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Artificial Intelligence Enhances Precision Oncology in Breast Cancer Management

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In the evolving landscape of breast cancer treatment, the integration of artificial intelligence (AI) into precision oncology is proving to be transformative. By leveraging AI, healthcare professionals can tailor therapies to the unique genetic makeup of each patient, potentially improving outcomes and extending survival rates. This article delves into the systematic review of studies that explore the application of AI in the personalized management of breast cancer, highlighting the significant strides and future potential in this domain.

Table of Contents

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Methodology and Analysis

Researchers conducted a thorough systematic review in September 2023, examining databases such as PubMed, Embase, Scopus, and Web of Science. The search focused on keywords related to Breast Cancer, Artificial Intelligence, and Precision Oncology. Excluding non-English studies and those that were descriptive or qualitative, the review adhered to rigorous quality assessment standards, ensuring the credibility of the selected studies.

Forty-six studies were identified, emphasizing the use of AI models in personalized breast cancer treatment. The review revealed that seventeen studies utilized various deep learning methods, achieving notable success in predicting treatment response and prognosis. This advancement is crucial for market access, as it aligns with the need for more precise and effective treatment options in the healthcare market.

Key Findings in AI and Breast Cancer

The review highlighted two studies that employed neural networks and clustering techniques to predict patient survival and categorize breast tumors effectively. Additionally, one study used transfer learning to anticipate treatment response, showcasing the versatility of AI in different aspects of cancer management. Twenty-six studies demonstrated that machine learning methods significantly enhance breast cancer classification, screening, diagnosis, and prognosis. The widespread use of models like Naive Bayes (NB), Support Vector Machines (SVM), Random Forests (RF), XGBoost, and Reinforcement Learning underscores the robust capabilities of AI in this field.

The models achieved an impressive average area under the curve (AUC) of 0.91, with accuracy, sensitivity, specificity, and precision averaging between 90-96%. These metrics underscore the reliability and effectiveness of AI in improving breast cancer treatment outcomes, making a compelling case for its broader adoption in the healthcare market.

Market Access Implications

– The integration of AI in precision oncology can streamline patient stratification, ensuring timely and appropriate treatment, which is crucial for market access.
– Enhanced predictive models can reduce treatment costs by minimizing trial-and-error approaches, making advanced therapies more accessible.
– AI-driven insights can support regulatory approvals and reimbursement strategies, facilitating quicker market entry for innovative treatments.
– The robust performance metrics of AI models can attract investment in AI-based healthcare solutions, promoting market growth.

The confluence of AI and precision oncology is poised to revolutionize breast cancer management. By identifying hidden patterns in complex genetic data, AI empowers healthcare providers to make more informed decisions, ultimately benefiting patient outcomes and the healthcare market.

Artificial intelligence has emerged as a pivotal tool in the fight against breast cancer, optimizing treatment strategies and enhancing patient care. As these technologies continue to evolve, their impact on the healthcare market will only grow, driving advancements in personalized medicine.

Original Article:

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BMC Cancer. 2024 Jul 18;24(1):852. doi: 10.1186/s12885-024-12575-1.

ABSTRACT

BACKGROUND: Providing appropriate specialized treatment to the right patient at the right time is considered necessary in cancer management. Targeted therapy tailored to the genetic changes of each breast cancer patient is a desirable feature of precision oncology, which can not only reduce disease progression but also potentially increase patient survival. The use of artificial intelligence alongside precision oncology can help physicians by identifying and selecting more effective treatment factors for patients.

METHOD: A systematic review was conducted using the PubMed, Embase, Scopus, and Web of Science databases in September 2023. We performed the search strategy with keywords, namely: Breast Cancer, Artificial intelligence, and precision Oncology along with their synonyms in the article titles. Descriptive, qualitative, review, and non-English studies were excluded. The quality assessment of the articles and evaluation of bias were determined based on the SJR journal and JBI indices, as well as the PRISMA2020 guideline.

RESULTS: Forty-six studies were selected that focused on personalized breast cancer management using artificial intelligence models. Seventeen studies using various deep learning methods achieved a satisfactory outcome in predicting treatment response and prognosis, contributing to personalized breast cancer management. Two studies utilizing neural networks and clustering provided acceptable indicators for predicting patient survival and categorizing breast tumors. One study employed transfer learning to predict treatment response. Twenty-six studies utilizing machine-learning methods demonstrated that these techniques can improve breast cancer classification, screening, diagnosis, and prognosis. The most frequent modeling techniques used were NB, SVM, RF, XGBoost, and Reinforcement Learning. The average area under the curve (AUC) for the models was 0.91. Moreover, the average values for accuracy, sensitivity, specificity, and precision were reported to be in the range of 90-96% for the models.

CONCLUSION: Artificial intelligence has proven to be effective in assisting physicians and researchers in managing breast cancer treatment by uncovering hidden patterns in complex omics and genetic data. Intelligent processing of omics data through protein and gene pattern classification and the utilization of deep neural patterns has the potential to significantly transform the field of complex disease management.

PMID:39026174 | DOI:10.1186/s12885-024-12575-1


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