-negative tumors. Intermediate to high levels of TROP2 expression were observed in the majority of carcinomas of each molecular subtype, with a wide range of expression in each subtype. Occasional tumors with low or absent TROP2 expression were encountered, including two metaplastic carcinomas which were completely negative for TROP2. Our observations support the continued investigation of the efficacy of sacituzumab govitecan in all molecular subtypes of breast carcinoma. Furthermore, the observed wide range of expression of TROP2 suggests that TROP2 may have potential utility as a biomarker for predicting responsiveness to sacituzumab govitecan. If this proves to be the case, then immunohistochemical staining for TROP2 would be critical for identifying those patients whose tumors are completely negative for TROP2, since these patients may be least likely or unlikely to respond to this agent, and alternative therapies may be more appropriate in such instances. • TROP2 expression did not differ between breast carcinoma molecular subtypes. • The majority of each subtype exhibited intermediate to high expression of TROP2. • Two tumors (both metaplastic carcinomas) were completely negative for TROP2. • Literature suggests TROP2-negative tumors may not respond to TROP2-directed agents. [ABSTRACT FROM AUTHOR]

Abstract Introduction Breast cancer is the most frequent cancer in women worldwide. The primary treatment is breast-conserving surgery or mastectomy with an adequate clearance margin. Diathermy blade is used extensively in breast-conserving surgery. Surgical smoke produced as a side product has cancer-specific molecular features. Differential mobility spectrometry (DMS) is a rapid and affordable technology for analysis of complex gas mixtures. In our study we examined surgical smoke from malignant and benign breast tissue created with a diathermy blade using DMS. Material and methods Punch biopsies of 4 mm diameter from breast cancer surgical specimens were taken during gross dissection of fresh surgical specimen and placed in a well plate. The measurement system is a custom-built device called automatic tissue analysis system (ATAS) based on a DMS sensor. Each specimen was incised with a diathermy blade and the surgical smoke was analyzed. Results We examined 106 carcinoma samples from 21 malignant breast tumors. Benign samples (n = 198) included macroscopically normal mammary gland (n = 82), adipose tissue (n = 88) and vascular tissue (n = 28). The classification accuracy when comparing malignant samples to all benign samples was 87%. The sensitivity was 80% and the specificity was 90%. The classification accuracy of carcinomas to ductal and lobular was 94%, 47%, respectively. Conclusions Benign and malignant breast tissue can be identified with ATAS. These results lay foundation for intraoperative margin assessment with DMS from surgical smoke. [ABSTRACT FROM AUTHOR]

Breast cancer is a major health concern affecting women and men globally. Early detection and accurate classification of breast cancer are vital for effective treatment and survival of patients. This study addresses the challenge of accurately classifying breast tumors using machine learning classifiers such as MLP, AdaBoostM1, logit Boost, Bayes Net, and the J48 decision tree. The research uses a dataset available publicly on GitHub to assess the classifiers' performance and differentiate between the occurrence and non-occurrence of breast cancer. The study compares the 10-fold and 5-fold cross-validation effectiveness, showing that 10- fold cross-validation provides superior results. Also, it examines the impact of varying split percentages, with a 66% split yielding the best performance. This shows the importance of selecting appropriate validation techniques for machine learning-based breast tumor classification. The results also indicate that the J48 decision tree method is the most accurate classifier, providing valuable insights for developing predictive models for cancer diagnosis and advancing computational medical research. [ABSTRACT FROM AUTHOR]

The characterization of tumors is crucial for guiding appropriate treatment strategies and enhancing patient survival rates. Surface thermography shows promise in the non-invasive detection of thermal patterns associated with the existence of breast tumors. Nevertheless, the precise prediction of both tumor size and location using temperature characteristics presents a critical challenge. This is due to the limited availability of thermal images labeled with the corresponding tumor size and location. This work proposes a deep learning approach based on convolutional neural networks (CNN) in combination with thermographic images for estimating breast tumor size and location. Successive COMSOL-based simulations are conducted, including a 3D breast model with various tumor scenarios. Thus, different noise levels were included in the development of the thermographic image dataset. Every image was accordingly labeled with the corresponding tumor location and size to train the CNN model. Mean absolute error (MAE) and the coefficient of determination (R²) were considered as evaluation metrics. The results show that the proposed CNN model achieved a reasonable prediction performance with MAE-R² values of 0.872-98.6% for tumor size, 1.161-96.8% for x location, 1.086-97.1% for y location, and 0.954-96.7% for z location. This study indicates that the combination of surface thermography and deep learning is a convenient tool for predicting breast tumor parameters. [ABSTRACT FROM AUTHOR]

Posttraumatic growth (PTG) in patients with breast cancer has been reported. Mindfulness-based stress reduction (MBSR) has been found to be effective in improving the psychological well-being during cancer care. This study aimed to evaluate the impact of MBSR on the levels of PTG, perceived social support, and quality of life. A sample of 101 participants who experienced early chemotherapy enrolled in an MBSR group (n = 50) and a routine care group (n = 51) for 8 weeks. Their postintervention data were combined with preintervention data through a bundle of questionnaires reporting levels of PTG, perceived social support, and quality of life. MBSR showed the prominent validity in raising the levels of PTG and increasing in perceived social support and quality of life from pre- to posttreatment. MBSR improved the levels of PTG, perceived social support, and quality of life in patients with breast cancer in early chemotherapy. In conclusion, MBSR may help to optimize the positive psychological processes that will be targeted to maximize the efficacy of treatment for future cancer survivors. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most fatal diseases leading to the death of several women across the world. But early diagnosis of breast cancer can help to reduce the mortality rate. So an efficient multi-task learning approach is proposed in this work for the automatic segmentation and classification of breast tumors from ultrasound images. The proposed learning approach consists of an encoder, decoder, and bridge blocks for segmentation and a dense branch for the classification of tumors. For efficient classification, multi-scale features from different levels of the network are used. Experimental results show that the proposed approach is able to enhance the accuracy and recall of segmentation by 1.08%, 4.13%, and classification by 1.16%, 2.34%, respectively than the methods available in the literature. [ABSTRACT FROM AUTHOR]

Targeted therapy for patients with HER2‐positive (HER2+) breast cancer has improved overall survival, but many patients still suffer relapse and death from the disease. Intratumor heterogeneity of both estrogen receptor (ER) and HER2 expression has been proposed to play a key role in treatment failure, but little work has been done to comprehensively study this heterogeneity at the single‐cell level. In this study, we explored the clinical impact of intratumor heterogeneity of ER protein expression, HER2 protein expression, and HER2 gene copy number alterations. Using combined immunofluorescence and in situ hybridization on tissue sections followed by a validated computational approach, we analyzed more than 13 000 single tumor cells across 37 HER2+ breast tumors. The samples were taken both before and after neoadjuvant chemotherapy plus HER2‐targeted treatment, enabling us to study tumor evolution as well. We found that intratumor heterogeneity for HER2 copy number varied substantially between patient samples. Highly heterogeneous tumors were associated with significantly shorter disease‐free survival and fewer long‐term survivors. Patients for which HER2 characteristics did not change during treatment had a significantly worse outcome. This work shows the impact of intratumor heterogeneity in molecular diagnostics for treatment selection in HER2+ breast cancer patients and the power of computational scoring methods to evaluate in situ molecular markers in tissue biopsies. A subset of breast carcinomas exhibits substantial heterogeneity with regard to HER2 protein expression, HER2 gene copy number alteration, and estrogen receptor protein expression both between cases but also within cases (inter‐ and intratumor heterogeneity). Highly heterogeneous tumors were associated with significantly shorter disease‐free survival and fewer long‐term survivors. Addressing cell‐to‐cell variation may be of importance in diagnostic settings. [ABSTRACT FROM AUTHOR]

Background: The presence of lymphovascular invasion (LVI) in cases with breast cancer is considered a bad prognostic sign. The purpose of this study is to compare the efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) versus unenhanced magnetic resonance imaging (UE-MRI + DBT) in predicting LVI in women with pathologically confirmed breast cancer. Methods: This prospective self-controlled study enrolled a total of 70 cases of pathologically proven breast cancer. All the patients underwent tomosynthesis, non-contrast, and post-contrast MRI. Depending on the broken halo sign seen in tomosynthesis, peritumoral edema, dark rim diffusion at diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) values evaluated in MRI. Results: The accuracy of LVI detection by tomosynthesis was 58%; unenhanced and enhanced MRI had the same results at 60%. The accuracy of detecting LVI was raised to 64% by combining the tomosynthesis results with unenhanced MRI. Conclusions: Tomosynthesis parameters are promising tools in detecting LVI in breast cancer with better diagnostic accuracy in combination with unenhanced MRI. [ABSTRACT FROM AUTHOR]

Copyright of African Journal of Reproductive Health is the property of Women's Health & Action Research Centre and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The 21-gene recurrence score (RS) assay (Oncotype DX) is used to guide adjuvant chemotherapy use for patients with hormone receptor-positive, HER2 (human epidermal growth factor receptor 2)-negative, axillary node-negative breast cancer. Its role, however, in providing prognostic information for late distant recurrence when added to clinicopathologic prognostic factors is unknown. Methods: A patient-specific meta-analysis including 10,004 women enrolled in three trials was updated using extended follow-up data from TAILORx, integrating the RS with histologic grade, tumor size, and age at surgery for the RSClin tool. Cox models integrating clinicopathologic factors and the RS were compared by using likelihood ratio (LR) tests. External validation of prognosis for distant recurrence in years 0 to 10 and 5 to 10 was performed in an independent cohort of 1098 women in a real-world registry. Results: RSClin provided significantly more prognostic information than either the clinicopathologic factors (ΔLR chi-square, 86.2; P<0.001) or RS alone (ΔLR chi-square, 131.0; P<0.001). The model was prognostic in an independent cohort for distant recurrence by 10 years after diagnosis (standardized hazard ratio, 1.56; 95% confidence interval, 1.25 to 1.94), was associated with late distant recurrence risk between 5 and 10 years after diagnosis (standardized hazard ratio, 1.78; 95% confidence interval, 1.25 to 2.55), and approximated the observed 10-year distant recurrence risk (Lin concordance, 0.87) and 5- to 10-year distant recurrence risk (Lin concordance, 0.92). Conclusions: The 21-gene RS is prognostic for distant recurrence and overall survival in early breast cancer. A model integrating the 21-gene RS and clinicopathologic factors improved estimates of distant recurrence risk compared with either used individually and stratified late distant recurrence risk. (Funded by the National Cancer Institute, National Institutes of Health [U10CA180820, U10CA180794, UG1CA189859, U10CA180868, and U10CA180822] and others.) [ABSTRACT FROM AUTHOR]

Morning Report is a time-honored tradition where physicians-in-training present cases to their colleagues and clinical experts to collaboratively examine an interesting patient presentation. The Morning Report section seeks to carry on this tradition by presenting a patient's chief concern and story, inviting the reader to develop a differential diagnosis and discover the diagnosis alongside the authors of the case. This report examines the story of a 53-year-old woman with a history of breast cancer who presented with abnormal axillary lymph nodes detected on surveillance imaging. Using history, physical examination, and diagnostic workup, an illness script for her presentation emerges. A differential diagnosis is developed and refined until a final diagnosis is confirmed. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: Non-palpable breast tumors represent an increasing management problem in modern Breast Units. Therefore, a simple and accurate procedure to localize these lesions is needed. To date, the most commonly used technique is wire localization, but there are some disadvantages. Methods: We conducted a prospective study on patients with malignant or benign non-palpable breast tumors who were surgically treated and underwent intraoperative ultrasound (IOUS) from May 2006 to June 2007. Margins of excision were inked and specifically assessed by the pathologist, and were considered positive if ≤1 mm. Results: There were 77 patients (60 malignant and 17 benign lesions), with a median age of 54 years (36–87), and a median diameter of 9mm (4–17). All lesions were correctly identified and localized by IOUS, and free margins of excision were obtained in 75/77 cases (97%). Only two patients required a re-excision, one for multifocal disease and one for margins of excision of 1mm. In the remaining cases, the median distance from the tumor to the closest margins of excision, with exclusion of the posterior (fascial) and anterior (skin) margins, was 1.3cm (0.3–3.2). Conclusions: IOUS is a simple and accurate procedure that can be used to identify most non-palpable breast tumors, and has many advantages over the more commonly used wire-localization technique. [Copyright &y& Elsevier]

• Segmentation algorithms based on deep neural network achieve good results in medical images. • U-net used in medical imaging segmentation but the accuracy rate needs to be improved. • Propose Attention Enhanced U-net based on U-net to highlight salient features. • Apply three groups of HDC with expansion rates of [1,2,5] in AE U-net to reduce spatial information loss. Breast cancer poses a great threat on women health due to its high malignant rate. In China, ultrasound screening is the commonly-used method for breast cancer diagnosis, and the localization and segmentation of the lesions in ultrasound images are helpful for breast cancer detection. In this paper, an Attention Enhanced U-net with hybrid dilated convolution (AE U-net with HDC) model was proposed and employed to segment the breast tumors in ultrasound images. First, based on Attention U-net, we added a new loss function to update the weight matrix in the AGs module, in order to enhance the weight of the lesion area. Combined with fine-tuning training method, the precision of breast ultrasound image lesion region segmentation was improved from 82.38% to 86.28% and the M-IOU was improved from 76.27% to 81.81%. Second, three groups of HDC with expansion rates of [1,2,5] were integrated into AE U-net to replace the four convolution operations. HDC module brought larger receptive field and reduced the loss of spatial information. The experimental results proved that HDC module was helpful to improve the Acc of image segmentation results from 94.18% to 95.81% and the Recall from 78.69% to 80.48%. Combined with U-net, the F1 score, AUC, Acc and M-IOU of the network proposed in this paper had significantly improved. It proved that AE U-net with HDC model would have very important research value and application prospect for modern medicine. [ABSTRACT FROM AUTHOR]

Purpose In this study, we demonstrate a novel imaging technique, based on ultra-small-angle X-ray scattering (USAXS) that uses a Laue-case Si wafer as the angle analyzer. Methods: We utilized the (1 1 1) diffraction plane of a 356 μm thick, symmetrically cut Si wafer as the angle analyzer, denoted by A[L]. With this device, we performed USAXS imaging experiments using 19.8 keV synchrotron X-rays. The objects we imaged were formalin-fixed, paraffin-embedded breast tumors (an invasive carcinoma and an intraductal papilloma). During image acquisition by a charge-coupled device (CCD) camera, we varied the rotation angle of the analyzer in 0.02″ steps from −2.40″ to +2.40″ around the Bragg angle. The exposure time for each image was 2 s. We determined the amount of ultra-small-angle X-ray scattering from the width of the intensity curve obtained for each local pixel during the rotation of the analyzer. Results: We acquired USAXS images of malignant and benign breast tumor specimens using the A[L] analyzer; regions with larger USAXS form brighter areas in the image. We varied the sensitivity of the USAXS image by changing the threshold level of the object rocking curve. Conclusions: The USAXS images can provide information about the internal distribution of closely packed scattering bodies in a sample with reasonable sensitivity. This information differs from that obtainable through refraction-contrast imaging. Although further validation studies will be necessary, we conclude that USAXS imaging using a Laue-case analyzer may have significant potential as a new diagnosis technique. [ABSTRACT FROM AUTHOR]

Abstract: Background: The development of predictive biomarkers for IGF targeted anti-cancer therapeutics remains a critical unmet need. The insulin receptor A isoform (InsR-A) has been identified as a possible biomarker candidate but quantification of InsR-A in widely available formalin fixed paraffin embedded (FFPE) tissues is complicated by its similarities with the metabolic signaling insulin receptor isoform B (InsR-B). In the present study, qPCR based assays specific for InsR-A, InsR-B and IGF-1R were developed for use in FFPE tissues and tested for feasible use in clinical archived FFPE estrogen receptor (ER)+and ER− breast cancer tumors. Design: FFPE compatible primer sets were designed with amplicon sizes of less than 60 base pairs and validated for target specificity, assay repeatability and amplification efficiency. FFPE tumors from ER+ (n=83) and ER−(n=64) primary untreated breast cancers, and ER+ hormone refractory (HR ER+) (n=61) breast cancers were identified for feasibility testing. The feasible use of InsR-A and InsR-B qPCRs were tested using all tumor groups and the feasibility of IGF-1R qPCR was determined using HR ER+ tumors. Results: All qPCR assays were highly reproducible with amplification efficiencies between 96-104% over a 6 log range with limits of detection of 4 or 5 copies per reaction. Greater than 90% of samples were successfully amplified using InsR-A, InsR-B or IGF-1R qPCR primer sets and greater than 88% of samples tested amplified both InsR isoforms or both isoforms and IGF-1R. InsR-A was the predominant isoform in 82% ER+, 68% ER− and 100% HR ER+ breast cancer. Exploratory analyses demonstrated significantly more InsR-A expression in ER+ and HR ER+ groups compared to InsR-B (ER+ p<0.05, HR ER+ p<0.0005) and both groups had greater InsR-A expression when compared to ER− tumors (ER+ p<0.0005, HR ER+ p<0.05). IGF-1R expression of HR ER+ tumors was lower than InsR-A (p<0.0005) but higher than InsR-B (p<0.0005). The InsR-B expression of HR ER+ tumors was significantly reduced compared other tumor subgroups (ER+ and ER−, p<0.0005) and lead to a significant elevation of HR ER+ InsR-A: InsR-B ratios (ER+ and ER−, p<0.0005). Conclusions: The validated, highly sensitive InsR-A and InsR-B qPCR based assays presented here are the first to demonstrate the feasible amplification of InsR isoforms in FFPE tissues. Quantification data generated from this feasibility study indicating InsR-A is more predominant than InsR-B in breast cancer support the use of these assays for further investigation of InsR-A and InsR-B as predictive biomarkers for IGF targeted therapeutics. [Copyright &y& Elsevier]

Background: The staging and management of patients with breast cancer are significantly influenced by the pathological state of the axillary lymph nodes (ALN). Thus, it is very desirable to have a differential diagnosis of metastatic ALN. Purpose: The aim of this research is to assess the effectiveness of non-invasive ALN staging using PEM versus traditional breast mammography and ultrasound in patients presenting with early-stage breast cancer. This study focuses on determining if PEM can reliably stage ALNs and potentially eliminate the need for more invasive methods. Methods: In a study involving 94 breast cancer patients, PEM was used to assess ALNs. The nodes were visually evaluated for positive or negative uptake, and further categorized as non-specific, indeterminate, or malignant based on their maximum uptake values. This classification was then compared with histopathological results. Results: There were a total of 94 breast cancer patients; the results from PEM demonstrated an ALN detection sensitivity of 83.3%. The specificity of PEM in this context was found to be 98.08%, and the overall accuracy rate was 91.49%. Additionally, the Positive Predictive Value (PPV) was calculated to be 97.2%, and the Negative Predictive Value (NPV) was 87.9%. Conclusions: The initial study has shown encouraging outcomes in terms of accurate lymph node assessment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Medical Imaging & Radiation Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Konuralp Medical Journal / Konuralp Tip Dergisi is the property of Duzce University Medical School and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Mammography alone is an ineffective method for breast cancer surveillance and diagnosing cancer recurrence. The aim was to evaluate the ability of artificial intelligence (AI) to read digital mammograms as an additive tool to exclude recurrence in the operative bed of known breast cancer patients following the different surgical procedures. Methods: We used a retrospective cohort study of post-surgery mammograms (n = 577). Imaging was performed within 6 months after the surgery or more. The AI solution used to read mammograms (AI-MMG) provided a targeted heat map of the operative bed, which was supported by a decision likelihood score percentage of cancer recurrence. The reference for suspicious or malignant-looking abnormalities (n = 62, 12.3%) was diagnosed by biopsy. A clear operative bed and benign-looking changes (n = 442) were confirmed by ultrasound characterization patterns and one year of intermittent follow-up. Results: The AI scoring percentage for a clear operative bed ranged between 0 and 26%, with a mean of 15% ± 5.4%. Operative bed benign changes ranged from 10 to 88%, with a mean of 48.2% ± 21.2%, while malignancy recurrence ranged from 65 to 99%, with an average of 87.7% ± 10.5%. The "ROC: Receiver Operating Characteristic" curve for AI to predict cancer in the surgical bed on mammograms was 0.906. The optimum cutoff value to distinguish between benign postoperative alterations and malignancy recurrence was 56.5% (95%, CI 0.824–1.060, p value < 0.001). Excellent agreement between AI-MMG and pathology or ultrasound results was observed, and Kappa was 0.894, p value < 0.001. Conclusions: The use of artificial intelligence has enhanced the diagnostic performance of the postoperative mammograms to rule out recurrent malignancies in breast cancer surveillance. [ABSTRACT FROM AUTHOR]

Background: In estrogen receptor-positive metastatic breast cancer, ESR1 mutations (ESR1m) are a common mechanism of acquired resistance to aromatase inhibitors (ArIh). However, the impact ESR1 alterations have on CDK4/6 inhibitor (CDK4/6i) sensitivity has not been established. Analyses of CDK4/6i trials suggest that the endocrine therapy partner and specific ESR1 allele may affect susceptibility. We analyzed a real-world data set to investigate CDK4/6i efficacy in ESR1m metastatic breast cancer and associated clinical factors. Methods: ESR1m were identified by analysis of circulating-tumor deoxyribonucleic acid. The GuardantINFORM database contains genomic information from tumors linked with claims data. Patients who started a CDK4/6i within 30 days of sequencing were categorized as having ESR1m or non-ESR1-mutant (non-ESR1m) breast cancer. Data were analyzed to determine the real-world time-to-next-treatment, defined as the start of a breast cancer treatment to initiation of the subsequent treatment. Results: One hundred forty-five patients with ESR1m and 612 with non-ESR1m metastatic breast cancer were analyzed. ESR1m and non-ESR1m tumors had similar real-world time-to-next-treatment on CDK4/6i regimens (hazard ratio, 1.02; 95% confidence interval, 0.82 to 1.23). Duration on therapy in the first-line and second-line plus treatment settings were comparable regardless of ESR1 status. We stratified treatment duration by concurrent endocrine therapy, and patients with ESR1m had worse outcomes on ArIh but comparable real-world time-to-next-treatment on fulvestrant. Conclusions: These data suggest ESR1 variants are not associated with pan-CDK4/6i resistance and are consistent with the hypothesis that CDK4/6 blockade combined with a selective estrogen receptor degrader is potentially an effective option for ESR1m metastatic breast cancer. [ABSTRACT FROM AUTHOR]

It has been hypothesized based on accumulated data that a class of small noncoding RNAs, termed micro RNAs, are key factors in intercellular communication. Here, micro RNAs present in interstitial breast tumor fluids have been analyzed to identify relevant markers for a diagnosis of breast cancer and to elucidate the cross-talk that exists among cells in a tumor microenvironment. Matched tumor interstitial fluid samples ( TIF, n = 60), normal interstitial fluid samples ( NIF, n = 51), corresponding tumor tissue specimens ( n = 54), and serum samples ( n = 27) were collected from patients with breast cancer, and detectable micro RNAs were analyzed and compared. In addition, serum data from 32 patients with breast cancer and 22 healthy controls were obtained for a validation study. To identify potential serum biomarkers of breast cancer, first the micro RNA profiles of TIF and NIF samples were compared. A total of 266 micro RNAs were present at higher level in the TIF samples as compared to normal counterparts. Sixty-one of these micro RNAs were present in > 75% of the serum samples and were subsequently tested in a validation set. Seven of the 61 micro RNAs were associated with poor survival, while 23 were associated with the presence of immune cells and adipocytes. To our knowledge, these data demonstrate for the first time that profiling of micro RNAs in TIF can identify novel biomarkers for the prognostic classification and detection of breast cancer. In addition, the present findings demonstrate that micro RNAs may represent the cross-talk that occurs between tumor cells and their surrounding stroma. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Medical Imaging & Radiation Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Medical Imaging & Radiation Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aim: The aim of this study was to study intratumoral heterogeneity in the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) between two different cores/blocks of core needle biopsy (CNB), two different blocks of the same lesion in resection specimen (RS), and in between the CNB and RS. Materials and Methods: This is a prospective observational study of all the cases of breast carcinoma received in the department of histopathology for 2 years. All the patients with breast carcinoma diagnosed by CNB and RS were analyzed by three pathologists independently by immunohistochemistry. Results: Of the 75 cases, 74 were females and 1 male with a peak incidence of 60–70 years. Invasive breast carcinoma of no special type (97%) was the most common, followed by invasive lobular carcinoma (3%). Grade III tumors (44%) were more common, followed by Grade II (39%) and Grade I (17%). ER and PR showed a concordance rate of 96% (P < 0.05), whereas the HER2 showed a concordance rate of 93% (P < 0.05%). The concordance of ER decreased with increasing tumor size and grade, while PR and HER2 showed variable concordance with tumor size and grade. Conclusion: The concordance rate of ER and PR (96%) was higher than (93%) HER2. In most cases, biomarker expression in core biopsy and single tumor block was reflective of breast carcinoma as a whole. However, because of the heterogeneous expression of biomarkers, we recommend testing tumors with negative biomarker expression on CNB, again on the RS. [ABSTRACT FROM AUTHOR]

Background: Tumor survival, promotion, and metastatic functions are regulated by the tumor microenvironment (TME). The primo vascular system (PVS), the third circulatory system in animals, is currently thought to be a highly effective pathway for the spread of cancer cells. Objectives: In the present study, we intend to determine the TME effects on the PVS pattern in breast cancer for the first time. Methods: Heterotopic and orthotopic metastatic triple-negative breast cancer (TNBC) mice models were created. After 35 days, the skin was retracted, and a 2 cm skin incision was made up and down from the surface of the tumor tissue. In preparation for PVS staining, the dyes (trypan blue and alamarBlue) were injected throughout the tumor tissues. Under a stereomicroscope, PVS in heterotopic and orthotopic tumors was seen. Results: According to our data, there are no appreciable variations in PVS patterns and density between heterotopic and orthotopic animal models. Furthermore, alamarBlue is a good option for tumor PVS staining, as demonstrated by our research. Conclusion: For the first time, our data gave significant new information about the PVS in TNBC. Creating new anti-cancer treatments may be made possible by a better understanding of the biological characteristics of the TME and PVS. [ABSTRACT FROM AUTHOR]

Copyright of Nosileftiki is the property of Hellenic Nurses' Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Medical Imaging & Radiation Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)