GR-334 Comparative Evaluation of EMBED Dataset for Mammogram Classification Using Deep Learning Techniques

Breast cancer is a global health concern for women. The detection of breast cancer in its early stages is crucial, and screening mammography serves as a vital leading-edge tool for achieving this goal. In this study, we evaluated the performance of centralized versions of Resnet 50v2 and Resnet 152v2 models for classification of mammograms using different datasets, which were divided by location number extracted from the EMBED dataset. The datasets were preprocessed and used various techniques to improve the performance of the models. The models are trained and evaluated using metrics such as accuracy, area under the curve (AUC), F1 score, precision, and recall. The results indicate good performance for both models, with the Resnet 152v2 model slightly outperforming the Resnet 50v2 model in terms of AUC score. Our findings demonstrate the potential of machine learning algorithms in breast cancer screening, with our model achieving an AUC score of 0.83

Performance Gaps of Artificial Intelligence Models Screening Mammography -- Towards Fair and Interpretable Models

Even though deep learning models for abnormality classification can perform well in screening mammography, the demographic and imaging characteristics associated with increased risk of failure for abnormality classification in screening mammograms remain unclear. This retrospective study used data from the Emory BrEast Imaging Dataset (EMBED) including mammograms from 115,931 patients imaged at Emory University Healthcare between 2013 to 2020. Clinical and imaging data includes Breast Imaging Reporting and Data System (BI-RADS) assessment, region of interest coordinates for abnormalities, imaging features, pathologic outcomes, and patient demographics. Deep learning models including InceptionV3, VGG16, ResNet50V2, and ResNet152V2 were developed to distinguish between patches of abnormal tissue and randomly selected patches of normal tissue from the screening mammograms. The distributions of the training, validation and test sets are 29,144 (55.6%) patches of 10,678 (54.2%) patients, 9,910 (18.9%) patches of 3,609 (18.3%) patients, and 13,390 (25.5%) patches of 5,404 (27.5%) patients. We assessed model performance overall and within subgroups defined by age, race, pathologic outcome, and imaging characteristics to evaluate reasons for misclassifications. On the test set, a ResNet152V2 model trained to classify normal versus abnormal tissue patches achieved an accuracy of 92.6% (95%CI=92.0-93.2%), and area under the receiver operative characteristics curve 0.975 (95%CI=0.972-0.978). Imaging characteristics associated with higher misclassifications of images include higher tissue densities (risk ratio [RR]=1.649; p=.010, BI-RADS density C and RR=2.026; p=.003, BI-RADS density D), and presence of architectural distortion (RR=1.026; p<.001). Small but statistically significant differences in performance were observed by age, race, pathologic outcome, and other imaging features (p<.001).Comment: 21 pages, 4 tables, 5 figures, 2 supplemental table and 1 supplemental figur

Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography

IntroductionTo date, most mammography-related AI models have been trained using either film or digital mammogram datasets with little overlap. We investigated whether or not combining film and digital mammography during training will help or hinder modern models designed for use on digital mammograms.MethodsTo this end, a total of six binary classifiers were trained for comparison. The first three classifiers were trained using images only from Emory Breast Imaging Dataset (EMBED) using ResNet50, ResNet101, and ResNet152 architectures. The next three classifiers were trained using images from EMBED, Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), and Digital Database for Screening Mammography (DDSM) datasets. All six models were tested only on digital mammograms from EMBED.ResultsThe results showed that performance degradation to the customized ResNet models was statistically significant overall when EMBED dataset was augmented with CBIS-DDSM/DDSM. While the performance degradation was observed in all racial subgroups, some races are subject to more severe performance drop as compared to other races.DiscussionThe degradation may potentially be due to ( 1) a mismatch in features between film-based and digital mammograms ( 2) a mismatch in pathologic and radiological information. In conclusion, use of both film and digital mammography during training may hinder modern models designed for breast cancer screening. Caution is required when combining film-based and digital mammograms or when utilizing pathologic and radiological information simultaneously

Alpha mangostin inhibits proliferation, migration, and invasion of human breast cancer cells via STAT3 inhibition

Background: Signal Transducer and Activator of Transcription 3 (STAT3) is an identified critical protein associated with the progression of cancer. Alpha mangostin (α-M), a powerful dietary xanthone found to have anti-cancer properties against various cancers. However, the precise mechanism of its anti-cancer activity is not fully understood. Therefore, the current work hypothesized that targeting STAT3 with α-M inhibits the migration, invasion, and proliferation of breast cancer cells. Firstly, we evaluated the binding affinity of α-M/STAT3 complex using molecular dynamic simulations (MDS) and further we determined the likely underlying mechanism of STAT3 through in-vitro experiments. α-M treatment affected the levels of STAT3 phosphorylation, hnRNP-A1, PKM2, and EMT markers. α-M stimulation in breast cancer cells also resulted in suppressed migratory and invasive behaviour. More importantly, the treatment also affected the Ki67 and BrdU positive cells. In summary, we found the anti-migratory and anti-proliferative actions of α-M in breast cancer cells via STAT3 inhibition. Also, the study significantly adds a new nutraceutical for therapeutic intervention of invasive breast cancer

Unraveling the Interplay of Known and Unknown Factors

© 2023 The Authors. Published by American Chemical Society.Cancer diagnoses have been increasing worldwide, and solid tumors are among the leading contributors to patient mortality, creating an enormous burden on the global healthcare system. Cancer is responsible for around 10.3 million deaths worldwide. Solid tumors are one of the most prevalent cancers observed in recent times. On the other hand, early diagnosis is a significant challenge that could save a person's life. Treatment with existing methods has pitfalls that limit the successful elimination of the disorder. Though nanoparticle-based imaging and therapeutics have shown a significant impact in healthcare, current methodologies for solid tumor treatment are insufficient. There are multiple complications associated with the diagnosis and management of solid tumors as well. Recently, surface-conjugated nanoparticles such as lipid nanoparticles, metallic nanoparticles, and quantum dots have shown positive results in solid tumor diagnostics and therapeutics in preclinical models. Other nanotheranostic material platforms such as plasmonic theranostics, magnetotheranostics, hybrid nanotheranostics, and graphene theranostics have also been explored. These nanoparticle theranostics ensure the appropriate targeting of tumors along with selective delivery of cargos (both imaging and therapeutic probes) without affecting the surrounding healthy tissues. Though they have multiple applications, nanoparticles still possess numerous limitations that need to be addressed in order to be fully utilized in the clinic. In this review, we outline the importance of materials and design strategies used to engineer nanoparticles in the treatment and diagnosis of solid tumors and how effectively each method overcomes the drawbacks of the current techniques. We also highlight the gaps in each material platform and how design considerations can address their limitations in future research directions.publishersversionpublishe

Advanced phytochemical-based nanocarrier systems for the treatment of breast cancer

As the world’s most prevalent cancer, breast cancer imposes a significant societal health burden and is among the leading causes of cancer death in women worldwide. Despite the notable improvements in survival in countries with early detection programs, combined with different modes of treatment to eradicate invasive disease, the current chemotherapy regimen faces significant challenges associated with chemotherapy-induced side effects and the development of drug resistance. Therefore, serious concerns regarding current chemotherapeutics are pressuring researchers to develop alternative therapeutics with better efficacy and safety. Due to their extremely biocompatible nature and efficient destruction of cancer cells via numerous mechanisms, phytochemicals have emerged as one of the attractive alternative therapies for chemotherapeutics to treat breast cancer. Additionally, phytofabricated nanocarriers, whether used alone or in conjunction with other loaded phytotherapeutics or chemotherapeutics, showed promising results in treating breast cancer. In the current review, we emphasize the anticancer activity of phytochemical-instigated nanocarriers and phytochemical-loaded nanocarriers against breast cancer both in vitro and in vivo. Since diverse mechanisms are implicated in the anticancer activity of phytochemicals, a strong emphasis is placed on the anticancer pathways underlying their action. Furthermore, we discuss the selective targeted delivery of phytofabricated nanocarriers to cancer cells and consider research gaps, recent developments, and the druggability of phytoceuticals. Combining phytochemical and chemotherapeutic agents with nanotechnology might have far-reaching impacts in the future

WNT-β Catenin Signaling as a Potential Therapeutic Target for Neurodegenerative Diseases: Current Status and Future Perspective

Wnt/β-catenin (WβC) signaling pathway is an important signaling pathway for the maintenance of cellular homeostasis from the embryonic developmental stages to adulthood. The canonical pathway of WβC signaling is essential for neurogenesis, cell proliferation, and neurogenesis, whereas the noncanonical pathway (WNT/Ca2+ and WNT/PCP) is responsible for cell polarity, calcium maintenance, and cell migration. Abnormal regulation of WβC signaling is involved in the pathogenesis of several neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and spinal muscular atrophy (SMA). Hence, the alteration of WβC signaling is considered a potential therapeutic target for the treatment of neurodegenerative disease. In the present review, we have used the bibliographical information from PubMed, Google Scholar, and Scopus to address the current prospects of WβC signaling role in the abovementioned neurodegenerative diseases