Role of p-glycoprotein expression in predicting response to neoadjuvant chemotherapy in breast cancer-a prospective clinical study

BACKGROUND: Neoadjuvant chemotherapy (NACT) is an integral part of multi-modality approach in the management of locally advanced breast cancer. It is vital to predict response to chemotherapy in order to tailor the regime for a particular patient. The prediction would help in avoiding the toxicity induced by an ineffective chemotherapeutic regime in a non-responder and would also help in the planning of an alternate regime. Development of resistance to chemotherapeutic agents is a major problem and one of the mechanisms considered responsible is the expression of 170-k Da membrane glycoprotein (usually referred to as p-170 or p-glycoprotein), which is encoded by multidrug resistance (MDR1) gene. This glycoprotein acts as an energy dependent pump, which actively extrudes certain families of chemotherapeutic agents from the cells. The expression of p-glycoprotein at initial presentation has been found to be associated with refractoriness to chemotherapy and a poor outcome. Against this background a prospective study was conducted using C219 mouse monoclonal antibody specific for p-glycoprotein to ascertain whether pretreatment detection of p-glycoprotein expression could be utilized as a reliable predictor of response to neoadjuvant chemotherapy in patients with breast cancer. PATIENTS AND METHODS: Fifty cases of locally advanced breast cancer were subjected to trucut(® )biopsy and the tissue samples were evaluated immunohistochemically for p-glycoprotein expression and ER, PR status. The response to neoadjuvant chemotherapy was assessed clinically and by using ultrasound after three cycles of FAC regime (cyclophosphamide 600 mg/m(2), Adriamycin 50 mg/m(2), 5-fluorourail 600 mg/m(2 )at an interval of three weeks). The clinical response was correlated with both the pre and post chemotherapy p-glycoprotein expression. Descriptive studies were performed with SPSS version 10. The significance of correlation between tumor response and p-glycoprotein expression was determined with chi square test. RESULTS: A significant relationship was found between the pretreatment p-glycoprotein expression and clinical response. The positive p-glycoprotein expression was associated with poor clinical response rates. When the clinical response was correlated with p-glycoprotein expression, a statistically significant negative correlation was observed between the clinical response and p- glycoprotein expression (p < 0.05). There was another significant observation in terms of development of post NACT p-glycoprotein positivity. Before initiation of NACT, 26 patients (52%) were p-glycoprotein positive and after three cycles of NACT, the positivity increased to 73.5% patients. CONCLUSION: The study concluded that pretreatment p-glycoprotein expression predicts and indicates a poor clinical response to NACT. Patients with positive p-glycoprotein expression before initiation of NACT were found to be poor responders. Thus pretreatment detection of p-glycoprotein expression may be utilized, as a reliable predictor of response to NACT in patients with breast cancer The chemotherapy induced p-glycoprotein positivity observed in the study could possibly explain the phenomenon of acquired chemoresistance and may also serve as an intermediate end point in evaluating drug response particularly if the adjuvant therapy is planned with the same regime

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer

Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen’s Kappa value. These indicators highlight the model’s proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information

Oral squamous cell carcinoma detection using EfficientNet on histopathological images

Introduction: Oral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accuracy and efficiency, highlighting the need for more reliable approaches. This study aims to explore the discriminative potential of histopathological images of oral epithelium and OSCC. By utilizing a database containing 1224 images from 230 patients, captured at varying magnifications and publicly available, a customized deep learning model based on EfficientNetB3 was developed. The model’s objective was to differentiate between normal epithelium and OSCC tissues by employing advanced techniques such as data augmentation, regularization, and optimization. Methods: The research utilized a histopathological imaging database for Oral Cancer analysis, incorporating 1224 images from 230 patients. These images, taken at various magnifications, formed the basis for training a specialized deep learning model built upon the EfficientNetB3 architecture. The model underwent training to distinguish between normal epithelium and OSCC tissues, employing sophisticated methodologies including data augmentation, regularization techniques, and optimization strategies. Results: The customized deep learning model achieved significant success, showcasing a remarkable 99% accuracy when tested on the dataset. This high accuracy underscores the model’s efficacy in effectively discerning between normal epithelium and OSCC tissues. Furthermore, the model exhibited impressive precision, recall, and F1-score metrics, reinforcing its potential as a robust diagnostic tool for OSCC. Discussion: This research demonstrates the promising potential of employing deep learning models to address the diagnostic challenges associated with OSCC. The model’s ability to achieve a 99% accuracy rate on the test dataset signifies a considerable leap forward in earlier and more accurate detection of OSCC. Leveraging advanced techniques in machine learning, such as data augmentation and optimization, has shown promising results in improving patient outcomes through timely and precise identification of OSCC

A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma

One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit from automated diagnostic techniques that analyze a patient’s histopathology images to identify abnormal oral lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely, normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed swarm intelligence technique called the Modified Gorilla Troops Optimizer. While there are many optimization algorithms used in the literature for feature selection, weight updating, and optimal parameter identification in deep learning models, this work focuses on using optimization algorithms as an intermediate layer to convert extracted features into features that are better suited for classification. Three datasets comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures, namely, InceptionV2, MobileNetV3, and EfficientNetB3, are investigated as feature extraction layers. Two fully connected Neural Network layers, batch normalization, and dropout are used as classification layers. With the best accuracy of 0.89 among the examined feature extraction models, MobileNetV3 exhibits good performance. This accuracy is increased to 0.95 when the suggested Modified Gorilla Troops Optimizer is used as an intermediary layer

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen–Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model’s decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies

Burden and risk factors for Pseudomonas aeruginosa community-acquired pneumonia:a Multinational Point Prevalence Study of Hospitalised Patients

Pseudornonas aeruginosa is a challenging bacterium to treat due to its intrinsic resistance to the antibiotics used most frequently in patients with community-acquired pneumonia (CAP). Data about the global burden and risk factors associated with P. aeruginosa-CAP are limited. We assessed the multinational burden and specific risk factors associated with P. aeruginosa-CAP. We enrolled 3193 patients in 54 countries with confirmed diagnosis of CAP who underwent microbiological testing at admission. Prevalence was calculated according to the identification of P. aeruginosa. Logistic regression analysis was used to identify risk factors for antibiotic-susceptible and antibiotic-resistant P. aeruginosa-CAP. The prevalence of P. aeruginosa and antibiotic-resistant P. aeruginosa-CAP was 4.2% and 2.0%, respectively. The rate of P. aeruginosa CAP in patients with prior infection/colonisation due to P. aeruginosa and at least one of the three independently associated chronic lung diseases (i.e. tracheostomy, bronchiectasis and/or very severe chronic obstructive pulmonary disease) was 67%. In contrast, the rate of P. aeruginosa-CAP was 2% in patients without prior P. aeruginosa infection/colonisation and none of the selected chronic lung diseases. The multinational prevalence of P. aeruginosa-CAP is low. The risk factors identified in this study may guide healthcare professionals in deciding empirical antibiotic coverage for CAP patients

Atypical pathogens in hospitalized patients with community-acquired pneumonia: A worldwide perspective

Background: Empirical antibiotic coverage for atypical pathogens in community-acquired pneumonia (CAP) has long been debated, mainly because of a lack of epidemiological data. We aimed to assess both testing for atypical pathogens and their prevalence in hospitalized patients with CAP worldwide, especially in relation with disease severity. Methods: A secondary analysis of the GLIMP database, an international, multicentre, point-prevalence study of adult patients admitted for CAP in 222 hospitals across 6 continents in 2015, was performed. The study evaluated frequency of testing for atypical pathogens, including L. pneumophila, M. pneumoniae, C. pneumoniae, and their prevalence. Risk factors for testing and prevalence for atypical pathogens were assessed through univariate analysis. Results: Among 3702 CAP patients 1250 (33.8%) underwent at least one test for atypical pathogens. Testing varies greatly among countries and its frequency was higher in Europe than elsewhere (46.0% vs. 12.7%, respectively, p &lt; 0.0001). Detection of L. pneumophila urinary antigen was the most common test performed worldwide (32.0%). Patients with severe CAP were less likely to be tested for both atypical pathogens considered together (30.5% vs. 35.0%, p = 0.009) and specifically for legionellosis (28.3% vs. 33.5%, p = 0.003) than the rest of the population. Similarly, L. pneumophila testing was lower in ICU patients. At least one atypical pathogen was isolated in 62 patients (4.7%), including M. pneumoniae (26/251 patients, 10.3%), L. pneumophila (30/1186 patients, 2.5%), and C. pneumoniae (8/228 patients, 3.5%). Patients with CAP due to atypical pathogens were significantly younger, showed less cardiovascular, renal, and metabolic comorbidities in comparison to adult patients hospitalized due to non-atypical pathogen CAP. Conclusions: Testing for atypical pathogens in patients admitted for CAP in poorly standardized in real life and does not mirror atypical prevalence in different settings. Further evidence on the impact of atypical pathogens, expecially in the low-income countries, is needed to guidelines implementation

Prevalence and etiology of community-acquired pneumonia in immunocompromised patients

Background. The correct management of immunocompromised patients with pneumonia is debated. We evaluated the prevalence, risk factors, and characteristics of immunocompromised patients coming from the community with pneumonia. Methods. We conducted a secondary analysis of an international, multicenter study enrolling adult patients coming from the community with pneumonia and hospitalized in 222 hospitals in 54 countries worldwide. Risk factors for immunocompromise included AIDS, aplastic anemia, asplenia, hematological cancer, chemotherapy, neutropenia, biological drug use, lung transplantation, chronic steroid use, and solid tumor. Results. At least 1 risk factor for immunocompromise was recorded in 18% of the 3702 patients enrolled. The prevalences of risk factors significantly differed across continents and countries, with chronic steroid use (45%), hematological cancer (25%), and chemotherapy (22%) the most common. Among immunocompromised patients, community-acquired pneumonia (CAP) pathogens were the most frequently identified, and prevalences did not differ from those in immunocompetent patients. Risk factors for immunocompromise were independently associated with neither Pseudomonas aeruginosa nor non\u2013community-acquired bacteria. Specific risk factors were independently associated with fungal infections (odds ratio for AIDS and hematological cancer, 15.10 and 4.65, respectively; both P = .001), mycobacterial infections (AIDS; P = .006), and viral infections other than influenza (hematological cancer, 5.49; P < .001). Conclusions. Our findings could be considered by clinicians in prescribing empiric antibiotic therapy for CAP in immunocompromised patients. Patients with AIDS and hematological cancer admitted with CAP may have higher prevalences of fungi, mycobacteria, and noninfluenza viruses

Microbiological testing of adults hospitalised with community-acquired pneumonia: An international study

This study aimed to describe real-life microbiological testing of adults hospitalised with community-acquired pneumonia (CAP) and to assess concordance with the 2007 Infectious Diseases Society of America (IDSA)/American Thoracic Society (ATS) and 2011 European Respiratory Society (ERS) CAP guidelines. This was a cohort study based on the Global Initiative for Methicillin-resistant Staphylococcus aureus Pneumonia (GLIMP) database, which contains point-prevalence data on adults hospitalised with CAP across 54 countries during 2015. In total, 3702 patients were included. Testing was performed in 3217 patients, and included blood culture (71.1%), sputum culture (61.8%), Legionella urinary antigen test (30.1%), pneumococcal urinary antigen test (30.0%), viral testing (14.9%), acute-phase serology (8.8%), bronchoalveolar lavage culture (8.4%) and pleural fluid culture (3.2%). A pathogen was detected in 1173 (36.5%) patients. Testing attitudes varied significantly according to geography and disease severity. Testing was concordant with IDSA/ATS and ERS guidelines in 16.7% and 23.9% of patients, respectively. IDSA/ATS concordance was higher in Europe than in North America (21.5% versus 9.8%; p&lt;0.01), while ERS concordance was higher in North America than in Europe (33.5% versus 19.5%; p&lt;0.01). Testing practices of adults hospitalised with CAP varied significantly by geography and disease severity. There was a wide discordance between real-life testing practices and IDSA/ATS/ERS guideline recommendations