OBJECTIVE: Breast cancer is one of the leading cancer causes among women worldwide. It can be classified as invasive ductal carcinoma (IDC) or metastatic cancer. Early detection of breast cancer is challenging due to the lack of early warning signs. Generally, a mammogram is recommended by specialists for screening. Existing approaches are not accurate enough for real-time diagnostic applications and thus require better and smarter cancer diagnostic approaches. This study aims to develop a customized machine-learning framework that will give more accurate predictions for IDC and metastasis cancer classification.
METHODS: This work proposes a convolutional neural network (CNN) model for classifying IDC and metastatic breast cancer. The study utilized a large-scale dataset of microscopic histopathological images to automatically perceive a hierarchical manner of learning and understanding.
RESULTS: It is evident that using machine learning techniques significantly (15%-25%) boost the effectiveness of determining cancer vulnerability, malignancy, and demise. The results demonstrate an excellent performance ensuring an average of 95% accuracy in classifying metastatic cells against benign ones and 89% accuracy was obtained in terms of detecting IDC.
CONCLUSIONS: The results suggest that the proposed model improves classification accuracy. Therefore, it could be applied effectively in classifying IDC and metastatic cancer in comparison to other state-of-the-art models.

Ductal carcinoma in situ (DCIS) is a heterogeneous breast disease that remains challenging to treat due to its unpredictable progression to invasive breast cancer (IBC). Contemporary literature has become increasingly focused on extracellular matrix (ECM) alterations with breast cancer progression. However, the spatial regulation of the ECM proteome in DCIS has yet to be investigated in relation to IBC. We hypothesized that DCIS and IBC present distinct ECM proteomes that could discriminate between these pathologies. Tissue sections of pure DCIS, mixed DCIS-IBC, or pure IBC (n = 22) with detailed pathological annotations were investigated by multiplexed spatial proteomics. Across tissues, 1,005 ECM peptides were detected in pathologically annotated regions and their surrounding extracellular microenvironments. A comparison of DCIS to IBC pathologies demonstrated 43 significantly altered ECM peptides. Notably, eight fibrillar collagen peptides could distinguish with high specificity and sensitivity between DCIS and IBC. Lesion-targeted proteomic imaging revealed heterogeneity of the ECM proteome surrounding individual DCIS lesions. Multiplexed spatial proteomics reported an invasive cancer field effect, in which DCIS lesions in closer proximity to IBC shared a more similar ECM profile to IBC than distal counterparts. Defining the ECM proteomic microenvironment provides novel molecular insights relating to DCIS and IBC.

Bacopa monnieri (L) Wettst, commonly known as Brahmi, stands as a medicinal plant integral to India\'s traditional medical system, Ayurveda, where it is recognized as a \"medhya rasayana\"-a botanical entity believed to enhance intellect and mental clarity. Its significant role in numerous Ayurvedic formulations designed to address conditions such as anxiety, memory loss, impaired cognition, and diminished concentration underscores its prominence. Beyond its application in cognitive health, Brahmi has historically been employed in Ayurvedic practices for the treatment of inflammatory diseases, including arthritis. In contemporary biomedical research, Bacopa monnieri can attenuate the release of pro-inflammatory cytokines TNF-α and IL-6 in animal models. However, there remains a paucity of information regarding Bacopa\'s potential as an anticancer agent, warranting further investigation in this domain. Based on previous findings with Brahmi (Bacopa monnieri), the current study aims to find out the role of Brahmi plant preparation (BPP) in immunomodulatory actions on IDC. Employing a specific BPP concentration, we conducted a comprehensive study using MTT assay, ELISA, DNA methylation analysis, Western blotting, ChIP, and mRNA profiling to assess BPP\'s immunomodulatory properties. Our research finding showed the role of BPP in augmenting the action of T helper 1 (TH1) cells which secreted interferon-γ (IFN-γ) which in turn activated cytotoxic T-lymphocytes (CTL) to kill the cells of IDC (*p < 0.05). Moreover, we found out that treatment with BPP not only increased the activities of tumor-suppressor genes (p53 and BRCA1) but also decreased the activities of oncogenes (Notch1 and DNAPKcs) in IDC (*p < 0.05). BPP had an immense significance in controlling the epigenetic dysregulation in IDC through the downregulation of Histone demethylation & Histone deacetylation and upregulation of Histone methylation and Histone acetylation (*p < 0.05). Our Chromatin immunoprecipitation (ChIP)-qPCR data showed BPP treatment increased percentage enrichment of STAT1 & BRCA1 (*p < 0.05) and decreased percentage enrichment of STAT3, STAT5 & NF ΚB (*p < 0.05) on both TBX21 and BRCA1 gene loci in IDC. In addition, BPP treatment reduced the hypermethylation of the BRCA1-associated-DNA, which is believed to be a major factor in IDC (*p < 0.05). BPP not only escalates the secretion of type 1 specific cytokines but also escalates tumor suppression and harmonizes various epigenetic regulators and transcription factors associated with Signal Transducer and Activator of Transcription (STAT) to evoke tumor protective immunity in IDC.

UNASSIGNED: Medullary breast carcinoma (MBC) is a rare type of breast cancer. Our study aimed to compare the differences in clinical characteristics and prognosis between MBC and invasive ductal carcinoma (IDC), and to further develop and validate nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in MBC patients.
UNASSIGNED: A total of 179,613 patients from the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015, including 596 MBC patients, were analyzed using the Kaplan-Meier method and propensity score matching (PSM) to compare patients\' OS and CSS. Cox proportional hazard regression model was used to determine independent prognostic factors for OS and CSS in MBC patients. Nomograms were constructed based on Cox regression analysis whereas receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the predictive accuracy.
UNASSIGNED: There were significant differences in the clinical characteristics between MBC and IDC. According to the logrank test, MBC had better OS and CSS than IDC before and after PSM. Cox multivariate analysis showed that age, race, tumor size, lymph node (LN), and radiation therapy were independent prognostic factors for OS, whereas age, tumor size, American Joint Committee on Cancer (AJCC) stage, laterality, type of surgery, and chemotherapy were independent prognostic factors for CSS. Nomograms of OS and CSS were constructed based on independent prognostic factors.
UNASSIGNED: MBC had better OS and CSS than IDC. Nomograms based on clinicopathological features were sufficiently accurate in predicting the OS and CSS for MBC patients, which can effectively predict the survival risk of MBC patients and guide clinicians to provide more effective treatment measures.

UNASSIGNED: Sclerosing adenosis (SA) is a common proliferative benign lesion without atypia in the breast that may mimic invasive ductal carcinoma (IDC) on medical imaging, leading to it often being misdiagnosed and mistreated. Consequently, the purpose of this study was to assess the diagnostic value of multimodal ultrasound imaging in distinguishing SA from IDC.
UNASSIGNED: Multimodal ultrasound imaging, including automated breast volume scan (ABVS), elasticity imaging (EI), and color Doppler flow imaging (CDFI), were performed on 120 consecutive patients comprising 122 breast lesions (54 SA, 68 IDC). All lesions were pathologically confirmed. Multimodal ultrasound imaging features were compared between the two groups. Binary logistic regression analysis based on ABVS, EI, and CDFI was conducted to formulate a logistic regression equation for differentiating SA from IDC. The diagnostic performances of ABVS, EI, CDFI, and their combination were compared by the receiver operating characteristic (ROC) curve analysis.
UNASSIGNED: The sensitivity, specificity, and accuracy of ABVS, EI, CDFI, and their combination in differentiating SA from IDC were, respectively, 75.00%, 72.22%, and 73.77%; 86.76%, 72.22%, and 80.33%; 73.53%, 64.81%, and 69.67%; and 88.24%, 74.07%, and 81.97%. Combining multimodal ultrasound imaging yielded an area under the curve (AUC) of 0.895 (95% confidence interval: 0.827-0.943), which was higher than that of ABVS, EI, and CDFI, with AUC values of 0.736, 0.795, and 0.692, respectively, and the difference was statistically significant (ABVS vs. combined model, P<0.001; CDFI vs. combined model, P<0.001; EI vs. combined model, P<0.001). There was no significant difference in the diagnostic efficacy among the three imaging modalities (ABVS vs. EI, P=0.266; ABVS vs. CDFI, P=0.4671; EI vs. CDFI, P=0.051). Compared with those in IDC, the calcification (16.67% vs. 57.35%; P<0.001) and retraction phenomena in the coronal planes (18.52% vs. 57.35%; P<0.001) were less common in patients with SA, while circumscribed margin (38.89% vs. 5.88%; P<0.001), vascularity grade 0-I (64.81% vs. 26.47%; P<0.001), and elasticity scores 1-3 (72.22% vs. 13.24%; P<0.001) were more frequently found in patients with SA. Patients with SA were significantly younger than were patients with IDC (43±11 vs. 54±11 years; P<0.001), and the lesion size was smaller in patients with SA than in those with IDC (median size 1.0 cm; interquartile range (IQR), 0.9 cm vs. median size 1.3 cm; IQR, 1.3 cm; P<0.001).
UNASSIGNED: The preliminary results suggested that multimodal ultrasound imaging can improve the diagnostic accuracy of SA and provide additional information for differential diagnosis of SA and IDC.

UNASSIGNED: Breast cancer is one of the most frequently occurring malignant cancers worldwide. Invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) are the two most common histological subtypes of breast cancer. In this study, we aimed to deeply explore molecular characteristics and the relationship between IDC and ILC subtypes in luminal A subgroup of breast cancer using comprehensive proteomics and phosphoproteomics analysis.
UNASSIGNED: Cancer tissues and noncancerous adjacent tissues (NATs) with the luminal A subtype (ER- and PR-positive, HER2-negative) were obtained from paired IDC and ILC patients respectively. Label-free quantitative proteomics and phosphoproteomics methods were used to detect differential proteins and the phosphorylation status between 10 paired breast cancer and NATs. Then, the difference in protein expression and its phosphorylation between IDC and ILC subtypes were explored. Meanwhile, the activation of kinases and their substrates was also revealed by Kinase-Substrate Enrichment Analysis (KSEA).
UNASSIGNED: In the luminal A breast cancer, a total of 5,044 high-confidence proteins and 3,808 phosphoproteins were identified from 10 paired tissues. The protein phosphorylation level in ILC tissues was higher than that in IDC tissues. Histone H1.10 was significantly increased in IDC but decreased in ILC, Conversely, complement C4-B and Crk-like protein were significantly decreased in IDC but increased in ILC. Moreover, the increased protein expression of Septin-2, Septin-9, Heterogeneous nuclear ribonucleoprotein A1 and Kinectin but reduce of their phosphorylation could clearly distinguish IDC from ILC. In addition, IDC was primarily related to energy metabolism and MAPK pathway, while ILC was more closely involved in the AMPK and p53/p21 pathways. Furthermore, the kinomes in IDC were primarily significantly activated in the CMGC groups.
UNASSIGNED: Our research provides insights into the molecular characterization of IDC and ILC and contributes to discovering novel targets for further drug development and targeted treatment.

UNASSIGNED: Mucinous cystadenocarcinoma (MCA) mainly occurs in the ovary, pancreas, and appendix, whereas the breast is a rare primary site of occurrence. Invasive ductal carcinoma (IDC) is the most common breast malignancy. Only 31 cases of the breast MCA have been reported in the English literature, and the coexistence of MCA and IDC in the breast are rare.
UNASSIGNED: Here, we describe a 61-year-old postmenopausal woman with no family history of breast cancer or other breast-related diseases, who presented with a palpable mass in her left breast lasting for 2 months. On ultrasonography examination, the tumor was a cystic-solid lesion with clear boundary. Magnetic resonance imaging (MRI) showed a mass with low signal intensity on T1 weighted imaging and high signal intensity on T2 weighted imaging. Intraoperative frozen sections revealed metastatic tumor cells in one sentinel lymph node (1/4). She then underwent left modified radical mastectomy with axillary dissection. The post-operative pathological examination showed the tumor consisted mostly of MCA (60%), with a small proportion of intermediate-grade IDC. The MCA had a well-demarcated cystic structure with papillary projections and abundant mucoid material. The epithelium lining cystic spaces was tall columnar, with mucin-producing cells that had basally located nuclei. The degree of cytological atypia varied considerably. Axillary lymph nodes were normal (0/15). The MCA was triple-negative for estrogen receptor (ER), progesterone receptor (PR), and HER2, and positive for CK7 but negative for CK20. Through next-generation sequencing, no mutations in the BRCA1 and BRCA2 genes were identified in our case, which was not highlighted in prior cases. After surgery, the patient underwent eight cycles of chemotherapy, and she has been disease-free during the 10-month follow-up. In addition to detailing this instance of mixed MCA and IDC of the breast, we reviewed relevant literature and compare our findings with other patients who had breast MCAs.
UNASSIGNED: Our results improved the understanding of mixed MCA and IDC, especially MCA, and provided a basis for its diagnosis and differential diagnosis from other metastatic diseases.

UNASSIGNED: Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting.
UNASSIGNED: We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (µFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items.
UNASSIGNED: The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P<0.001); however, MKT, MKA, MKI, FA, and µFA were significantly higher (P<0.005). Among all the diffusion parameters, MKI was positively correlated with the tumor size on both MRI and pathological specimen (rs=0.38, P<0.05 vs. rs=0.54, P<0.01), whereas MKT had a positive correlation with the tumor size in the pathological specimen only (rs=0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MKT, MKA, and µFA compared to the metastasis negative group (P<0.05).
UNASSIGNED: Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing breast cancers with information on tissue microstructures. Particularly, µFA could be a potential imaging biomarker for evaluating breast cancers prior to surgery or chemotherapy.

We describe the case of a 65-year-old female with a history of left-sided ductal carcinoma in situ in 2008. Mammography in January 2020 demonstrated calcifications in the previously affected breast. Subsequent stereotactic biopsy results were benign. In the months that followed, the patient experienced breast changes but avoided returning to the facility as the COVID-19 pandemic worsened. In August of 2020, the patient returned for a repeat mammogram, which indicated 2 suspicious masses in the left breast. Further analysis through ultrasound-guided core biopsy ultimately led to a left mastectomy and lymph node biopsy, which were performed in September 2020. Pathology results revealed multifocal invasive ductal carcinoma stage IIB.

This study is aimed to assess the feasibility of AutoML technology for the identification of invasive ductal carcinoma (IDC) in whole slide images (WSI).
The study presents an experimental machine learning (ML) model based on Google Cloud AutoML Vision instead of a handcrafted neural network. A public dataset of 278,124 labeled histopathology images is used as the original dataset for the model creation. In order to balance the number of positive and negative IDC samples, this study also augments the original public dataset by rotating a large portion of positive image samples. As a result, a total number of 378,215 labeled images are applied.
A score of 91.6% average accuracy is achieved during the model evaluation as measured by the area under precision-recall curve (AuPRC). A subsequent test on a held-out test dataset (unseen by the model) yields a balanced accuracy of 84.6%. These results outperform the ones reported in the earlier studies. Similar performance is observed from a generalization test with new breast tissue samples we collected from the hospital.
The results obtained from this study demonstrate the maturity and feasibility of an AutoML approach for IDC identification. The study also shows the advantage of AutoML approach when combined at scale with cloud computing.