Brain Cancer

Artificial Intelligence Application for Anti-tumor Drug Synergy Prediction.

Currently, the main therapeutic methods for cancer include surgery, radiation therapy, and chemotherapy. However, chemotherapy still plays an important role in tumor therapy. Due to the variety of pathogenic factors, the development process of tumors is complex and regulated by many factors, and the treatment of a single drug is easy to cause the human body to produce a drug-resistant phenotype to...

Meta-transfer Learning for Brain Tumor Segmentation: Within and Beyond Glioma.

In recent years, numerous algorithms have emerged for the segmentation of brain tumors, propelled by both the advancements of deep learning techniques and the influential open benchmark set by the BraTS challenge. This chapter provides an overview of the background that gave rise to automated brain tumor segmentation algorithms, reviews representative deep learning-based approaches, and reflects t...

Machine Learning and Radiomics in Gliomas.

The integration of machine learning (ML) and radiomics is emerging as a pivotal advancement in glioma research, offering novel insights into the diagn...

Machine and Deep Learning in Hyperspectral Fluorescence-Guided Brain Tumor Surgery.

Malignant glioma resection is often the first line of treatment in neuro-oncology. During glioma surgery, the discrimination of tumor's edges can be c...

Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery.

The chapter explores the extensive integration of artificial intelligence (AI) in healthcare systems, with a specific focus on its application in ster...

Enhancing coronary artery plaque analysis via artificial intelligence-driven cardiovascular computed tomography.

Coronary computed tomography angiography (CCTA) is a noninvasive imaging modality of cardiac structures and vasculature considered comparable to invas...

Response to "Medical Statistics Unlock the Gateway to Further Research: Using Deep Learning to Predict CDKN2A/B Homozygous Deletion in Isocitrate Dehydrogenase-Mutant Astrocytoma".

Medical Statistics Unlock the Gateway to Further Research: Using Deep Learning to Predict CDKN2A/B Homozygous Deletion in Isocitrate Dehydrogenase-Mutant Astrocytoma.

[Robot-assisted prostatectomy for PT3. Oncological and functional outcomes].

In 2020, prostate cancer (PCa) ranked third in the structure of the most significant oncological diseases. In the Russian Federation, in terms of the ...

[Research Progress in the Application of Ultrasound-Guided Robot in Minimally Invasive Surgery].

With the continuous advances in modern medical technology and equipment,minimally invasive surgery (MIS) is widely applied in clinical practice.Ultras...

Evaluation of deep learning-based deliverable VMAT plan generated by prototype software for automated planning for prostate cancer patients.

This study aims to evaluate the dosimetric accuracy of a deep learning (DL)-based deliverable volumetric arc radiation therapy (VMAT) plan generated u...

Impact of a reduced iodine load with deep learning reconstruction on abdominal MDCT.

To evaluate the impact of a reduced iodine load using deep learning reconstruction (DLR) on the hepatic parenchyma compared to conventional iterative ...

[A Dual-Aware deep learning framework for identification of glioma isocitrate dehydrogenase genotype using magnetic resonance amide proton transfer modalities].

OBJECTIVE: To propose a Dual-Aware deep learning framework for genotyping of isocitrate dehydrogenase (IDH) in gliomas based on magnetic resonance ami...

Unlocking glioma genetics with deep learning.

The AI era in medicine has ushered in new opportunities to improve the diagnosis and treatment of human disease. CHARM, an AI algorithm described in t...

Development of a deep learning-based error detection system without error dose maps in the patient-specific quality assurance of volumetric modulated arc therapy.

To detect errors in patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), we proposed an error detection method based o...

Deep learning based on preoperative magnetic resonance (MR) images improves the predictive power of survival models in primary spinal cord astrocytomas.

BACKGROUND: Prognostic models for spinal cord astrocytoma patients are lacking due to the low incidence of the disease. Here, we aim to develop a full...

Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO).

PURPOSE: Efforts to use growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling, ...

Code-free machine learning for classification of central nervous system histopathology images.

Machine learning (ML), an application of artificial intelligence, is currently transforming the analysis of biomedical data and specifically of biomed...

Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.

BACKGROUND: Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial d...

COMBINING HI-RESOLUTION SCAN MODE WITH DEEP LEARNING RECONSTRUCTION ALGORITHMS IN CARDIAC CT.

To investigate the impact of combining the high-resolution (Hi-res) scan mode with deep learning image reconstruction (DLIR) algorithm in CT. Two phan...