Is There a Role for Machine Learning in Liquid Biopsy for Brain Tumors? A Systematic Review

Potential of AI and ML in oncology research including diagnosis, treatment and future directions: A comprehensive prospective

Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in cancer research, offering the ability to process huge data rapidly and make precise therapeutic decisions. Over the last decade, AI, particularly deep learning (DL) and machine learning (ML), has significantly enhanced cancer prediction, diagnosis, and treatment by leveraging algorithms such as convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs). These technologies provide reliable, efficient solutions for managing aggressive diseases like cancer, which have high recurrence and mortality rates. This review prospective highlights the applications of AI in oncology, a long with FDA-approved technologies like EFAI RTSuite CT HN-Segmentation System, Quantib Prostate, and Paige Prostate, and explore their role in advancing cancer detection, personalized care, and treatment. Furthermore, we also explored broader applications of AI in healthcare, addressing challenges, limitations, regulatory considerations, and ethical implications. By presenting these advancements, we underscore AI's potential to revolutionize cancer care, management and treatment.

Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment

Advances in neuro-oncology have transformed the diagnosis and management of brain tumors, which are among the most challenging malignancies due to their high mortality rates and complex neurological effects. Despite advancements in surgery and chemoradiotherapy, the prognosis for glioblastoma multiforme (GBM) and brain metastases remains poor, underscoring the need for innovative diagnostic strategies. This review highlights recent advancements in imaging techniques, liquid biopsies, and artificial intelligence (AI) applications addressing current diagnostic challenges. Advanced imaging techniques, including diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS), improve the differentiation of tumor progression from treatment-related changes. Additionally, novel positron emission tomography (PET) radiotracers, such as 18F-fluoropivalate, 18F-fluoroethyltyrosine, and 18F-fluluciclovine, facilitate metabolic profiling of high-grade gliomas. Liquid biopsy, a minimally invasive technique, enables real-time monitoring of biomarkers such as circulating tumor DNA (ctDNA), extracellular vesicles (EVs), circulating tumor cells (CTCs), and tumor-educated platelets (TEPs), enhancing diagnostic precision. AI-driven algorithms, such as convolutional neural networks, integrate diagnostic tools to improve accuracy, reduce interobserver variability, and accelerate clinical decision-making. These innovations advance personalized neuro-oncological care, offering new opportunities to improve outcomes for patients with central nervous system tumors. We advocate for future research integrating these tools into clinical workflows, addressing accessibility challenges, and standardizing methodologies to ensure broad applicability in neuro-oncology.

Elements of liquid biopsies: isolation, analysis, and clinical application in cancer diagnosis to prognosis

INTRODUCTION

The liquid biopsy is a breakthrough in the field of medical diagnostics. It serves as a sentinel that can quietly detect even the subtlest aberrations that indicate the presence of disease. They make it possible to uncover relevant genetic factors of tumors with minimal to no risk to cancer patients. Liquid biopsies allow detailed diagnosis, dynamic treatment monitoring, and accurate prognosis. They are also invaluable in diagnosing other diseases such as infectious diseases and aberrant gene mutations.

AREAS COVERED

The present review undertakes an in-depth analysis of the existing status of liquid biopsy diagnostic tools, focusing on their principal components. Furthermore, the review highlights pertinent and recent research in this field to provide a comprehensive understanding of the current state of this technology and its prospects.

EXPERT OPINION

Despite new and upcoming research in liquid biopsies, multiple areas need to be further explored before the viable transition into the clinical arena. With the advancements in tools such as artificial intelligence and machine learning and the integration of these technologies with liquid biopsies, these challenges are being addressed and will eventually lead to the development of a highly evolved liquid biopsy diagnostic tools.

Research on biomarkers using innovative artificial intelligence systems in breast cancer

Cancer is highly diverse and heterogeneous. Accurate and rapid analysis of the characteristics of individual cancer cells, using a complex array of big data that includes various clinicopathological features and molecular mechanisms, is crucial for advancing precision medicine. In recent years, experts in biomedical sciences and data sciences have explored the potential of artificial intelligence (AI) to analyze such extensive data sets. The next phase of AI-based medical research on cancer should focus on the practical applications of AI tools and how they can be effectively used in actual medical research settings. Recently, translational research that leverages AI and comprehensive genetic analysis data has emerged as a significant research focus. This field represents an opportunity for groundbreaking discoveries to be shared globally. To further precision medicine in clinical practice, it is vital to develop sophisticated AI tools for cancer research. These tools should not only identify potential therapeutic targets through comprehensive genetic analysis but also predict therapeutic outcomes in clinical settings.

Unveiling the impact of corticosteroid therapy on liquid biopsy-detected cell-free DNA levels in meningioma and glioblastoma patients

The liquid biopsy era has brought several possibilities to improve precision in patient care. Among the different sources of analytes, the cfDNA has been explored as a possible disease indicator, especially in cancer. Intracranial tumors still represent a challenge for liquid biopsy due to the blood-brain barrier being able to restrain both the migrating tumor cells and the liberation of cfDNA into the blood circulation. The aim of this work was to compare the differences between the cfDNA concentration in the plasma from patients with central nervous system tumors, and for this we analyzed a cohort of 188 individuals with glioblastoma (N = 57), brain metastasis (N = 15), meningioma (N = 90) and schwannoma (N = 26). Plasma samples were obtained immediately before tumor excision, and the cfDNA was isolated from the samples and quantified. The results showed that cfDNA plasma levels vary according to the tumors analyzed, with glioblastoma and brain metastasis presenting higher median levels of cfDNA than meningiomas and schwannomas. In addition, corticosteroid treatment resulted in higher cfDNA levels in meningioma and glioblastoma patients and vasogenic brain edema resulted in higher cfDNA levels only in meningioma patients. We hypothesize that cfDNA evaluation might have clinical monitoring value and that other clinical variables, such as corticosteroid used, should be considered during the liquid biopsy clinical evaluation of intracranial tumors.

Beyond blood: Advancing the frontiers of liquid biopsy in oncology and personalized medicine

Liquid biopsy is emerging as a pivotal tool in precision oncology, offering a noninvasive and comprehensive approach to cancer diagnostics and management. By harnessing biofluids such as blood, urine, saliva, cerebrospinal fluid, and pleural effusions, this technique profiles key biomarkers including circulating tumor DNA, circulating tumor cells, microRNAs, and extracellular vesicles. This review discusses the extended scope of liquid biopsy, highlighting its indispensable role in enhancing patient outcomes through early detection, continuous monitoring, and tailored therapy. While the advantages are notable, we also address the challenges, emphasizing the necessity for precision, cost-effectiveness, and standardized methodologies in its broader application. The future trajectory of liquid biopsy is set to expand its reach in personalized medicine, fueled by technological advancements and collaborative research.