Cellular and molecular basis of therapeutic approaches to breast cancer

El‐Tanani, Mohamed; Khatib, Arwa Omar Al; Al-Najjar, Belal O.; Shakya, Ashok K.; El-Tanani, Yahia; Lee, Yin-Fai; Serrano‐Aroca, Ángel; Mishra, Vijay; Mishra, Yachana; Aljabali, Alaa A. A.; Goyal, Rohit; Negi, Poonam; Farani, Marzieh Ramezani; Binabaj, Maryam Moradi; Gholami, Amir; Charbe, Nitin; Tambuwala, Murtaza M.

doi:10.1016/j.cellsig.2022.110492

Cited by 19 publications

(7 citation statements)

References 173 publications

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“…Traditionally, the diagnosis of breast cancer relied on manual examination of histopathological images by experienced pathologists. While pathologists possess extensive expertise, this approach is time-consuming, subject to interobserver variability, and can be prone to errors, especially in distinguishing between subtle histological subtypes ( 1 ). The introduction of traditional machine learning approaches marked the beginning of automated image analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Al Moteri,

Mahesh,

Thakur

et al. 2024

Front. Med.

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Al Moteri,

Mahesh,

Thakur

et al. 2024

Front. Med.

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“…A hypersensitive reaction is the most lethal type of adverse event, characterized by life-threatening symptoms such as hypotension, asthma, fever, and hypoxia caused by the massive release of cytokines. Bevacizumab has several class effects, with heart failure and hypertension being the most common [22].…”

Section: Bevacizumabmentioning

confidence: 99%

Recent Perspectives on Cardiovascular Toxicity Associated with Colorectal Cancer Drug Therapy

Kashyap,

Mangrulkar,

Kushwaha

et al. 2023

Pharmaceuticals

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Cardiotoxicity is a well-known adverse effect of cancer-related therapy that has a significant influence on patient outcomes and quality of life. The use of antineoplastic drugs to treat colorectal cancers (CRCs) is associated with a number of undesirable side effects including cardiac complications. For both sexes, CRC ranks second and accounts for four out of every ten cancer deaths. According to the reports, almost 39% of patients with colorectal cancer who underwent first-line chemotherapy suffered cardiovascular impairment. Although 5-fluorouracil is still the backbone of chemotherapy regimen for colorectal, gastric, and breast cancers, cardiotoxicity caused by 5-fluorouracil might affect anywhere from 1.5% to 18% of patients. The precise mechanisms underlying cardiotoxicity associated with CRC treatment are complex and may involve the modulation of various signaling pathways crucial for maintaining cardiac health including TKI ErbB2 or NRG-1, VEGF, PDGF, BRAF/Ras/Raf/MEK/ERK, and the PI3/ERK/AMPK/mTOR pathway, resulting in oxidative stress, mitochondrial dysfunction, inflammation, and apoptosis, ultimately damaging cardiac tissue. Thus, the identification and management of cardiotoxicity associated with CRC drug therapy while minimizing the negative impact have become increasingly important. The purpose of this review is to catalog the potential cardiotoxicities caused by anticancer drugs and targeted therapy used to treat colorectal cancer as well as strategies focused on early diagnosing, prevention, and treatment of cardiotoxicity associated with anticancer drugs used in CRC therapy.

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“…Breast cancer originates from abnormal cell proliferation within the breast tissue, often leading to the formation of a breast tumor and the potential spread of cancer to other parts of the body ( 2 ). Cancerous tumors are referred to as malignant because they hinder normal body functions and push out healthy tissue ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

Jabeen,

Khan,

Hameed

et al. 2024

Front. Oncol.

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With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate of this disease pose severe global health issues for women. Identifying the disease’s influence is the only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging to identify BC. Still, the precision of each strategy differs based on the available resources, the issue’s nature, and the dataset being used. We proposed a novel deep bottleneck convolutional neural network with a quantum optimization algorithm for breast cancer classification and diagnosis from mammogram images. Two novel deep architectures named three-residual blocks bottleneck and four-residual blocks bottle have been proposed with parallel and single paths. Bayesian Optimization (BO) has been employed to initialize hyperparameter values and train the architectures on the selected dataset. Deep features are extracted from the global average pool layer of both models. After that, a kernel-based canonical correlation analysis and entropy technique is proposed for the extracted deep features fusion. The fused feature set is further refined using an optimization technique named quantum generalized normal distribution optimization. The selected features are finally classified using several neural network classifiers, such as bi-layered and wide-neural networks. The experimental process was conducted on a publicly available mammogram imaging dataset named INbreast, and a maximum accuracy of 96.5% was obtained. Moreover, for the proposed method, the sensitivity rate is 96.45, the precision rate is 96.5, the F1 score value is 96.64, the MCC value is 92.97%, and the Kappa value is 92.97%, respectively. The proposed architectures are further utilized for the diagnosis process of infected regions. In addition, a detailed comparison has been conducted with a few recent techniques showing the proposed framework’s higher accuracy and precision rate.

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Cellular and molecular basis of therapeutic approaches to breast cancer

Cited by 19 publications

References 173 publications

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

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

Recent Perspectives on Cardiovascular Toxicity Associated with Colorectal Cancer Drug Therapy

A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

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