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"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence . This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate and compare performance of different artificial intelligence (AI) models in differentiating between benign and malignant breast tumors on diffusion-weighted imaging (DWI), including comparison with radiologist assessments. Materials and Methods In this retrospective study, patients with breast lesions underwent 3T breast MRI from May 2019 to March 2022. In addition to T1-weighted imaging, T2-weighted imaging, and contrast-enhanced imaging, DWI was acquired five b-values (0, 200, 800, 1000, and 1500 s/mm 2 ). DWI data split into training and tuning and test sets were used for the development and assessment of AI models, including a small 2D convolutional neural network (CNN), ResNet18, EfficientNet-B0, and a 3D CNN. Performance of the DWI-based models in differentiating between benign and malignant breast tumors was compared with that of radiologists assessing standard breast MRI, with diagnostic performance assessed using receiver operating characteristic analysis. The study also examined data augmentation effects (A: random elastic deformation, B: random affine transformation/random noise, and C: mixup) on model performance. Results A total of 334 breast lesions in 293 patients (mean age [SD], 56.5 [15.1] years; all female) were analyzed. 2D CNN models outperformed the 3D CNN on the test dataset (area under the receiver operating characteristic curve [AUC] with different data augmentation methods: 0.83-0.88 versus 0.75-0.76). There was no evidence of a difference in performance between the small 2D CNN with augmentations A and B (AUC 0.88) and the radiologists (AUC 0.86) on the test dataset ( P = .64). When comparing the small 2D CNN to radiologists, there was no evidence of a difference in specificity (81.4% versus 72.1%; P = .64) or sensitivity (85.9% versus 98.8%; P = .64). Conclusion AI models, particularly a small 2D CNN, showed good performance in differentiating between malignant and benign breast tumors using DWI, without needing manual segmentation. ©RSNA, 2024.

Background and Aim Breast cancer is a leading cause of cancer-related deaths among women, and ultrasound imaging is crucial for early detection. However, variability in interpretation can affect diagnosis. Therefore, this study compared the performance of artificial neural networks (ANNs) and support vector machines (SVMs) in classifying breast tumors using ultrasound images. Method This comparative study was conducted from June 1, 2023, to June 1, 2024, using a convenience sampling method. Data were gathered from the Center for Breast Diseases at Nanakali Hospital in Erbil, Kurdistan Region of Iraq, and a publicly available dataset from Kaggle. ANN and SVM models were then applied to classify the tumors. Statistical analysis was performed using R (R Foundation for Statistical Computing, Vienna, Austria) and IBM SPSS Statistics for Windows, Version 28.0 (Released 2021; IBM Corp., Armonk, New York, United States), with performance metrics such as accuracy, sensitivity, specificity, and Kappa coefficient calculated for both models. Results The ANN model achieved an accuracy of 87.78%, with a sensitivity of 86.67% and a specificity of 88.89%. The SVM model demonstrated an accuracy of 86.67%, with a higher specificity of 95.56% but a lower sensitivity of 77.78%. Both models showed substantial agreement between predicted and actual classifications, with Kappa coefficients of 75.56% for ANN and 73.33% for SVM. The mean, skewness, and area were identified as the most important variables for the ANN model, while solidity, circularity, and perimeter were the most critical features of the SVM model. Conclusions The results indicated that ANN had a marginally higher accuracy than SVM in classifying breast tumors. It is recommended to further optimize these models for clinical use, improve the integration of machine learning in medical imaging, and expand the dataset to enhance model generalizability and robustness., Competing Interests: Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Othman Abdullah et al.)

Background: Magnetic seed localization is a novel and reliable technique for perioperative localization of non-palpable breast cancers. However, due to susceptibility artifacts, magnetic seeds cannot be in situ during response monitoring of neoadjuvant chemotherapy with MRI. Contrast-enhanced mammography (CEM) could provide an alternative modality for response monitoring while magnetic seeds are in situ . This feasibility study aimed to investigate whether implanted magnetic seeds cause imaging artifacts in CEM examinations. Methods: A phantom experiment and patient studies were conducted to assess the presence of imaging artifacts caused by magnetic seeds on CEM. Chicken breast filet phantoms containing magnetic seeds were imaged using CEM and MRI. Next, twenty women with non-palpable breast tumors scheduled for breast-conserving surgery were included and received a magnetic marker seed preoperatively. Immediately after seed implantation, postprocedural images were taken using the CEM mode on our mammography units. All images were assessed by two experienced breast radiologists for the presence of artifacts. Descriptive statistics were used to present the study results. Results: The phantom experiment revealed no imaging artifacts on CEM, whereas significant artifacts were present on MRI. This allowed us to continue with the patient studies, in which no imaging artifacts associated with magnetic seeds were observed at all. Surgical outcomes demonstrated successful retrieval of all magnetic seeds and negative surgical margins in 19 out of 20 cases. Conclusion: To the best of our knowledge, this is the first study demonstrating that the combination of CEM and magnetic seeds is feasible and does not cause any significant imaging artifacts., Competing Interests: Competing Interests: The Pintuition Seed® magnetic marker seeds utilized in this study were provided free-of-charge by Sirius Medical B.V., Eindhoven, The Netherlands. The authors have declared that no other possibly competing interest exists., (© The author(s).)

Background: Phyllodes tumors (PTs) of the breast are uncommon fibroepithelial neoplasms that tend to recur locally and may have metastatic potential. Their pathogenesis is poorly understood. Hippo signaling pathway plays an essential role in organ size control, tumor suppression, tissue regeneration and stem cell self-renewal. Hippo signaling dysfunction has been implicated in cancer. Recent evidence suggests that there is cross-talk between the Hippo signaling key proteins YAP/TAZ and the epithelial-mesenchymal transition (EMT) master regulators Snail and ZEB. In this study we aimed to investigate the expression of Hippo signaling pathway components and EMT regulators in PTs, in relation to tumor grade., Methods: Expression of Hippo signaling effector proteins YAP, TAZ and their DNA binding partner TEAD was evaluated by immunohistochemistry in paraffin-embedded tissue specimens from 86 human phyllodes breast tumors (45 benign, 21 borderline, 20 malignant), in comparison with tumor grade and with the expression of EMT-related transcription factors ZEB and Snail., Results: Nuclear immunopositivity for YAP, TAZ and TEAD was detected in both stromal and epithelial cells in PTs and was significantly higher in high grade tumors. Interestingly, there was a significant correlation between the expression of YAP, TAZ, TEAD and the expression of ZEB and SNAIL., Conclusions: Our results originally implicate Hippo signaling pathway in PTs pathogenesis and suggest that an interaction between Hippo signaling key components and EMT regulators may promote the malignant features of PTs., Competing Interests: Declaration of Competing Interest We have no conflicts of interest to disclose. The authors received no specific funding for this work., (Copyright © 2024 Elsevier GmbH. All rights reserved.)

Loss of BRCA2 (breast cancer 2) is lethal for normal cells. Yet it remains poorly understood how, in BRCA2 mutation carriers, cells undergoing loss of heterozygosity overcome the lethality and undergo tissue-specific neoplastic transformation. Here, we identified mismatch repair gene mutL homolog 1 (MLH1) as a genetic interactor of BRCA2 whose overexpression supports the viability of Brca2-null cells. Mechanistically, we showed that MLH1 interacts with Flap endonuclease 1 (FEN1) and competes to process the RNA flaps of Okazaki fragments. Together, they restrained the DNA2 nuclease activity on the reversed forks of lagging strands, leading to replication fork (RF) stability in BRCA2-deficient cells. In these cells, MLH1 also attenuated R-loops, allowing the progression of stable RFs, which suppressed genomic instability and supported cell viability. We demonstrated the significance of their genetic interaction by the lethality of Brca2-mutant mice and inhibition of Brca2-deficient tumor growth in mice by Mlh1 loss. Furthermore, we described estrogen as inducing MLH1 expression through estrogen receptor a (ERa), which might explain why the majority of BRCA2 mutation carriers develop ER-positive breast cancer. Taken together, our findings reveal a role of MLH1 in relieving replicative stress and show how it may contribute to the establishment of BRCA2-deficient breast tumors. [ABSTRACT FROM AUTHOR]

The alarming liability of breast cancer, mainly among younger Saudi women, has been attributed to diverse factors requiring a manageable valuation. Therefore, this study aimed to assess the types of tumors and breast cancer presentation stages in Northern Saudi Arabia. In the present study, retrieved data regarding breast biopsies were received from the Department of Pathology at King Salman Hospital, Hail, Northern Saudi Arabia. Included data referring to breast cancer biopsies patients (including 131 females and two males) diagnosed during the period from November 2019 to November 2020. Out of the 133 patients, breast cancer was diagnosed in 49/133(36.8%) patients included 45/49(91.8%) invasive ductal carcinoma (IDC), 2/49(4.1%) invasive lobular carcinoma (ILC), and 2/49(4.1%) papillary carcinoma. The remaining 84 benign breast lesions included 40/84(47.6%) fibroadenoma, 11/84(13%) ductal hyperplasia, 9/84(10.7%) fibrocystic disease, 8/84(9.5%) adenosis, 4/84(4.8%) chronic mastitis, 3/84(3.6%) breast abscess & intraductal papilloma, 2/84(2.4%) duct ectasia & phylloides tumor, and 1/84(1.2%) lactating adenoma & lipoma. Breast cancer is highly prevalent in Northern Saudi Arabia with predominantly invasive ductal carcinoma. There is an increase in the incidence of younger patients with advanced stages of initial presentation. Fibroadenoma is the commonest benign breast lesion, followed by fibrocystic diseases in Northern Saudi Arabia.

Breast tumors adversely affect the holistic well-being of women. Histopathological images are a critical substantiation for doctors to diagnose breast tumor types. The structure of various types of tumor cells exhibits significant correlations, thereby posing challenges to the diagnosis using conventional methods. In this work, the enhanced EfficientNet was employed for the diagnosis of breast tumors, which enabled the network model to learn the features of the disease automatically and improve the accuracy of the diagnosis of breast tumor types. Firstly, the convolutional block attention module was used to extract effective features. Secondly, the group convolution and channel shuffle operations were introduced to improve the feature representation ability of the model. Thirdly, the Hard-Swish activation function was applied to improve the convergence speed of the model. Finally, Experiments showed that the improved EfficientNet network achieved 98.4% accuracy in eight classifications on the BreakHis dataset, which was expected to act a decision aid tool in breast tumor diagnostic research.

This study aimed to investigate the diagnostic value of the resistive index (RI) in Spectral Doppler ultrasound for determining breast malignancy, as there is currently no optimal cut-off point for the determination of breast malignancy. The B-mode and Doppler (including Color and Spectral) breast ultrasound examinations of 62 subjects were gathered. These included histopathological results of 31 cases with malignant tumors and 31 cases of benign tumors which served as controls. All examinations and measurements just before the core-needle biopsy of the mass were validated by expert radiologists. All patients were examined using a 7 to 15 MHz GE Logiq linear array transducer. Color Doppler and Spectral Doppler were set on PRF 700-1,000 Hz, maximal gain 85-90%, and wall filler 50-100 Hz. Histopathological results were used as the gold standard. The study's average RI value in benign breast tumors was 0.55 ± 0.16, and the average RI value in malignant breast tumors was 0.97 ± 0.10. According to the Spearman correlation test, the relationship coefficient R is 0.887 (P-value＜0.0001), showing a strong and statistically significant relationship with a positive correlation direction between the increasing RI value on a Spectral Doppler ultrasound examination and the increasing possibility of malignancy in breast tumors. An increase in the value of the RI indicates an increase in the risk of malignancy. Studies relating to this correlation might benefit from future prospective research with a longer follow-up on patients with Breast Imaging-Reporting and Data System (BI-RADS) lesion Category 3.

Conventional photosensitizers for tumor phototherapy are greatly limited by their reliance on a single‐cell death process, leading to insufficient therapeutic effects and restricted biomedical applications. To address these limitations, multifunctional fluorophores, BTA and BTB, are designed based on an electron donor‐π‐acceptor system. By adjusting the strength of the intramolecular charge transfer through varying electron‐donating moieties, their optical properties are tailored. BTA emits bright near‐infrared II (NIR‐II) fluorescence and exhibits typical aggregation‐induced emission (AIE) characteristics, a large Stokes shift (>250 nm), good photostability, satisfactory biocompatibility, and remarkable mitochondria‐targeting capabilities. Notably, it demonstrates potent photodynamic and photothermal properties. BTA as an effective photosensitizer and a photothermal agent, generates various cytotoxic Type I and II reactive oxygen species and photothermal energy to effectively destroy tumor cell mitochondria and suppress tumor growth. Importantly, the molecular mechanisms underlying combination phototherapy are elucidated, revealing that it induces synergistic cell apoptosis and pyroptosis. These results highlight the potential of multifunctional AIE materials with bright NIR‐II fluorescence and mitochondria‐targeting characteristics for the synergistic phototherapy of breast tumors, offering new insights for future therapeutic developments. [ABSTRACT FROM AUTHOR]

Studies have reported overexpression of NAT1 gene for xenobiotic metabolizing arylamine N -acetyltransferase type 1 in estrogen receptor positive breast tumors, and this association has been linked to patient chemoresistance and response to tamoxifen. We probed the expression of NAT1 , using quantitative reverse transcription PCR to screen clinically characterized breast cancer tissue cDNA arrays. Primers detecting all NAT1 alternative transcripts were used, and the protocol and results are reported according to consensus guidelines. The clinical information about 166 tumor samples screened is provided, including tumor stage, estrogen and progesterone receptor status and HER2 expression. NAT1 was found to be significantly ( P  < 0.001) upregulated in hormone receptor positive vs. negative tumors. No correlation was apparent between NAT1 and tumor stage or HER2 expression. Our findings demonstrate a strong correlation between the expression of NAT1 and steroid hormone receptors in breast tumors, supporting its possible utility as a pharmacogenetic biomarker or drug target. Of the two polymorphic NAT genes, NAT1 is the one primarily expressed in breast tissue, and is subjected to regulation by two differential promoters and more than one polyadenylation signal. Hormonal factors may enhance NAT1 gene expression at the transcriptional or epigenetic level, and tamoxifen has additionally been shown to inhibit NAT1 enzymatic activity. The outcome of tamoxifen treatment is also more favorable in patients with NAT1 overexpressing tumors. The study adds to the growing body of evidence implicating NAT1 in breast cancer and its pharmacological treatment., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

We have developed an imaging biomarker for quantitatively monitoring the response to clinical treatment in cancer patients. Similar to other diffusion-weighted imaging DWI techniques, our method allows for the monitoring of breast cancer progression based on the diffusion coefficient values in the affected area. Our technique has the advantage of using images from mammograms and mesoscopic multiparticle collision MPC simulation, making it more affordable and easier to implement compared to other DWI techniques, such as diffusion-weighted MRI. To create our simulation, we start with the region of interest from a mammogram where the lesion is located and build a flat simulation box with impenetrable cylindrical obstacles of varying diameters to represent the tissue's heterogeneity. The volume of each obstacle is based on the intensity of the mammogram pixels, and the diffusion coefficient is calculated by simulating the behavior of a point particle fluid inside the box using MPC. We tested our technique on two mammograms of a male patient with a moderately differentiated breast ductal carcinoma lesion, taken before and after the first cycle of four chemotherapy sessions. As seen in other DWI studies, our technique demonstrated significant changes in the fluid concentration map of the tumor lesion, and the relative values of the diffusion coefficient showed a clear difference before and after chemotherapy. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) is the most aggressive molecular subtype, with a poor survival rate compared to others subtypes. For a long time, chemotherapy was the only systemic treatment for TNBC, and the identification of actionable molecular targets might ultimately improve the prognosis for TNBC patients. We performed a genome-wide analysis of DNA methylation at CpG islands on a collection of one hundred ten breast carcinoma samples and six normal breast tissue samples using reduced representation bisulfite sequencing with the XmaI restriction enzyme (XmaI-RRBS) and identified a subset of TNBC samples with significant hypomethylation at the LTB4R/LTB4R2 genes' CpG islands, including CpG dinucleotides covered with cg12853742 and cg21886367 HumanMethylation 450K microarray probes. Abnormal DNA hypomethylation of this region in TNBC compared to normal samples was confirmed by bisulfite Sanger sequencing. Gene expression generally anticorrelates with promoter methylation, and thus, the promoter hypomethylation detected and confirmed in our study might be revealed as an indirect marker of high LTB4R/LTB4R2 expression using a simple methylation-sensitive PCR test. Analysis of RNA-seq expression and DNA methylation data from the TCGA dataset demonstrates that the expression of the LTB4R and LTB4R2 genes significantly negatively correlates with DNA methylation at both CpG sites cg12853742 (R = −0.4, p = 2.6 × 10−6; R = −0.21, p = 0.015) and cg21886367 (R = −0.45, p = 7.3 × 10−8; R = −0.24, p = 0.005), suggesting the upregulation of these genes in tumors with abnormal hypomethylation of their CpG island. Kaplan–Meier analysis using the TCGA-BRCA gene expression and clinical data revealed poorer overall survival for TNBC patients with an upregulated LTB4R. To this day, only the leukotriene inhibitor LY255283 has been tested on an MCF-7/DOX cell line, which is a luminal A breast cancer molecular subtype. Other studies compare the effects of Montelukast and Zafirlukast (inhibitors of the cysteinyl leukotriene receptor, which is different from LTB4R/LTB4R2) on the MDA-MB-231 (TNBC) cell line, with high methylation and low expression levels of LTB4R. In our study, we assess the therapeutic effects of various drugs (including leukotriene receptor inhibitors) with the DepMap gene effect and drug sensitivity data for TNBC cell lines with hypomethylated and upregulated LTB4R/LTB4R2 genes. LY255283, Minocycline, Silibinin, Piceatannol, Mitiglinide, 1-Azakenpaullone, Carbetocin, and Pim-1-inhibitor-2 can be considered as candidates for the additional treatment of TNBC patients with tumors demonstrating LTB4R/LTB4R2 hypomethylation/upregulation. Finally, our results suggest that the epigenetic status of leukotriene B4 receptors is a novel, potential, predictive, and prognostic biomarker for TNBC. These findings might improve individualized therapy for TNBC patients by introducing new therapeutic adjuncts as anticancer agents. [ABSTRACT FROM AUTHOR]

Background: Social behaviors such as altruism, where one self-sacrifices for collective benefits, critically influence an organism's survival and responses to the environment. Such behaviors are widely exemplified in nature but have been underexplored in cancer cells which are conventionally seen as selfish competitive players. This multidisciplinary study explores altruism and its mechanism in breast cancer cells and its contribution to chemoresistance. Methods: MicroRNA profiling was performed on circulating tumor cells collected from the blood of treated breast cancer patients. Cancer cell lines ectopically expressing candidate miRNA were used in co-culture experiments and treated with docetaxel. Ecological parameters like relative survival and relative fitness were assessed using flow cytometry. Functional studies and characterization performed in vitro and in vivo include proliferation, iTRAQ-mass spectrometry, RNA sequencing, inhibition by small molecules and antibodies, siRNA knockdown, CRISPR/dCas9 inhibition and fluorescence imaging of promoter reporter-expressing cells. Mathematical modeling based on evolutionary game theory was performed to simulate spatial organization of cancer cells. Results: Opposing cancer processes underlie altruism: an oncogenic process involving secretion of IGFBP2 and CCL28 by the altruists to induce survival benefits in neighboring cells under taxane exposure, and a self-sacrificial tumor suppressive process impeding proliferation of altruists via cell cycle arrest. Both processes are regulated concurrently in the altruists by miR-125b, via differential NF-κB signaling specifically through IKKβ. Altruistic cells persist in the tumor despite their self-sacrifice, as they can regenerate epigenetically from non-altruists via a KLF2/PCAF-mediated mechanism. The altruists maintain a sparse spatial organization by inhibiting surrounding cells from adopting the altruistic fate via a lateral inhibition mechanism involving a GAB1-PI3K-AKT-miR-125b signaling circuit. Conclusions: Our data reveal molecular mechanisms underlying manifestation, persistence and spatial spread of cancer cell altruism. A minor population behave altruistically at a cost to itself producing a collective benefit for the tumor, suggesting tumors to be dynamic social systems governed by the same rules of cooperation in social organisms. Understanding cancer cell altruism may lead to more holistic models of tumor evolution and drug response, as well as therapeutic paradigms that account for social interactions. Cancer cells constitute tractable experimental models for fields beyond oncology, like evolutionary ecology and game theory. [ABSTRACT FROM AUTHOR]

The hypoxic tumor microenvironment (TME) significantly affects cancer treatment. Conventional chemotherapeutic agents cannot effectively target hypoxic tumor tissue, which decreases efficacy and results in severe toxic side effects. To alleviate this problem, a self‐driving biomotor is developed by functionalizing MCDP nanoparticles containing calcium peroxide and doxorubicin (DOX) loaded onto polydopamine‐coated metal–organic frameworks（MOF）, with the anaerobic Bifidobacterium infantis (Bif) for synergistic chemotherapy and chemodynamic therapy (CDT) against breast cancer. The materials of institute Lavoisier (MIL) frameworks + CaO2 + DOX + polydopamine (MCDP)@Bif biohybrid actively targets hypoxic regions of solid tumors via the inherent targeting ability of Bif. Once it has accumulated in the tumor tissue, MCDP generates hydroxyl radicals through the enhanced Fenton‐type reactions between Fe2+ and self‐generated hydrogen peroxide in the acidic TME. The disruption of Ca2+ homeostasis and resulting mitochondrial Ca2+ overload triggers apoptosis and enhances oxidative stress, promoting tumor cell death. The results found that the DOX concentration in MCDP@Bif‐treated tumors is 3.8 times higher than that in free‐DOX‐treated tumors, which significantly prolongs the median survival of the tumor‐bearing mice to 69 days and reduces the toxic side effects of DOX. Therefore, the novel bacteria‐driven drug delivery system is highly effective in achieving synergistic chemotherapy and CDT against solid tumors. [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]

Abstract Background Social behaviors such as altruism, where one self-sacrifices for collective benefits, critically influence an organism’s survival and responses to the environment. Such behaviors are widely exemplified in nature but have been underexplored in cancer cells which are conventionally seen as selfish competitive players. This multidisciplinary study explores altruism and its mechanism in breast cancer cells and its contribution to chemoresistance. Methods MicroRNA profiling was performed on circulating tumor cells collected from the blood of treated breast cancer patients. Cancer cell lines ectopically expressing candidate miRNA were used in co-culture experiments and treated with docetaxel. Ecological parameters like relative survival and relative fitness were assessed using flow cytometry. Functional studies and characterization performed in vitro and in vivo include proliferation, iTRAQ-mass spectrometry, RNA sequencing, inhibition by small molecules and antibodies, siRNA knockdown, CRISPR/dCas9 inhibition and fluorescence imaging of promoter reporter-expressing cells. Mathematical modeling based on evolutionary game theory was performed to simulate spatial organization of cancer cells. Results Opposing cancer processes underlie altruism: an oncogenic process involving secretion of IGFBP2 and CCL28 by the altruists to induce survival benefits in neighboring cells under taxane exposure, and a self-sacrificial tumor suppressive process impeding proliferation of altruists via cell cycle arrest. Both processes are regulated concurrently in the altruists by miR-125b, via differential NF-κB signaling specifically through IKKβ. Altruistic cells persist in the tumor despite their self-sacrifice, as they can regenerate epigenetically from non-altruists via a KLF2/PCAF-mediated mechanism. The altruists maintain a sparse spatial organization by inhibiting surrounding cells from adopting the altruistic fate via a lateral inhibition mechanism involving a GAB1-PI3K-AKT-miR-125b signaling circuit. Conclusions Our data reveal molecular mechanisms underlying manifestation, persistence and spatial spread of cancer cell altruism. A minor population behave altruistically at a cost to itself producing a collective benefit for the tumor, suggesting tumors to be dynamic social systems governed by the same rules of cooperation in social organisms. Understanding cancer cell altruism may lead to more holistic models of tumor evolution and drug response, as well as therapeutic paradigms that account for social interactions. Cancer cells constitute tractable experimental models for fields beyond oncology, like evolutionary ecology and game theory.

Background: Compared to conventional 2D mammography, digital breast tomosynthesis (DBT) offers greater breast lesion detection rates. Ring-like hypodense artifacts surrounding dense lesions are a common byproduct of DBT. This study’s purpose was to assess whether minuscule changes spanning this halo—termed the “broken halo sign”—could improve lesion classification. Methods: This retrospective study was approved by the local ethics review board. After screening 288 consecutive patients, DBT studies of 191 female participants referred for routine mammography with a subsequent histologically verified finding of the breast were assessed. Examined variables included patient age, histological diagnosis, architectural distortion, maximum size, maximum halo depth, conspicuous margins, irregular shape and broken halo sign. Results: While a higher halo strength was indicative of malignancy in general (p = 0.031), the broken halo sign was strongly associated with malignancy (p < 0.0001, odds ratio (OR) 6.33), alongside architectural distortion (p = 0.012, OR 3.49) and a diffuse margin (p = 0.006, OR 5.49). This was especially true for denser breasts (ACR C/D), where the broken halo sign was the only factor predicting malignancy (p = 0.03, 5.22 OR). Conclusion: DBT-associated halo artifacts warrant thorough investigation in newly found breast lesions as they are associated with malignant tumors. The “broken halo sign”—the presence of small lines of variable diameter spanning the peritumoral areas of hypodensity—is a strong indicator of malignancy, especially in dense breasts, where architectural distortion may be obfuscated due to the surrounding tissue.

Background: Considering the high prevalence of breast masses and the importance of screening and follow-up for malignancies in women, and since age and hormonal changes during menopause play a role in determining the type of pathology and prognosis of the masses, the purpose of this study is to determine the relationship between menopause and mass pathology. Methods: This was a cross-sectional-analytical study that was conducted in order to investigate the types of pathology of breast masses and their relationship with the age of menopause in women referred to Shahid Beheshti Hospital in Qom between April 2013 and April 2019. The inclusion criteria include: having sufficient file information and having a contact number to complete personal information, no history of cancer in other parts of the body and simultaneous malignancies, and the exclusion criteria also include: samples that were taken by cellular aspiration sampling, There were women who had a history of first-degree breast malignancy and were previously diagnosed with breast cancer and there was a possibility that the new mass under study was metastatic. Finally, the patients' information was extracted from the files and recorded in pre-prepared checklists. And the supplementary information of the incomplete files was asked and completed using the contact information of the patients. After collecting the information, using SPSS software version 26 and with chi-square and t-test statistical tests and considering the significance level of 0.05 the data was analyzed. Results: He prevalence of malignancy in postmenopausal women was higher than in premenopausal women. Also, a significant difference was found between tumor size, the presence or absence of metastasis, and menopause (P0.05). Conclusion: Breast malignancies (invasive ductal carcinoma, invasive mixed carcinoma, mucinous, medullary, and papillary carcinoma) were more common in postmenopausal women than non-menopausal women. In both groups, ductal dilatation and chronic inflammation were the most benign findings, and fibroadenoma was found at a much lower rate in menopausal women than in non-menopausal.

Breast cancer is the most common malignancy among women, and early diagnosis and targeted therapy have garnered significant attention. Non-coding RNAs have emerged as potential diagnostic, prognostic, and treatment biomarkers for breast cancer. This study aimed to evaluate the expression of non-coding RNAs, specifically miR-506 and circular RNA 000284, and their target gene SNAIL-2 in breast tumors compared to normal controls. The study also focused on clinicopathological characteristics, and plasma was monitored for expression of circ0000284 to identify a possible accessible cancer-related marker. Using the SYBR-Green Real-time PCR technique, total RNA was extracted from 80 breast tumors and normal adjacent tissues, and circ0000284, miR-506, and SNAIL2 expression were analyzed. The results showed overexpression, down-regulation, and up-regulation of circRNA 000284, miR-506, and SNAIL-2 gene, respectively. These expression changes were associated with advanced stages of the disease and lymph nodal involvement, which are signs of a poor prognosis. Additionally, a positive direct correlation was observed between circRNA000284 expression in tumors and plasma. Moreover, it was discovered that circ-0000284 sponged miR-506, causing up-regulation of SNAIL-2 as its mRNA target. The upregulation of SNAIL-2 as an epithelial-mesenchymal-transition (EMT)factor leads to poor prognosis in breast cancer and is epigenetically regulated by miR-506 and circRNA 000284. Therefore, the overexpression of circRNA000284 in plasma could be considered an indicator of lymph nodal involvement and advanced stages of cancer, and nominated as a poor prognostic biomarker for future considerations.

Breast radiologists encounter unusual lesions, which may not be well described in the literature. Previously based on histologic and molecular classifications, the World Health Organization (WHO) classification of tumors has become increasingly multidisciplinary. Familiarity with imaging features and basic pathology of infrequent breast lesions, as well as their current classification according to the WHO, may help the radiologist evaluate biopsy results for concordance and help direct the management of uncommon breast lesions. This review article provides a case-based review of imaging features and WHO histologic classification of rare breast tumors., (© Society of Breast Imaging 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Breast cancer is one of the most common malignant tumors in women worldwide and poses a severe threat to their health. Therefore, this study examined patients who underwent breast cancer surgery, analyzed hospitalization costs and structure, and explored the impact of China Healthcare Security Diagnosis Related Groups (CHS-DRG) management on patient costs. It aimed to provide medical institutions with ways to reduce costs, optimize cost structures, reduce patient burden, and improve service efficiency., Aim: To study the CHS-DRG payment system's impact on breast cancer surgery costs., Methods: Using the CHS-DRG (version 1.1) grouping criteria, 4073 patients, who underwent the radical resection of breast malignant tumors from January to December 2023, were included in the JA29 group; 1028 patients were part of the CHS-DRG payment system, unlike the rest. Through an independent sample t -test, the length of hospital stay as well as total hospitalization, medicine and consumables, medical, nursing, medical technology, and management expenses were compared. Pearson's correlation coefficient was used to test the cost correlation., Results: In terms of hospitalization expenses, patients in the CHS-DRG payment group had lower medical, nursing, and management expenses than those in the diagnosis-related group (DRG) non-payment group. For patients in the DRG payment group, the factors affecting the total hospitalization cost, in descending order of relevance, were medicine and consumable costs, consumable costs, medicine costs, medical costs, medical technology costs, management costs, nursing costs, and length of hospital stay. For patients in the DRG non-payment group, the factors affecting the total hospitalization expenses in descending order of relevance were medicines and consumable expenses, consumable expenses, medical technology expenses, the cost of medicines, medical expenses, nursing expenses, length of hospital stay, and management expenses., Conclusion: The CHS-DRG system can help control and reduce unnecessary medical expenses by controlling medicine costs, medical consumable costs, and the length of hospital stay while ensuring medical safety., Competing Interests: Conflict-of-interest statement: The authors report that they have no relevant conflicts of interest to disclose., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)

Breast cancer is one of the most common types of cancer among Iraqi women. MRI has been used in the detection of breast tumors for its efficient performance in the diagnosis process providing high accuracy. In this paper, breast MRI image data from 89 patients were classified using GLCM and CNN feature extraction methods. Four models were evaluated consisting of GLCM, CNN, combined GLCM and CNN features based models. The statistical ANOVA feature selection method was used to reduce the redundant features. The reduced feature subset was fed to CNN classifier for obtaining either normal or abnormal breast images. The proposed method was assessed in terms of accuracy, precision, recall and F1-score. The model provided 100% classification accuracy. [ABSTRACT FROM AUTHOR]

ObjectiveTo examine amide proton transfer-weighted (APTw) combined with diffusion weighed (DWI) and dynamic contrast enhanced (DCE) MRI for early prediction of pathological response to neoadjuvant chemotherapy in invasive breast cancer.MaterialsIn this prospective study, 50 female breast cancer patients (49.58 ± 10.62 years old) administered neoadjuvant chemotherapy (NAC) were enrolled with MRI carried out both before NAC (T0) and at the end of the second cycle of NAC (T1). The patients were divided into 2 groups based on tumor response according to the Miller-Payne Grading (MPG) system. Group 1 included patients with a greater degree of decrease in major histologic responder (MHR, Miller-Payne G4-5), while group 2 included non-MHR cases (Miller-Payne G1-3). Traditional imaging protocols (T1 weighted, T2 weighted, diffusion weighted, and DCE-MRI) and APTw imaging were scanned for each subject before and after treatment. APTw value (APTw0 and APTw1), Dmax (maximum diameter, Dmax0 and Dmax1), V (3D tumor volume, V0 and V1), and ADC (apparent diffusion coefficient, ADC0 and ADC1) before and after treatment, as well as changes between the two times points (ΔAPT, ΔDmax, ΔV, ΔADC) for breast tumors were compared between the two groups.ResultsAPT0 and APT1 values significantly differed between the two groups (p = 0.034 and 0.01). ΔAPTw values were significantly lower in non-MHR tumors compared with MHR tumors (p = 0.015). ΔDmax values were significantly higher in MHR tumors compared with non-MHR tumors (p = 0.005). ADC0 and ADC1 values were significantly higher in MHR tumors than in non-MHR tumors (p = 0.038 and 0.035). AUC (Dmax+DWI + APTw) = AUC (Dmax+APTw) > AUC (APTw) > AUC (Dmax+DWI) > AUC (Dmax).ConclusionAPTw imaging along with change of tumor size showed a significant potential in early prediction of MHR for NAC treatment in breast cancer, which might allow timely regimen refinement before definitive surgical treatment.

This paper presents a prototype of a portable and modular electrical impedance tomography (EIT) system for breast tumor detection. The proposed system uses MATLAB to generate three-dimensional representations of breast tissue. The modular architecture of the system allows for flexible customization and scalability. It consists of several interconnected modules. Each module can be easily replaced or upgraded, facilitating system maintenance and future enhancements. Testing of the prototype has shown promising results in preliminary screening based on experimental studies. Agar models were used for the experimental stage of this project. The 3D representations provide clinicians with valuable information for accurate diagnosis and treatment planning. Further research and refinement of the system is warranted to validate its performance in future clinical trials.