Brain Cancer

Prospective biopsy-controlled validation of an AI model for predicting glioblastoma infiltration: Results from the SupraGlio trial.

BACKGROUND: Glioblastoma recurrence is driven by diffuse microscopic infiltration beyond the contrast-enhancing tumour margin. GlioMap is an open-access AI model predicting voxelwise infiltration and recurrence risk from multiparametric MRI. This prospective study aimed to validate GlioMap's biological accuracy and prognostic relevance through histopathological assessment, transcriptomic profiling...

Cauchy Lotus Optimization-based feature selection and ResNet 101 based XceptionNet architecture for radiation pneumonitis prediction in lung cancer patients.

Thoracic radiotherapy for lung cancer patients followed by radiation pneumonitis (RP) has significant clinical side effects. Risk-adaptive treatment planning can be supported by accurate early RP prediction. Using thoracic CT scans, this study suggests an efficient deep learning algorithm for RP prediction. An analysis was conducted on a retrospective cohort of 548 patients with lung cancer who re...

Use of Large Language Models to Enhance Failure Mode and Effects Analysis: A Case Study.

PURPOSE: Failure Mode and Effects Analysis (FMEA) is widely used in radiation oncology to proactively identify and mitigate risks, but it is time-cons...

A review of clinical trial radiotherapy quality assurance: value, evolution and lessons.

BACKGROUND AND PURPOSE: Deviations in radiotherapy quality can significantly affect clinical trial outcomes, including overall survival. Radiotherapy ...

Combined multi-omics and multi-spectral profiling of plasma extracellular vesicles reveals liquid biopsy biomarkers for glioma diagnosis.

Plasma small extracellular vesicles (sEVs) are a promising liquid biopsy tool. This study aims to delineate and validate a multimodal plasma sEV bioma...

Identifying environmental factors associated with tetrodotoxin contamination in bivalve mollusks using eXplainable AI.

Since 2012, tetrodotoxin (TTX) has been found in seafoods such as bivalve mollusks in temperate European waters. TTX contamination leads to food safet...

CEP-IP: An explainable framework for cell subpopulation identification in single-cell transcriptomics.

BACKGROUND AND OBJECTIVE: Single-cell RNA sequencing (scRNA-seq) frameworks lack explainable approaches for identifying cell subpopulations harboring ...

Clinically Localized Prostate Cancer: AUA/ASTRO Guideline Amendment (2026).

PURPOSE: Following completion of the 2025 Update Literature Review (ULR), the American Urological Association (AUA) incorporated new evidence generate...

Machine Learning-Based Synthetic Computed Tomography Derived From Temporal Bone Magnetic Resonance Imaging.

IMPORTANCE: For diagnostics and presurgical planning in otology, both magnetic resonance imaging (MRI) and computed tomography (CT) are frequently req...

X2Shape: CT-free 3D multi-organ reconstruction with biplanar X-rays.

Reconstructing three-dimensional (3D) anatomy from routine X-ray imaging remains a long-standing challenge, promising high accessibility and minimal r...

Contrastive multimodal deep learning for survival prediction in grade 2/3 gliomas.

BACKGROUND: Accurate survival prediction for grade 2/3 glioma patients remains challenging due to tumor biological heterogeneity and limitations of cu...

Brain tumors classification using electrical bioimpedance spectroscopy based on a multi-scale feature extraction network with frequency band attention mechanism.

Electrical bioimpedance (EBI) measurement provides insights into the biophysical properties of tissues, offering valuable information for tumor diagno...

Multimodal large language models in brain tumor imaging: clinical applications and future perspectives.

The use of multimodal data is essential for the precise diagnosis and treatment of brain tumors. In this context, multimodal data encompass multiseque...

AI-based classification of pediatric sagittal skeletal patterns using multi-view facial photographs.

OBJECTIVE: The increasing global demand to assess pediatric skeletal malocclusions poses a growing challenge, as current screening methods are depende...

Do AI-based contouring algorithms influence physicians in the online adaptive radiotherapy of patients with bladder cancer?

PURPOSE: In this prospective cross-over study, the precision of manual correction of the clinical target volume (CTV) during online-adaptive radiother...

A pipeline of machine learning-driven multi-modal data fusion methods for prognostic risk analysis in bevacizumab-treated metastatic colorectal cancer.

CT-Based Glioma Segmentation Using Deep Learning: Validation for Emergency Neuro-oncological Care.

Accurate and rapid delineation of diffuse gliomas is essential in emergency neuro-oncology, yet MRI is often unavailable. We present a deep-learning s...

A deep learning-based model for postoperative resection assessment in glioblastoma: A comparative study.

Digital twin applications in radiology and radiotherapy: Applications, challenges, and future perspectives.

Digital twin technology has emerged as a transformative innovation in healthcare, offering virtual replicas of physical entities at patient-level, equ...

Multiscale analysis and optimal glioma therapeutic candidate discovery using the CANDO platform.

Glioma is a highly malignant brain tumor with limited treatment options. We employed the Computational Analysis of Novel Drug Opportunities (CANDO) pl...