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Abstract

This study investigates revolutionary advancements in medulloblastoma treatment by combining liquid biopsy with AI-powered precision medicine. Early identification and individualized treatment plans are made possible by these technologies' non-invasive, real-time insights into tumor processes. The objectives of this strategy are to optimize patient-specific therapy, increase diagnostic accuracy, and eventually improve outcomes in pediatric neuro-oncology by examining biomarkers, such as circulating tumor DNA, and using sophisticated AI algorithms to understand complicated multi-omics data. One of the most difficult children brain tumors to diagnose and treat is medulloblastoma. This paper describes a revolutionary integrated strategy that combines liquid biopsy with AI-powered precision medicine to non-invasively monitor tumor dynamics and tailor treatment. In pediatric neuro-oncology, the methods seek to improve patient outcomes by optimizing therapy approaches, improving diagnostic accuracy, and utilizing real-time biomarker monitoring and sophisticated data modeling.

Keywords

Medulloblastoma, Liquid Biopsy, AI-Driven Precision Medicine, Circulating Tumor DNA (ctDNA), Circulating Tumor Cells (CTCs), Extracellular Vesicles (EVs), MicroRNAs (miRNAs), Biomarkers, Tumor Dynamics, Non-Invasive Monitoring, Molecular Subtyping, Risk Stratification, Minimal Residual Disease (MRD), Multi-Omics Integration, Machine Learning, Deep Learning, Radiomics, Personalized Therapy, Treatment Optimization, Pediatric Neuro-Oncology

Introduction

Medulloblastoma is a very aggressive embryonal tumor of the central nervous system (CNS) that mostly affects children, accounting for 20% of all pediatric brain tumors (Northcott et al., 2017). These tumors are high-grade and have a tendency to spread inside the central nervous system and, less commonly, outside the neuraxis (Cassie et al., 2017). The current standard of treatment is a combination of maximally safe surgical resection, craniospinal irradiation, and multi-agent chemotherapy (Gajjar et al., 2019). Despite advancements in treatment, major obstacles persist, including high recurrence rates, therapy-induced toxicities, and long-term neurocognitive deficits, particularly in younger patients (Ramaswamy et al., 2016). A major obstacle in the treatment of medulloblastoma is the difficulty of collecting recurring tumor samples to track the course of the illness and the effectiveness of treatment. Conventional tumor samples necessitate risky, invasive neurosurgical techniques that are impractical for long-term surveillance (Taylor et al., 2019). As a result, liquid biopsy is becoming increasingly popular as a non-invasive option for real-time tumor dynamics monitoring. Liquid biopsy is the process of identifying and analyzing tumor-derived biomarkers from biofluids, including blood and cerebrospinal fluid (CSF), such as circulating tumor DNA (ctDNA), extracellular vesicles (EVs), and microRNAs (miRNAs) (Wang et al., 2021). This method has a lot of potential for improving diagnosis, risk assessment, and directing individualized treatment. The use of artificial intelligence (AI) in precision medicine is another game-changing advance in medulloblastoma treatment. AI generates insights by leveraging powerful computing and inference, allowing the system to think and learn, and using enhanced intelligence to boost clinical decision-making (Johnson K.B. et al., 2021). AI improves decision-making across several fields, including medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practices, even to the extent AI aids in the classification and selection of patient populations (Carini, Seyhan., 2024). Humans differ greatly on a genetic, biochemical, physiological, exposure, and behavioral level, particularly in relation to disease processes and response to therapy, according to research investigations using data-intensive biomedical technology (Schork N.J., 2019). AI, particularly machine learning (ML) and deep learning (DL) algorithms, has shown great promise in improving tumor categorization, predicting therapy responses, and discovering new biomarkers (Fathi Kazerooni et al., 2022). AI-driven precision medicine can offer customized treatment plans that maximize patient outcomes while reducing toxicity by utilizing multi-omics datasets, imaging modalities, and longitudinal patient data with tumors (Bzdok et al., 2020). Furthermore, AI can help detect early relapses using predictive modeling, enabling earlier therapies (Wang et al., 2023). This study will look at the synergistic potential of liquid biopsy and AI-powered precision medicine in medulloblastoma, including recent advances, clinical uses, present difficulties, and future possibilities.

Reference

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Sewanu Stephen Godonu
Corresponding author

All American Institute of Medical Sciences

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Aafrin Steffi Vijaya Kumar Glory
Co-author

All American Institute of Medical Sciences

Sewanu Stephen Godonu, Aafrin Steffi Vijaya Kumar Glory, A Comparative Review of Liquid Biopsy and AI-Powered Precision Medicine in Medulloblastoma: A Paradigm Shift in Diagnosis and Treatment, Int. J. Sci. R. Tech., 2025, 2 (3), 268-281. https://doi.org/10.5281/zenodo.15051286

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