====== Artificial Intelligence for glioblastoma research ======

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[[Artificial intelligence]] (AI) is transforming **[[glioblastoma research]]** across multiple domains, from diagnosis to treatment and prognosis prediction. 


===== 1. AI in Diagnosis & Imaging =====

AI plays a crucial role in **medical imaging** for glioblastoma detection, segmentation, and classification:
- **Deep learning models (CNNs, GANs)** improve **MRI-based GBM detection**.
- **[[Radiomics]]** extracts quantitative imaging features to differentiate **GBM subtypes**.
- **AI-powered segmentation** (e.g., using **U-Net, DeepLabV3** models) automates **tumor boundary detection**, reducing observer variability.

**Notable Applications:**
- **BraTS Challenge datasets** (Brain Tumor Segmentation) train AI models on GBM/MRI data.
- **Federated Learning (FL)** allows AI models to learn across hospitals **without sharing raw patient data**.

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===== 2. AI in Genomics & Biomarkers =====

AI aids in analyzing large-scale **multi-omics data** (genomics, transcriptomics, proteomics) to identify **molecular subtypes** and actionable biomarkers:
- **Machine learning** identifies **IDH mutations, MGMT promoter methylation**.
- **AI-driven drug discovery** screens for potential therapeutic targets.
- **Integrating radiogenomics** links imaging features to molecular signatures.

**Notable Applications:**
- AI models predict **survival outcomes** based on imaging-genomic correlations.
- **Deep learning identifies non-invasive biomarkers** from liquid biopsies.

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===== 3. AI in Treatment Personalization =====

AI optimizes glioblastoma treatment through:
- **Predicting radiotherapy response** using imaging + molecular data.
- **Personalized chemotherapy regimens** via AI-based drug sensitivity modeling.
- **Virtual clinical trials**: AI simulates patient response to experimental drugs.

**Notable Applications:**
- **AI-guided precision medicine** suggests individualized treatments.
- **Combination therapy modeling** predicts the best drug pairs for GBM.

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==== 4. AI for Prognosis & Survival Prediction ====

AI enhances survival prediction by integrating:
- **Imaging features (radiomics)**
- **Histopathological data**
- **Clinical parameters**
- **Genomic alterations**

**[[Deep learning]]** models provide survival predictions that outperform traditional **Kaplan-Meier** curves.

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==== 5. AI for Drug Discovery & Repurposing ====

AI accelerates **[[glioblastoma drug discovery]]** by:

- Identifying **novel drug targets** using network-based AI.

- Screening FDA-approved drugs for **repurposing** (e.g., **AI identified anti-parasitic drugs as GBM candidates**).

- **Predicting blood-brain barrier (BBB) permeability** for better drug selection.

**Notable AI platforms:**
- **AlphaFold** (Protein Structure Prediction)
- **IBM Watson for Drug Discovery**
- **DeepMind’s AI in Pharmacology**

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==== 6. AI-Driven Surgery & Robotics ====

AI improves neurosurgical precision by:
- **Enhancing intraoperative imaging** (real-time AI-assisted neuronavigation).
- **Optimizing resection margins** (deep learning assists in distinguishing tumor vs. healthy tissue).
- **Robotic-assisted neurosurgery** (AI augments robotic systems like **ROSA** or **Brainlab**).

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==== 7. AI in Clinical Decision Support Systems (CDSS) ====

- AI-based **decision support tools** integrate imaging, pathology, and genomics to assist oncologists.
- **Chatbot AI assistants** help in patient education and symptom monitoring.

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## **Future Directions & Challenges**
- **Data Privacy**: Federated Learning (FL) could mitigate privacy concerns.
- **Model Interpretability**: Black-box AI models require **explainability**.
- **Standardization**: More **multi-center validation** of AI models needed.
- **[[Clinical Translation]]**: AI models must be **clinically validated** before integration into practice.

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## **Conclusion**
AI is revolutionizing glioblastoma research through **enhanced diagnostics, precision medicine, drug discovery, and neurosurgery support**. The integration of **deep learning, radiomics, genomics, and clinical AI** promises **improved patient outcomes**. However, challenges such as data privacy, standardization, and clinical adoption remain key hurdles to overcome.