Oncology/Hematology

Brain Cancer

Latest AI and machine learning research in brain cancer for healthcare professionals.

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Performance of deep learning models for automatic histopathological grading of meningiomas: a systematic review and meta-analysis.

BACKGROUND: Accurate preoperative grading of meningiomas is crucial for selecting the most suitable ...

Brain tumor classification using MRI images and deep learning techniques.

Brain tumors pose a significant medical challenge, necessitating early detection and precise classif...

Integrative Machine Learning of Glioma and Coronary Artery Disease Reveals Key Tumour Immunological Links.

It is critical to appreciate the role of the tumour-associated microenvironment (TME) in developing ...

A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy.

BACKGROUND: Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We...

Deciphering the Role of SLFN12: A Novel Biomarker for Predicting Immunotherapy Outcomes in Glioma Patients Through Artificial Intelligence.

Gliomas are the most prevalent form of primary brain tumours. Recently, targeting the PD-1 pathway w...

State-of-the-Art Deep Learning CT Reconstruction Algorithms in Abdominal Imaging.

The implementation of deep neural networks has spurred the creation of deep learning reconstruction ...

Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.

This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised ...

Enhancing neuro-oncology care through equity-driven applications of artificial intelligence.

The disease course and clinical outcome for brain tumor patients depend not only on the molecular an...

Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma.

BACKGROUND: Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diag...

Development of machine learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma.

The aim of this study was to develop a machine-learning prediction model for AKI after craniotomy an...

Leveraging machine learning for preoperative prediction of supramaximal ablation in laser interstitial thermal therapy for brain tumors.

OBJECTIVE: Maximizing safe resection in neuro-oncology has become paramount to improving patient sur...

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.

The development, application, and benchmarking of artificial intelligence (AI) tools to improve diag...

Prediction of Glioma enhancement pattern using a MRI radiomics-based model.

Contrast-MRI scans carry risks associated with the chemical contrast agents. Accurate prediction of ...

Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.

MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Sev...

Utilizing patient data: A tutorial on predicting second cancer with machine learning models.

BACKGROUND: The article explores the potential risk of secondary cancer (SC) due to radiation therap...

Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.

Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and none...

Predicting Overall Survival of Glioblastoma Patients Using Deep Learning Classification Based on MRIs.

INTRODUCTION: Glioblastoma (GB) is one of the most aggressive tumors of the brain. Despite intensive...

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