A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives

Corredor, Germán; Bharadwaj, Satvika; Pathak, T; Viswanathan, Vidya Sankar; Toro, Paula; Madabhushi, Anant

doi:10.1016/j.clbc.2023.06.004

Cited by 9 publications

(4 citation statements)

References 91 publications

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“…AI is very useful in medical applications including detecting tumors with using convolution neural networks (CNN) which can sometimes detect the importance in decision making much faster than human eye. CNN, or Convolutional Neural Networks, have shown promising results in detecting breast cancer through the analysis of medical images such as mammograms [35]. CNNs are a type of deep learning algorithm that is particularly well-suited for image recognition tasks.…”

Section: Resultsmentioning

confidence: 99%

“…Deep learning algorithms are capable of analyzing large amounts of medical imaging data, such as mammograms, MRIs, and ultrasounds, to identify patterns and anomalies that may indicate the presence of cancerous tumors. These algorithms can learn from vast datasets and continually improve their performance over time, making them valuable tools for radiologists in interpreting complex imaging studies [35]. Another important application of AI in breast cancer detection is the development of CAD systems.…”

Section: Resultsmentioning

confidence: 99%

“…Artificial intelligence (AI) techniques have significantly advanced laboratory monitoring for cancer, and healthcare professionals with powerful tools to aid in early detection, diagnosis, and treatment. Various AI techniques are being used in laboratory settings to analyze and help interpretation of molecular, genetic, and pathological data related to cancer, allowing for personalized and effective patient care [35,36]. Convolutional Neural Networks (CNNs) undergo a rigorous training process involving intricate technical procedures to facilitate feature extraction and model optimization.…”

Section: Discussionmentioning

confidence: 99%

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Application of AI in detection of breast cancer with laboratory results monitoring

Prevljak,

Kustura,

Hasanefendic

et al. 2024

Bioengineering Studies

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Breast cancer is one of the most common types of cancer among women worldwide, therefore an early and precise process of diagnostics plays an important role in improving the prognosis and outcome of treatment. The application of artificial intelligence (AI) allows faster and more precise analysis of medical imaging, which contributes to the early detection of tumors and lowers the number of false-negative results. This review article analyzed 60 scientific papers and using the most recent findings about this topic, searched for AI implementation in breast cancer research and how AI may improve overall survival outcomes for breast cancer patients.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Application of AI in detection of breast cancer with laboratory results monitoring

Prevljak,

Kustura,

Hasanefendic

et al. 2024

Bioengineering Studies

View full text Add to dashboard Cite

show abstract

“…The predictive molecular markers HER2, progestin receptor, and estrogen receptor were among those whose levels and statuses could be more clearly distinguished using the model created in this study 31 . A nomograph was created using medical characteristics based on radiomic algorithms to forecast the possibility of axillary lymph node metastasis and complications in people with initial BC.…”

Section: Radiomics In Breast Cancermentioning

confidence: 91%

Artificial Intelligence - A Primer for Diagnosis and Interpretation of Breast Cancer

Mohan Jha,

Prasada Kumar Mahapatra,

Abraham

et al. 2024

Int J Tre Onc Sci

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Breast Cancer (BC) is a major universal health problem. Early detection and precise diagnosis are vital for enlightening outcomes. Artificial Intelligence (AI) technologies can potentially revolutionize the field of BC by providing quantitative representations of medical images to assist in segmentation, diagnosis, and prognosis. AI can improve image quality, detect and segment breast lesions, classify cancer and predict its behavior, and integrate data from multiple sources to predict clinical outcomes. It can lead to more personalized and effective treatment for BC patients. Challenges faced by AI in real-life solicitations include data curation, model interpretability, and run-through guidelines. However, the clinical implementation of AI is expected to deliver vital guidance for patient-tailored management. BC is a major global health problem; early detection and treatment are crucial for improving outcomes. Imaging detection is a key screening, diagnosis, and treatment effectiveness assessment tool. However, the irresistible number of images creates a heavy capacity for radiologists and delays reporting. AI has the potential to revolutionize BC imaging by improving efficiency and accuracy. AI can recognize, segment, and diagnose tumor lesions automatically and analyze tumor images on a molecular level. It could lead to more personalized treatment strategies. However, AI-assisted imaging diagnosis is still in its early stages of development, and more research is needed to validate its clinical effectiveness. Therefore, AI is a promising new technology that has the potential to progress the diagnosis and treatment of BC, and AI-assisted imaging diagnosis is a promising new technology for improving the early detection and diagnosis of BC. More research is needed to bring this technology to clinical practice.

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Future of Team-based Basic and Translational Science in Radiation Oncology

Coppes,

van Dijk

2024

Seminars in Radiation Oncology

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A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives

Cited by 9 publications

References 91 publications

Application of AI in detection of breast cancer with laboratory results monitoring

Application of AI in detection of breast cancer with laboratory results monitoring

Artificial Intelligence - A Primer for Diagnosis and Interpretation of Breast Cancer

Future of Team-based Basic and Translational Science in Radiation Oncology

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