Benign Breast Tumors Research Articles

Abstract 1293: Uncovered: The breast cancer journey of a Black woman from advocacy to awareness to action

Abstract Background: There have been 3 significant life events guiding my journey from being a young, Black mother with stage 4 breast cancer and thyroid cancer to an engaged patient partner. The first was having a benign breast tumor removed at age 14. The second was my 2017 mBC diagnosis, including my bilateral mastectomy and the lack of images and information available for Black women concerning reconstruction and scar management. The third is exclusion from and the lack of diversity in clinical trials. Objective: In order to address the disparities in cancer and my unmet needs, I use my story and experiences as a catalyst for change primarily in three forms, which have evolved over time: advocacy, awareness, and action. Methods: Advocacy: To unburden myself from the shame about my breast health and the scar from my surgery at age 14, I vocalized my concerns and needs as an adult navigating my breast reconstruction options after my 2017 cancer diagnosis. Ongoing frustration that my needs were not being addressed led me to create Uncovered: A Breast Recognition Project, in collaboration with Rethink Breast Cancer. Uncovered, a digital and print resource, shines a light on the disparities and challenges that Black, Indigenous and people of color face in breast cancer care. Awareness: The 2020 edition started with my story and those of 7 Black women. After Uncovered launched, other racialized women reached out with similar experiences. The resource was expanded in 2021 to include stories and images of an Inuk woman, Black women, and women of color. This confirmed a systemic need to raise awareness and address racialized communities and inequities in breast cancer care. Action: As a result of the slow progress towards systemic change, I, along with the women of Uncovered, co-created the Wish List in 2021. The Uncovered Wish List is 6 accessible and actionable items that should be a priority for ending disparities in breast cancer care. Due to the complexity of my diagnosis, which limits my clinical trial options, and the historical underrepresentation of racialized communities in clinical trials, I now partner with cancer institutions and organizations that focus on research to address ongoing unmet needs with the goal to improve equity, diversity and inclusion. Results: Uncovered is a first-of-its-kind resource in Canada. To date, it has had a global reach of over 70 million through conferences, panels, podcasts, resource links, presentations, and various media. The recognition of my story, Uncovered, and the Wish List allows me to form collaborative connections, and become an engaged patient partner committed to systemic change. Conclusion: Despite systemic barriers, it is possible for Black women with breast cancer to advocate, raise awareness and take action towards change in meaningful ways. Citation Format: Michelle Audoin. Uncovered: The breast cancer journey of a Black woman from advocacy to awareness to action [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1293.

Nomogram for preoperative differentiation of benign and malignant breast tumors using contrast-enhanced cone-beam breast CT (CE CB-BCT) quantitative imaging and assessment features.

Breast cancer's impact necessitates refined diagnostic approaches. This study develops a nomogram using radiology quantitative features from contrast-enhanced cone-beam breast CT for accurate preoperative classification of benign and malignant breast tumors. A retrospective study enrolled 234 females with breast tumors, split into training and test sets. Contrast-enhanced cone-beam breast CT-images were acquired using Koning Breast CT-1000. Quantitative assessment features were extracted via 3D-slicer software, identifying independent predictors. The nomogram was constructed to preoperative differentiation benign and malignant breast tumors. Calibration curve was used to assess whether the model showed favorable correspondence with pathological confirmation. Decision curve analysis confirmed the model's superiority. The study enrolled 234 female patients with a mean age of 50.2years (SD ± 9.2). The training set had 164 patients (89 benign, 75 malignant), and the test set had 70 patients (29 benign, 41 malignant). The nomogram achieved excellent predictive performance in distinguishing benign and malignant breast lesions with an AUC of 0.940 (95% CI 0.900-0.940) in the training set and 0.970 (95% CI 0.940-0.970) in the test set. This study illustrates the effectiveness of quantitative radiology features derived from contrast-enhanced cone-beam breast CT in distinguishing between benign and malignant breast tumors. Incorporating these features into a nomogram-based diagnostic model allows for breast tumor diagnoses that are objective and possess good accuracy. The application of these insights could substantially increase reliability and efficacy in the management of breast tumors, offering enhanced diagnostic capability.

Multi-task learning for segmentation and classification of breast tumors from ultrasound images

Segmentation and classification of breast tumors are critical components of breast ultrasound (BUS) computer-aided diagnosis (CAD), which significantly improves the diagnostic accuracy of breast cancer. However, the characteristics of tumor regions in BUS images, such as non-uniform intensity distributions, ambiguous or missing boundaries, and varying tumor shapes and sizes, pose significant challenges to automated segmentation and classification solutions. Many previous studies have proposed multi-task learning methods to jointly tackle tumor segmentation and classification by sharing the features extracted by the encoder. Unfortunately, this often introduces redundant or misleading information, which hinders effective feature exploitation and adversely affects performance. To address this issue, we present ACSNet, a novel multi-task learning network designed to optimize tumor segmentation and classification in BUS images. The segmentation network incorporates a novel gate unit to allow optimal transfer of valuable contextual information from the encoder to the decoder. In addition, we develop the Deformable Spatial Attention Module (DSAModule) to improve segmentation accuracy by overcoming the limitations of conventional convolution in dealing with morphological variations of tumors. In the classification branch, multi-scale feature extraction and channel attention mechanisms are integrated to discriminate between benign and malignant breast tumors. Experiments on two publicly available BUS datasets demonstrate that ACSNet not only outperforms mainstream multi-task learning methods for both breast tumor segmentation and classification tasks, but also achieves state-of-the-art results for BUS tumor segmentation. Code and models are available at https://github.com/qqhe-frank/BUS-segmentation-and-classification.git.

The Size Differences of Breast Cancer and Benign Tumors Measured by Two-Dimensional Ultrasound and Contrast-Enhanced Ultrasound.

Ultrasound (US) imaging has been observed to underestimate tumor size in clinical practice. This study aims to compare the size measurements of breast cancer and benign tumors using two-dimensional ultrasound (2DUS) and contrast-enhanced ultrasound (CEUS). The study included 42 clinically confirmed breast cancer and 47 benign breast tumors. Two experienced physicians independently measured the maximal longitudinal and transverse diameters of the masses in 2DUS and CEUS. All analyses were performed in R (4.2.2) and GraphPad Prism 6. The maximal longitudinal and transverse diameters of breast cancer measured by CEUS were 26.61 ± 0.21% and 26.24 ± 0.19% larger compared with 2DUS, and benign breast tumors had an 11.74 ± 0.21% and 11.06 ± 0.14% increase in size compared with 2DUS. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) for the difference between 2DUS and CEUS was 0.870 for longitudinal diameters (95% CI: 0.795-0.945, sensitivity 0.842, specificity 0.783, threshold value 0.215), and 0.863 for transverse diameters (95% CI: 0.785-0.942, sensitivity 0.667, specificity 0.936, threshold value 0.203). The size measurements of both breast cancer and benign tumors were larger in CEUS compared with 2DUS, with CEUS measurements of breast cancer being more pronounced than those of benign breast tumors. These findings suggest that CEUS may provide a more precise assessment of tumor size, which is crucial for determining optimal treatment strategies and improving patient outcomes in breast cancer management.

Waist-to-height ratio is a better predictor of hypertension in women during recovery from anesthesia compared to BMI, waist-to-hip ratio, and waist circumference

This was an observational study of patients with benign breast tumors intended to investigate and compare the predictive value of body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) for hypertension in the recovery room. Logistic regression analysis was used to determine the association between these body fat anthropometric indices and hypertension. Receiver operating characteristic curve (ROC) analysis was performed to assess the comparative predictive ability. A total of 689 women were evaluated. Patients with BMI ≥28 (kg/m2), WC > 85 cm, WHR ≥0.82, and WHtR ≥0.5 had a significantly higher probability of increased systolic blood pressure (SBP) and diastolic blood pressure (DBP) than patients with less than threshold values (all P < 0.05). The areas under the ROC curve (AUC) of BMI, WC, and WHtR where all modestly significant (all AUC ≥0.65) and nearly identical at 0.6592, 0.65, and 0.6724, respectively. Conclusion: body fat anthropometric indices are useful predicting hypertension during recovery from general anesthesia in patients with benign breast tumors undergoing day surgery; WHtR outperformed the other indices and nearly identical.

Constructing the Optimal Classification Model for Benign and Malignant Breast Tumors Based on Multifeature Analysis from Multimodal Images.

The purpose of this study was to fuse conventional radiomic and deep features from digital breast tomosynthesis craniocaudal projection (DBT-CC) and ultrasound (US) images to establish a multimodal benign-malignant classification model and evaluate its clinical value. Data were obtained from a total of 487 patients at three centers, each of whom underwent DBT-CC and US examinations. A total of 322 patients from dataset 1 were used to construct the model, while 165 patients from datasets 2 and 3 formed the prospective testing cohort. Two radiologists with 10-20years of work experience and three sonographers with 12-20years of work experience semiautomatically segmented the lesions using ITK-SNAP software while considering the surrounding tissue. For the experiments, we extracted conventional radiomic and deep features from tumors from DBT-CCs and US images using PyRadiomics and Inception-v3. Additionally, we extracted conventional radiomic features from four peritumoral layers around the tumors via DBT-CC and US images. Features were fused separately from the intratumoral and peritumoral regions. For the models, we tested the SVM, KNN, decision tree, RF, XGBoost, and LightGBM classifiers. Early fusion and late fusion (ensemble and stacking) strategies were employed for feature fusion. Using the SVM classifier, stacking fusion of deep features and three peritumoral radiomic features from tumors in DBT-CC and US images achieved the optimal performance, with an accuracy and AUC of 0.953 and 0.959 [CI: 0.886-0.996], a sensitivity and specificity of 0.952 [CI: 0.888-0.992] and 0.955 [0.868-0.985], and a precision of 0.976. The experimental results indicate that the fusion model of deep features and peritumoral radiomic features from tumors in DBT-CC and US images shows promise in differentiating benign and malignant breast tumors.

Preoperative evaluation of red blood cell distribution width as a promising biomarker for discriminating between benign and malignant breast tumors and assessing breast cancer activity.

The red cell distribution width (RDW) has been recognized as a new prognostic factor in various pathophysiological states including breast cancers. The aim of this study was to compare the RDW values between patients diagnosed with benign and malignant breast tumors and to evaluate the potential of elevated RDW as a biomarker for breast cancer activity. A cross-sectional study compared RDW values in age-matched cohorts diagnosed with benign and malignant breast lesions and correlated them with prognostic factors for malignant breast tumors. The study excluded individuals with inflammatory disorders, pre-neoplastic disease, hematological disorders, or those taking immunosuppressive or anti-inflammatory medications. The RDW values were measured using an automated hematology analyzer. The study's statistical analysis was conducted using SPSS software at a significance level of 0.05. RDW demonstrated a statistically significant elevation in the cohort of patients with malignant breast tumors, those with metastatic breast tumors, a greater number of infiltrated axillary lymph nodes, larger tumor diameter, and a higher grade of the primary tumor mass. The findings of this study suggest that RDW has the potential to serve as a biomarker for assessing breast cancer activity and could offer substantial advantages in clinical contexts. The present study is not without limitations, and the authors suggest that a larger multicenter prospective study be conducted to further validate it's efficacy over an extended duration.

Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications

Ultrasound information entropy is a flexible approach for analyzing ultrasound backscattering. Shannon entropy imaging based on probability distribution histograms (PDHs) has been implemented as a promising method for tissue characterization and diagnosis. However, the bin number affects the stability of entropy estimation. In this study, we introduced the k-nearest neighbor (KNN) algorithm to estimate entropy values and proposed ultrasound KNN entropy imaging. The proposed KNN estimator leveraged the Euclidean distance between data samples, rather than the histogram bins by conventional PDH estimators. We also proposed cumulative relative entropy (CRE) imaging to analyze time-series radiofrequency signals and applied it to monitor thermal lesions induced by microwave ablation (MWA). Computer simulation phantom experiments were conducted to validate and compare the performance of the proposed KNN entropy imaging, the conventional PDH entropy imaging, and Nakagami-m parametric imaging in detecting the variations of scatterer densities and visualizing inclusions. Clinical data of breast lesions were analyzed, and porcine liver MWA experiments ex vivo were conducted to validate the performance of KNN entropy imaging in classifying benign and malignant breast tumors and monitoring thermal lesions, respectively. Compared with PDH, the entropy estimation based on KNN was less affected by the tuning parameters. KNN entropy imaging was more sensitive to changes in scatterer densities and performed better visualizable capability than typical Shannon entropy (TSE) and Nakagami-m parametric imaging. Among different imaging methods, KNN-based Shannon entropy (KSE) imaging achieved the higher accuracy in classification of benign and malignant breast tumors and KNN-based CRE imaging had larger lesion-to-normal contrast when monitoring the ablated areas during MWA at different powers and treatment durations. Ultrasound KNN entropy imaging is a potential quantitative ultrasound approach for tissue characterization.

Bilateral gigantomastia due to benign breast tumors: a case series and brief review focusing on bilateral diffuse pseudoangiomatous stromal hyperplasia

Gigantomastia is a rare condition characterized by excessive hypertrophy of the connective tissue of the breast, which can cause physical and emotional distress. Surgical intervention is crucial for improving patients’ quality of life; however, it is challenging to balance minimizing the risk of recurrence and maximizing favorable aesthetic outcomes. This study documents the successful management of three rare cases of bilateral gigantomastia resulting from benign tumors. These cases included rapidly growing bilateral diffuse tumorous pseudoangiomatous stromal hyperplasia (PASH) and bilateral juvenile giant fibroadenoma, with no long-term recurrence observed. We discuss the diagnostic challenges and management considerations for gigantomastia, with a focus on reviewing PASH and its differential diagnosis. The findings offer valuable insights into the successful management of diverse gigantomastia cases caused by benign tumors, potentially aiding clinicians in making more informed decisions regarding optimal patient care.

An efficient approach for breast cancer classification using machine learning

Breast cancer, a life-threatening disease affecting millions worldwide, poses significant challenges due to its time-consuming manual determination process, potential risks, and human errors. It is a condition where cells of the breast develop unnaturally and uncontrollably, resulting in a mass called a tumor. If lumps in the breast are not addressed, they can spread to other regions of the body, including the bones, liver, and lungs. Early diagnosis is crucial for effective treatment and improved patient outcomes. In this research paper, we focus on employing machine learning models to achieve quick identification of breast cancer tumors as benign or malignant. The primary objective is to develop a decision-making visualization pattern using swarm plots and heat maps. To accomplish this, we utilized the Light GBM (Gradient Boosting Machine) algorithm and compared its performance against other established machine learning models, namely Logistic Regression, Gradient Boosting Algorithm, Random Forest Algorithm, and XG Boost Algorithm. Ultimately, our study demonstrates that the Light GBM Algorithm exhibits the highest accuracy of 96.98% in distinguishing between benign and malignant breast tumors.

Carney complex predisposes to breast cancer: prospective study of 50 women.

Carney complex (CNC) is a rare genetic syndrome, mostly due to germline loss-of-function pathogenic variants in PRKAR1A. Carney complex includes pigmented skin lesions, cardiac myxomas, primary pigmented nodular adrenocortical dysplasia, and various breast benign tumors. The present study was designed to describe the characteristics of breast lesions in CNC patients and their association with other manifestations of CNC and PRKAR1A genotype. A 3-year follow-up multicenter French prospective study of CNC patients included 50 women who were analyzed for CNC manifestations and particularly breast lesions, with breast imaging, genotyping, and hormonal settings. Among the 38 women with breast imaging, 14 (39%) had breast lesions, half of them bilateral. Ten women (26%) presented with benign lesions and six with breast carcinomas (16%): one had ductal carcinoma in situ at 54, and five had invasive cancer before 50 years old, whom one with contralateral breast cancer during follow-up. The occurrence of breast cancer was more frequent in women with PRKAR1A pathogenic variant odds ratio = 6.34 (1.63-17.91) than in general population of same age. The mean age at breast cancer diagnosis was 44.7 years old: 17 years younger than in the general population. Breast cancer patients had good prognosis factors. All breast carcinomas occurred in individuals with familial CNC and PRKAR1A pathogenic variants. Loss of heterozygosity at the PRKAR1A locus in the 2 invasive breast carcinomas analyzed suggested a driver role of this tumor suppressor gene. As CNC could predispose to breast carcinoma, an adequate screening strategy and follow-up should be discussed in affected women. ClinicalTrial.gov NCT00668291.

Breast Cancer: Mitochondria-Centered Metabolic Alterations in Tumor and Associated Adipose Tissue.

The close cooperation between breast cancer and cancer-associated adipose tissue (CAAT) shapes the malignant phenotype, but the role of mitochondrial metabolic reprogramming and obesity in breast cancer remains undecided, especially in premenopausal women. Here, we examined mitochondrial metabolic dynamics in paired biopsies of malignant versus benign breast tumor tissue and CAAT in normal-weight and overweight/obese premenopausal women. Lower protein level of pyruvate dehydrogenase and citrate synthase in malignant tumor tissue indicated decreased carbon flux from glucose into the Krebs cycle, whereas the trend was just the opposite in malignant CAAT. Simultaneously, stimulated lipolysis in CAAT of obese women was followed by upregulated β-oxidation, as well as fatty acid synthesis enzymes in both tumor tissue and CAAT of women with malignant tumors, corroborating their physical association. Further, protein level of electron transport chain complexes was generally increased in tumor tissue and CAAT from women with malignant tumors, respective to obesity. Preserved mitochondrial structure in malignant tumor tissue was also observed. However, mitochondrial DNA copy number and protein levels of PGC-1α were dependent on both malignancy and obesity in tumor tissue and CAAT. In conclusion, metabolic cooperation between breast cancer and CAAT in premenopausal women involves obesity-related, synchronized changes in mitochondrial metabolism.

Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours.

This study aimed to develop a deep learning radiomic model using multimodal imaging to differentiate benign and malignant breast tumours. Multimodality imaging data, including ultrasonography (US), mammography (MG), and magnetic resonance imaging (MRI), from 322 patients (112 with benign breast tumours and 210 with malignant breast tumours) with histopathologically confirmed breast tumours were retrospectively collected between December 2018 and May 2023. Based on multimodal imaging, the experiment was divided into three parts: traditional radiomics, deep learning radiomics, and feature fusion. We tested the performance of seven classifiers, namely, SVM, KNN, random forest, extra trees, XGBoost, LightGBM, and LR, on different feature models. Through feature fusion using ensemble and stacking strategies, we obtained the optimal classification model for benign and malignant breast tumours. In terms of traditional radiomics, the ensemble fusion strategy achieved the highest accuracy, AUC, and specificity, with values of 0.892, 0.942 [0.886-0.996], and 0.956 [0.873-1.000], respectively. The early fusion strategy with US, MG, and MRI achieved the highest sensitivity of 0.952 [0.887-1.000]. In terms of deep learning radiomics, the stacking fusion strategy achieved the highest accuracy, AUC, and sensitivity, with values of 0.937, 0.947 [0.887-1.000], and 1.000 [0.999-1.000], respectively. The early fusion strategies of US+MRI and US+MG achieved the highest specificity of 0.954 [0.867-1.000]. In terms of feature fusion, the ensemble and stacking approaches of the late fusion strategy achieved the highest accuracy of 0.968. In addition, stacking achieved the highest AUC and specificity, which were 0.997 [0.990-1.000] and 1.000 [0.999-1.000], respectively. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity of 1.000 [0.999-1.000] under the early fusion strategy. This study demonstrated the potential of integrating deep learning and radiomic features with multimodal images. As a single modality, MRI based on radiomic features achieved greater accuracy than US or MG. The US and MG models achieved higher accuracy with transfer learning than the single-mode or radiomic models. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity under the early fusion strategy, showed higher diagnostic performance, and provided more valuable information for differentiation between benign and malignant breast tumours.

Dysregulation of miR-122, miR-574 and miR-375 in Egyptian patients with breast cancer.

The early detection of breast cancer (BC) is receiving global attention, creating an urgent need for more sensitive and comprehensive strategies for preventive intervention, therapy assessment, and prognosis prediction. Aberrant expression of miRNAs has been observed in various malignancies and may be potential targets for therapy. Our study aims to examine the expression profiles of miR-375, miR-574-3p, and miR-122 in the sera of Egyptian women with BC, benign breast lesions, and a control group. We hope to determine if these miRNAs can serve as minimally invasive biomarkers for BC. This is a case-control study in which 77 patients with newly diagnosed BC, 20 patients with benign breast tumors, and 30 normal healthy subjects as controls were recruited from the outpatient clinic of the National Cancer Institute. The assessment of miRNAs was conducted using RT-PCR (Applied Biosystems). The expression level of miRNA-122 was significantly upregulated in the BC group, while the expression levels of miRNA-574 and miRNA-375 showed significant downregulation in BC patients. Serum miR-122 and miRNA-375 were able to distinguish breast cancer from the benign and control groups in ROC curve analysis, with AUCs of 0.786 and 0.796, respectively. Our results also showed that serum miR-122 and miR-574 are significant predictor variables in the multivariate analysis, after adjusting for age. Our findings suggest that miR-122 may act as an onco-microRNA, while miR-574 and miR-375 may have a main tumour suppressor role. The studied miRNAs may serve as minimally invasive biomarkers for cases of breast cancer and as promising potential therapeutic targets for breast cancer.

The role of VEGF levels in the differentiation between malignant and benign breast tumor

The role of VEGF levels in the differentiation between malignant and benign breast tumor

Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis.

Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes.

Potential added value of computed tomography radiomics to multimodal prediction models for benign and malignant breast tumors.

Early diagnosis is crucial to the treatment of breast cancer, but conventional imaging detection is challenging. Radiomics has the potential to improve early diagnostic efficacy in a noninvasive manner. This study examined whether integrating computed tomography (CT) radiomics information based on ultrasound (US) models can improve the efficacy of breast cancer prediction. We retrospectively analyzed 420 patients with pathologically confirmed benign or malignant breast tumors. Clinical data and examination images were collected, and the population was divided into training (n=294) and validation (n=126) groups at a ratio of 7:3. The region of interest (ROI) was manually segmented along the tumor boundary using MaZda software, and the features of each ROI was extracted. After dimension reduction and screening, the best features were retained. Subsequently, random forest (RF), support vector machines, and K-nearest neighbor classifiers were used to establish prediction models in an US and combined-methods group. Finally, 8 of the 379 features were retained in the US group. Random forest was found to be the best model, and the area under the curve (AUC) of the training and validation groups was 0.90 [95% confidence interval (CI): 0.852-0.942] and 0.85 (95% CI: 0.775-0.930), respectively. Meanwhile, 12 of the 750 features were retained in the combined group. In this regard, random forest proved to be the best model, and the AUC of the training and validation group was 0.95 (95% CI: 0.918-0.981) and 0.92 (95% CI: 0.866-0.969), respectively. The calibration curve showed a good fit of the model. The decision curve showed that the clinical net benefit of the combined group was far greater than that of any single examination, and the prediction model of the combined group exhibited a degree of practical clinical value. The combined model based on US and CT images has potential application value in the prognostic prediction of benign and malignant breast diseases.

Phenotypic Analysis of Circulating Myeloid Derived Suppressor Cells and Their Subpopulations in Egyptian Females with Breast Cancer: A Single-Centre Case-Control Study.

Myeloid-derived suppressor cells (MDSC) are immature myeloid cells with suppressive function that has been thoroughly documented in the setting of cancer. Our purpose was to evaluate levels of MDSC and their subsets in a cohort of Egyptian patients with breast cancer. Evaluation of peripheral blood total MDSC and its subset was done using multicolor flowcytometry in 30 malignant, 10 benign breast tumor patients and 10 healthy control females. BC patients had higher total MDSC levels compared to controls (p= 0.01) particularly the Monocytic MDSC (M-MDSC) and abnormal MDSC subsets (p = 0.001 and p <0.001, respectively). A tumor size > 2 cm exhibited significantly higher granulocytic MDSCs (G-MDSCs) compared to tumor size < 2 cm (p= 0.02) whereas abnormal MDSCs were significantly higher in patients with a tumor size < 2 cm (p = 0.037). MDSC and its subsets can be used as a prognostic marker of tumor size as well as a potential targets for treatment in breast cancer patients.

Značaj ultrasonografije u praćenju multiplih fibroadenoma dojke

Introduction: Regular ultrasonographic check-ups help in early detection and follow-up of different diseases. Ultrasonography (US) is of the utmost diagnostic importance for mammographically "dense breasts", where glandular tissue is predominant, while in so-called "fat breasts" it is less reliable. It is used to determine the solid or cystic nature of a lesion, often tissue characterization, and the Doppler technique could reveal vascularization characteristics of the mass. Fibroadenomas are the most common benign breast tumors in women of all ages until menopause, with the highest incidence between 15 and 25 years of age. The US detects distinctly circumscribed, rarely round or lobular, hypoechoic masses with smooth and flat contours, uniform echoes with posterior augmentation, and marginal echo fading. Case report: We presented a female patient aged 21, who had multiple lesions in both breasts (25 lesions in total). Ultrasonographic exams were performed multiple times, in the last 5 years, and magnetic resonance (NMR) was done twice. Histopathologic verification diagnosed fibroadenomas. The biggest masses were surgically removed four times in this patient. What are the next steps? Are surgical interventions needed again, and if not how should she be followed up in the future? Is the implant insertion eligible for the sake of esthetic correction? Conclusion: Ultrasonography is a very significant and important method of follow-up of breast lesions. Mammography and magnetic resonance are used as complementary methods. According to a radiologist's expert opinions on breast diseases, ultrasonographic exams every six months and NMR may indicate the need for surgical intervention, especially if new lesions are detected or existing lesions are growing fast.

Aesthetic Evaluation Post Fibroadenoma Excision with Periareolar Incision

Background: Fibroadenomas, benign breast tumors, often necessitate surgical excision for diagnostic confirmation and symptom relief. The periareolar incision technique has gained popularity due to its ability to provide access for tumor excision while minimizing visible scarring and preserving breast aesthetics. Objective: This study was done to evaluate the cosmetic results following fibroadenoma removal via periareolar incision. Study Design: Prospective, observational study. Settings: Department of Surgery - Liaquat University Hospital, Hyderabad Pakistan. Duration: Three years from 2017 to 2020. Methods: The study included women aged 18 years or older with a confirmed diagnosis of fibroadenoma through imaging and biopsy, who were scheduled for fibroadenoma excision using a periareolar incision technique. All patients underwent excision of the fibroadenoma using the periareolar incision method. Dissection was carefully performed to access the fibroadenoma while minimizing disruption to surrounding tissues. The fibroadenoma was excised with precision to minimize trauma to adjacent structures. Aesthetic assessment was conducted using a scoring system ranging from 1 to 4, with patients providing subjective ratings of their cosmetic outcomes. Data collection was done through a specially designed proforma. Results: The mean age of the patients was 29.76 years, with a standard deviation of 5.51 years. The majority of patients, comprising 45.10%, reported perceiving a good aesthetic outcome during their final follow-up, indicating almost complete satisfaction. Additionally, 43.70% of patients perceived the outcome as fair, expressing partial satisfaction. Conversely, 11.30% of patients perceived the outcome as poor, reflecting dissatisfaction. The p-values for both age group and residence are above 0.05, indicating that there is no statistically significant relationship between these factors and aesthetic condition. Conclusion: The use of periareolar incision for excising fibroadenomas shows promising potential for favorable outcomes. The aesthetic acceptance of the surgical results was generally perceived as good. Rare instances of unfavorable outcomes were associated with factors such as incision extension.