Oncology/Hematology

Brain Cancer

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

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Robotic radiation shielding system reduces radiation-induced DNA damage in operators performing electrophysiological procedures.

Fluoroscopically guided electrophysiology (EP) procedures expose operators to low doses of ionizing ...

Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images.

BACKGROUND: Pineal region tumors (PRTs) are rare but deep-seated brain tumors, and complete surgical...

Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy.

One possible adverse effect of breast irradiation is the development of pulmonary fibrosis. The aim ...

Reconstruction of partially obscured objects with a physics-driven self-training neural network.

We investigate artificial-intelligence-supported in-line holographic imaging with coherent terahertz...

Multiple deep learning models based on MRI images in discriminating glioblastoma from solitary brain metastases: a multicentre study.

OBJECTIVE: Development of a deep learning model for accurate preoperative identification of glioblas...

Current trends and emerging themes in utilizing artificial intelligence to enhance anatomical diagnostic accuracy and efficiency in radiotherapy.

Artificial intelligence (AI) incorporation into healthcare has proven revolutionary, especially in r...

A self-supervised multimodal deep learning approach to differentiate post-radiotherapy progression from pseudoprogression in glioblastoma.

Accurate differentiation of pseudoprogression (PsP) from True Progression (TP) following radiotherap...

The role of artificial intelligence in occupational health in radiation exposure: a scoping review of the literature.

INTRODUCTION: Artificial intelligence (AI) has the potential to significantly enhance workplace safe...

Multicenter development of a deep learning radiomics and dosiomics nomogram to predict radiation pneumonia risk in non-small cell lung cancer.

Radiation pneumonia (RP) is the most common side effect of chest radiotherapy, and can affect patien...

RadField3D: a data generator and data format for deep learning in radiation-protection dosimetry for medical applications.

In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, c...

Photon-counting CT in cancer radiotherapy: technological advances and clinical benefits.

Photon-counting computed tomography (PCCT) marks a significant advancement over conventional Energy-...

Does Whole Brain Radiomics on Multimodal Neuroimaging Make Sense in Neuro-Oncology? A Proof of Concept Study.

Employing a whole-brain (WB) mask as a region of interest for extracting radiomic features is a feas...

Automatic Segmentation of Histopathological Glioblastoma Whole-Slide Images Utilizing MONAI.

Manual segmentation of histopathological images is both resource-intensive and prone to human error,...

Machine learning for grading prediction and survival analysis in high grade glioma.

We developed and validated a magnetic resonance imaging (MRI)-based radiomics model for the classifi...

Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study.

BACKGROUND: Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by s...

Prognostic value of circadian rhythm-associated genes in breast cancer.

OBJECTIVE: Breast cancer (BC) remains the most prevalent malignancy among women. Clinical evidence i...

An MRI-based deep transfer learning radiomics nomogram for predicting meningioma grade.

The aim of this study was to establish a nomogram based on clinical, radiomics, and deep transfer le...

Enhancing Climate-Driven Urban Tree Cooling with Targeted Nonclimatic Interventions.

Urban trees play a pivotal role in mitigating heat, yet the global determinants and patterns of thei...

An automated cascade framework for glioma prognosis via segmentation, multi-feature fusion and classification techniques.

Glioma is one of the most lethal types of brain tumors, accounting for approximately 33% of all diag...

Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.

OBJECTIVE: The aim of this study was to evaluate various radiomics-based machine learning classifica...

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