Brain Cancer

CXCL12 impact on glioblastoma cells behaviors under dynamic culture conditions: Insights for developing new therapeutic approaches.

Glioblastoma multiforme (GBM) is the most prevalent malignant brain tumor, with an average survival time of 14 to 20 months. Its capacity to invade brain parenchyma leads to the failure of conventional treatments and subsequent tumor recurrence. Recent studies have explored new therapeutic strategies using a chemoattracting gradient to attract GBM cells into a soft hydrogel trap where they can be ...

VASARI 2.0: a new updated MRI VASARI lexicon to predict grading and status in brain glioma.

INTRODUCTION: Precision medicine refers to managing brain tumors according to each patient's unique characteristics when it was realized that patients with the same type of tumor differ greatly in terms of survival, responsiveness to treatment, and toxicity of medication. Precision diagnostics can now be advanced through the establishment of imaging biomarkers, which necessitates quantitative imag...

Improving prediction of solar radiation using Cheetah Optimizer and Random Forest.

In the contemporary context of a burgeoning energy crisis, the accurate and dependable prediction of Solar Radiation (SR) has emerged as an indispensa...

Artificial Intelligence-Empowered Multistep Integrated Radiation Therapy Workflow for Nasopharyngeal Carcinoma.

PURPOSE: To establish an artificial intelligence (AI)-empowered multistep integrated (MSI) radiation therapy (RT) workflow for patients with nasophary...

Deep learning radiomics nomograms predict Isocitrate dehydrogenase (IDH) genotypes in brain glioma: A multicenter study.

PURPOSE: To explore the feasibility of Deep learning radiomics nomograms (DLRN) in predicting IDH genotype.

Can knowledge-based planning models validated on ethnically diverse patients lead to global standardisation of external beam radiation therapy for locally advanced cervix cancer?

BACKGROUND AND PURPOSE: Knowledge-based planning (KBP) can consistently and efficiently create high-quality Volumetric Arc Therapy (VMAT) plans for ce...

Generating 3D brain tumor regions in MRI using vector-quantization Generative Adversarial Networks.

Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Netwo...

Prognostic models for progression-free survival in atypical meningioma: Comparison of machine learning-based approach and the COX model in an Asian multicenter study.

BACKGROUND AND PURPOSE: Atypical meningiomas are prevalent intracranial tumors with varied prognoses and recurrence rates. The role of adjuvant radiot...

Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current.

BACKGROUND: The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular a...

Improving the prediction of patient survival with the aid of residual convolutional neural network (ResNet) in colorectal cancer with unresectable liver metastases treated with bevacizumab-based chemotherapy.

BACKGROUND: To verify overall survival predictions made with residual convolutional neural network-determined morphological response (ResNet-MR) in pa...

Convolutional neural network-assisted Raman spectroscopy for high-precision diagnosis of glioblastoma.

Glioblastoma multiforme (GBM) is the most lethal intracranial tumor with a median survival of approximately 15 months. Due to its highly invasive prop...

Proximity adjusted centroid mapping for accurate detection of nuclei in dense 3D cell systems.

In the past decade, deep learning algorithms have surpassed the performance of many conventional image segmentation pipelines. Powerful models are now...

Machine learning for predicting post-operative outcomes in meningiomas: a systematic review and meta-analysis.

PURPOSE: Meningiomas are the most common primary brain tumour and account for over one-third of cases. Traditionally, estimations of morbidity and mor...

A Fusion Model of MRI Deep Transfer Learning and Radiomics for Discriminating between Pilocytic Astrocytoma and Adamantinomatous Craniopharyngioma.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a fusion model combining MRI deep transfer learning (DTL) and radiomics for discrim...

Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans.

Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for understanding tumor growth dynamics and designing personalized...

Stress testing deep learning models for prostate cancer detection on biopsies and surgical specimens.

The presence, location, and extent of prostate cancer is assessed by pathologists using H&E-stained tissue slides. Machine learning approaches can acc...

Classification of speech arrests and speech impairments during awake craniotomy: a multi-databases analysis.

PURPOSE: Awake craniotomy presents a unique opportunity to map and preserve critical brain functions, particularly speech, during tumor resection. The...

Enhancing brain tumor classification by integrating radiomics and deep learning features: A comprehensive study utilizing ensemble methods on MRI scans.

BACKGROUND AND OBJECTIVE: This study aims to assess the effectiveness of combining radiomics features (RFs) with deep learning features (DFs) for clas...

Tunable and real-time automatic interventional x-ray collimation from semi-supervised deep feature extraction.

BACKGROUND: The use of endovascular procedures is becoming increasingly popular across multiple clinical domains. These procedures are generally perfo...

Deep learning-based overall survival prediction in patients with glioblastoma: An automatic end-to-end workflow using pre-resection basic structural multiparametric MRIs.

PURPOSE: Accurate and automated early survival prediction is critical for patients with glioblastoma (GBM) as their poor prognosis requires timely tre...