OBJECTIVE: In this study, we aimed to assess the new trends in characteristics, molecular subtypes, and imaging findings of breast cancer in very young women.
METHODS: We retrospectively reviewed the database of a primary breast cancer referral center in southern Iran in 342 cases of 30-year-old or younger women from 2001 to 2020. Pathologic data, including nuclear subtype and grade, tumor stage, presence of in situ cancer, imaging data including lesion type in mammogram and ultrasound, and treatment data were recorded. Descriptive statistics were applied. Differences between categorical values between groups were compared using Pearson\'s Chi-square test.
RESULTS: The mean age was 27.89 years. The tumor type was invasive ductal carcinoma in 82 % of cases. Fourteen patients (4.4 %) had only in situ cancer, and 170 patients had in situ components (49.7 %). Molecular subtypes were available in 278 patients, including 117 (42.1 %) Luminal A, 64 (23.0 %) Luminal B, 58 (20.9 %) triple negative, and 39 (14 %) HER2 Enriched. In those with mammograms available, 63 (30.1 %) had no findings, 53 (25.3 %) had mass, 27 (12.9 %) had asymmetry, whether focal or global, 21 (10 %) had microcalcifications solely, and 45 (21.5 %) had more than one finding. Microcalcifications were significantly more common in Luminal cancers than HER2 and triple-negative cancers (p = 0.041).
CONCLUSIONS: Our study shows the most common subtype to be Luminal A cancer, with 74 % of the tumors being larger than 2 cm at the time of diagnosis. Irregular masses with non-circumscribed margins were the most common imaging findings.

The lack of in vivo studies on the delivery of doxorubicin within human skin, especially the absence of data on the doxorubicin diffusion coefficient, has made understanding its transdermal delivery kinetics challenging. In this study, as a first step, governing equations and finite element methods were employed to reproduce Franz diffusion cell experiment in human cadaver skin. The application of this experiment representative model with a fitting method resulted in approximate values for the diffusivity of doxorubicin across various skin layers. The estimated values were used later to conduct a comprehensive examination of doxorubicin administration for breast tumor treatments. In a 2D axisymmetric model using Fick\'s Law and then a microneedles array 3D model, crucial parameters effects on delivery efficiency were examined, such as the microneedle tip diameter, tip-to-tip distance, and tumor depth. As highlighted by the findings of this study, these parameters have an impact on the effectiveness of doxorubicin delivery for treating breast tumors. The focus of this research is on the potential of numerical methods in biomedical engineering, which addresses the urgent need for data on doxorubicin diffusion in human skin and offers valuable insights into optimizing drug delivery strategies for enhanced therapeutic outcomes.

OBJECTIVE: This systematic review aimed to summarize evidence for the feasibility and acceptability of psychosocial interventions for body image among women diagnosed with breast cancer and the study methods used to evaluate the interventions in question.
METHODS: Articles were identified via MEDLINE, CINAHL, CENTRAL, PsychINFO, and EMBASE. Inclusion criteria were: (1) peer-reviewed publication in English from 2000 onward with accessible full-text, (2) reported data on the feasibility and/or acceptability of psychosocial interventions and/or study methods, (3) included at least one measure of body image or reported a body-related theme, and (4) sample comprised women diagnosed with breast cancer. All study designs were eligible. Two reviewers independently performed study selection, data extraction, and quality assessment.
RESULTS: Sixty-two articles were included. Participants and comparator groups varied as did interventions. Feasibility and acceptability of the interventions and study methods were inconsistently operationalized and reported across studies. Evidence of feasibility and acceptability was heterogeneous within and across studies, though mostly positive.
CONCLUSIONS: Published psychosocial interventions for body image and study methods are generally feasible and acceptable. Findings should be used to advance the development, implementation, and evaluation of interventions designed to improve outcomes (body image or otherwise) for women diagnosed with breast cancer.
BACKGROUND: This review was registered with the International Prospective Register of Systematic Reviews (PROSPERO; ID: CRD42021269062, 11 September 2021).

Objective: This article investigates the effect of varying breast tumor size on the fluence rate distribution within a breast model during the diffuse optical imaging procedure. Background: Early detection of breast cancer is of significant importance owing to its wide spread among women worldwide. Mastectomy surgery became very common due to the late detection of breast cancers by the conventional diagnostic methods such as X-ray mammography and magnetic resonance imaging. On the contrary, optical imaging techniques provide a safe and more sensitive methodology, which is suitable for the early detection criteria. Methods: The implementation was performed based on simulating multiple detectors placed on the outer surface of a human breast model to compute the optical fluence rate after probing the breast (normal and different tumor sizes) with laser irradiation. Different laser wavelengths ranging from the red to near-infrared rays spectral range were examined to determine the optimum fluence rate that shows the highest capability to differentiate between normal and cancerous breasts. A three-dimensional breast model was created using the COMSOL multiphysics package where the optical fluence rate was estimated based on the finite-element solution of the diffusion equation. Results: To evaluate the efficiency of the suggested technique for identifying cancers and discriminate them from normal breast at various wavelengths (600-1000 nm) and several tumor sizes. Conclusions: The obtained results reveal different fluence rate distributions in the breast with different radius tumors, especially at 600 nm due to the significant differences in the scattering coefficient between malignancies and healthy tissue.

Breast tumors are among the most common types of tumors that can affect both genders. It may spread to the whole breast without any symptoms. Therefore, the early detection and accurate diagnosis of breast tumors are significantly important. Current approaches for breast cancer screening such as positron emission tomography (PET) and magnetic resonance imaging (MRI) have some limitations of being time and money-consuming. In addition, mammography screening is not recommended for women under forty. Consequently, optical techniques have been introduced as safe and functional alternatives. Diffuse optical imaging is a non-invasive imaging technique that utilizes near-infrared light to examine biological tissues based on measuring the optical transmission and/or reflection at various locations on the tissue surface. In this paper, we propose a modified arrangement between the laser source and the detectors for distinguishing tumors from normal breast tissue. A three-dimensional model of the normal human breast with three types of tumors is developed using a COMSOL simulation software based on the finite element solution of Helmholtz equation to estimate optical fluence distribution. The breast model consists of four layers: skin, fat, glandular, and muscle, where the tumor is included in the glandular layer. Different wavelengths were used to determine the most proper wavelength for the discrimination between the normal tissue and tumor. The obtained results were verified using the receiver operating characteristic (ROC) method. The resultant fluence images show different features between normal breast and breast with tumor especially using 600-nm incident laser as demonstrated by the obtained ROC curves.

Introduction The frequency of breast cancer (BC) is increasing among Pakistani females. It has been estimated that one out every nine women is predicted to develop BC, which is the highest disease occurring rate in the Asian population. The study aimed to evaluate the factors responsible for delay in diagnosis of BC in Sindh, Pakistan. Methodology This study was conducted at the Medical Oncology Department of Jinnah Postgraduate Medical Center Karachi from December 2018 to June 2019. All women between 17 and 80 years diagnosed with BC who had treatment delay of more than six months were included in the study using a non-probability consecutive sampling technique. The face-to-face interviews were conducted by the researcher himself and all the data regarding demographics and factors related to treatment delay of BC was noted in a structured questionnaire. Data were analyzed using SPSS version 23 (IBM Corp., Armonk, NY). Results Appointment delay was significantly associated with a treatment-seeking delay in patients (p=0.03). Lack of awareness was another significant factor associated with treatment delay in BC patients. About 50 (70.4%) women who reported a lack of awareness sought treatment after 10 months of their first onset of symptoms (p=0.001). Cultural beliefs were a significant cause of treatment delay of 10-12 months in 71.8% of patients (p=0.021). Financial constraints significantly correlated with treatment delay (p=0.015). Conclusion The cultural beliefs, poor financial status, and lack of awareness are the significant factors for the treatment delay in BC patients. Promoting female health awareness can tackle many of these issues.

The automatic segmentation of breast tumors in ultrasound (BUS) has recently been addressed using convolutional neural networks (CNN). These CNN-based approaches generally modify a previously proposed CNN architecture or they design a new architecture using CNN ensembles. Although these methods have reported satisfactory results, the trained CNN architectures are often unavailable for reproducibility purposes. Moreover, these methods commonly learn from small BUS datasets with particular properties, which limits generalization in new cases. This paper evaluates four public CNN-based semantic segmentation models that were developed by the computer vision community, as follows: (1) Fully Convolutional Network (FCN) with AlexNet network, (2) U-Net network, (3) SegNet using VGG16 and VGG19 networks, and (4) DeepLabV3+ using ResNet18, ResNet50, MobileNet-V2, and Xception networks. By transfer learning, these CNNs are fine-tuned to segment BUS images in normal and tumoral pixels. The goal is to select a potential CNN-based segmentation model to be further used in computer-aided diagnosis (CAD) systems. The main significance of this study is the comparison of eight well-established CNN architectures using a more extensive BUS dataset than those used by approaches that are currently found in the literature. More than 3000 BUS images acquired from seven US machine models are used for training and validation. The F1-score (F1s) and the Intersection over Union (IoU) quantify the segmentation performance. The segmentation models based on SegNet and DeepLabV3+ obtain the best results with F1s>0.90 and IoU>0.81. In the case of U-Net, the segmentation performance is F1s=0.89 and IoU=0.80, whereas FCN-AlexNet attains the lowest results with F1s=0.84 and IoU=0.73. In particular, ResNet18 obtains F1s=0.905 and IoU=0.827 and requires less training time among SegNet and DeepLabV3+ networks. Hence, ResNet18 is a potential candidate for implementing fully automated end-to-end CAD systems. The CNN models generated in this study are available to researchers at https://github.com/wgomezf/CNN-BUS-segment, which attempts to impact the fair comparison with other CNN-based segmentation approaches for BUS images.

Background Human epidermal growth factor receptor 2-positive (HER2+) breast cancer has two distinct subtypes according to hormone receptor (HR) status. Survival, pattern of recurrence, and treatment response differ between HR-/HER2+ and HR+/HER2+ cancers. Purpose To investigate imaging and clinicopathologic features of HER2+ cancers and their correlation with HR expression. Material and Methods Between 2011 and 2013, 252 consecutive patients with 252 surgically confirmed HER2+ cancers (125 HR- and 127 HR+) were included. Two experienced breast radiologists blinded to the clinicopathologic findings reviewed the mammograms and magnetic resonance (MR) images using the BI-RADS lexicon. Tumor kinetic features were acquired by computer-aided detection (CAD). The imaging and clinicopathologic features of 125 HR-/HER2+ cancers were compared with those of 127 HR+/HER2+ cancers. Association between the HR status and each feature was assessed. Results Multiple logistic regression analysis showed that circumscribed mass margin (odds ratio [OR], 4.73; P < 0.001), associated non-mass enhancement (NME) on MR images (OR, 3.29; P = 0.001), high histologic grade (OR, 3.89; P = 0.002), high Ki-67 index (OR, 3.06; P = 0.003), and older age (OR, 2.43; P = 0.006) remained independent indicators associated with HR-/HER2+ cancers. Between the two HER2+ subtypes, there were no differences in mammographic imaging presentations and calcification features and MR kinetic features by a CAD. Conclusion HER2+ breast cancers have different MR imaging (MRI) phenotypes and clinicopathologic feature according to HR status. MRI features related to HR and HER2 status have the potential to be used for the diagnosis and treatment decisions in HER2+ breast cancer patients.

The aim of the study was to develop a scoring model incorporating the Breast Imaging Reporting and Data System (BI-RADS) and the contrast-enhanced ultrasound (CEUS) scoring system to differentiate between malignant and benign breast lesions. A total of 524 solid breast masses in 490 consecutive patients were evaluated with conventional US and CEUS in this prospective study. Each lesion was scored according to BI-RADS, CEUS, and CEUS-rerated BI-RADS. The diagnostic specificity, sensitivity and accuracy of BI-RADS were 77.9%, 88.9% and 84.0%, respectively, and the area under the receiver operating characteristic curve was 0.834. The corresponding values for rerated BI-RADS were 82.1%, 96.9%, 90.3% and 0.895. The area under the receiver operating characteristic curve of BI-RADS alone was significantly smaller than that of CEUS and the rerated BI-RADS (p = 0.008 compared with CEUS, p = 0.002 compared with rerated BI-RADS). This study indicates that rerating BI-RADS with the CEUS scoring system improves its diagnostic accuracy.

OBJECTIVE: We compared the contrast effect of three doses of DD-723 in subjects with breast tumors to determine the recommended dose. We then evaluated differential diagnosis results using plain ultrasonography, contrast-enhanced ultrasonography (plain + enhanced), and contrast-enhanced magnetic resonance imaging (MRI) compared to the pathological diagnosis.
METHODS: To evaluate the contrast effect, contrast-enhanced ultrasonic images were independently evaluated in a randomized sequence by three blinded reviewers trained in the evaluation method beforehand. Multiple evaluation results from the three reviewers were used to assess the overall contrast effect. The differential diagnosis was evaluated independently by three blinded reviewers using contrast-enhanced ultrasonic images and contrast-enhanced magnetic resonance images in a randomized sequence; reviewers were also blinded to subject characteristics. Multiple evaluation results from the three reviewers were used to assess the overall differential diagnosis.
RESULTS: The recommended dose of DD-723 is an intermediate dose of 0.12 μL MB/kg. Accuracy, sensitivity, and specificity were improved more in the differential diagnosis by contrast-enhanced ultrasonography than in plain ultrasonography. Accuracy and specificity were better and sensitivity similar compared to contrast-enhanced MRI.
CONCLUSIONS: An intermediate dose showed the highest efficacy in terms of overall contrast effect. Contrast-enhanced ultrasonography is safe and useful when used in differential diagnosis.