AIMC Topic: Brain Neoplasms

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Assessing the toxicological impact of DEGDB plasticizer exposure on glioblastoma multiforme via network toxicology, machine learning and in vitro methods.

Environmental pollution (Barking, Essex : 1987)
Diethylene glycol dibenzoate (DEGDB) is a novel environmentally friendly plasticizer. However, toxicological studies on DEGDB remain limited, and its potential harmful effects on the malignant progression of glioblastoma are still unclear. Further sy...

A predictive quality assurance model for patient-specific gamma passing rate of hyperarc-based stereotactic radiotherapy and radiosurgery of brain metastases.

Journal of applied clinical medical physics
OBJECTIVE: Measurement-based patient specific quality assurance (PSQA) is an increasingly debated topic among medical physicists. Developments like online adaptive radiotherapy and same-day stereotactic treatments limit the time to do measurement-bas...

Accurate Paediatric Brain Tumour Classification Through Improved Quantitative Analysis of H MR Imaging and Spectroscopy.

NMR in biomedicine
Multimodality imaging is an emerging research topic in neuro-oncology for its potential of being able to demonstrate tumours in a more comprehensive manner. Diffusion-weighted magnetic resonance imaging (dMRI) and proton magnetic resonance spectrosco...

Post-hoc eXplainable AI methods for analyzing medical images of gliomas (- A review for clinical applications).

Computers in biology and medicine
Deep learning (DL) has shown promise in glioma imaging tasks using magnetic resonance imaging (MRI) and histopathology images, yet their complexity demands greater transparency in artificial intelligence (AI) systems. This is noticeable when users mu...

Improving risk assessment of local failure in brain metastases patients using vision transformers - A multicentric development and validation study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: This study investigates the use of Vision Transformers (ViTs) to predict Freedom from Local Failure (FFLF) in patients with brain metastases using pre-operative MRI scans. The goal is to develop a model that enhances risk stra...

Ensemble-based Convolutional Neural Networks for brain tumor classification in MRI: Enhancing accuracy and interpretability using explainable AI.

Computers in biology and medicine
BACKGROUND: Accurate and efficient classification of brain tumors, including gliomas, meningiomas, and pituitary adenomas, is critical for early diagnosis and treatment planning. Magnetic resonance imaging (MRI) is a key diagnostic tool, and deep lea...

Assessment of outcomes and machine Learning-based models to predict local failure risk following stereotactic radiosurgery for small brain metastases.

Journal of neuro-oncology
INTRODUCTION: We assessed the outcomes of stereotactic radiosurgery (SRS) for small intact brain metastases (SBM) (≤ 2 cm) and developed machine learning (ML) algorithms to predict the probability of local failure (LF).

Effects of concurrent HER2-directed therapy on development of cerebral radionecrosis after stereotactic radiotherapy: a systematic review.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
PURPOSE: With increasing use of human epithelial growth factor receptor two (HER2)-targeted therapies, outcomes for numerous breast cancer patients have improved. Nevertheless, patients with HER2-positive tumours face a comparatively heightened risk ...

Symbolic and hybrid AI for brain tissue segmentation using spatial model checking.

Artificial intelligence in medicine
Segmentation of 3D medical images, and brain segmentation in particular, is an important topic in neuroimaging and in radiotherapy. Overcoming the current, time consuming, practise of manual delineation of brain tumours and providing an accurate, exp...

Interpretable Machine Learning Models for Differentiating Glioblastoma From Solitary Brain Metastasis Using Radiomics.

Academic radiology
PURPOSE: To develop and validate interpretable machine learning models for differentiating glioblastoma (GB) from solitary brain metastasis (SBM) using radiomics features from contrast-enhanced T1-weighted MRI (CE-T1WI), and to compare the impact of ...