Brain Cancer

Segmenting with confidence through uncertainty quantification for brain tumor imaging.

A major barrier to clinical adoption of artificial intelligence (AI) for brain tumor monitoring is the lack of calibrated uncertainty in automated segmentation, limiting clinician trust. We developed a deep learning framework that generates uncertainty estimates for meningioma segmentation on brain MRI. Evidential deep learning ensembles were trained on 1655 post-contrast T1-weighted MRIs (788 pat...

Identifying predictive hematological biomarkers for radiation exposure by machine learning in mouse models.

BACKGROUND: Population-scale radiation exposure assessment during radiological emergencies is hindered by the slow and costly nature of current methods, creating a need for rapid, affordable screening tools. Radiation biodosimetry using peripheral blood counts is a promising approach, but estimating low-dose exposures and exposure at extended time points remains challenging, especially when accoun...

Frontiers in Raman nano-diagnostics: SERS and TERS of extracellular vesicles for cancer detection.

Efforts to create rapid, non-invasive, and reliable cancer diagnostics have increasingly focused on extracellular vesicles (EVs), nanoscale carriers o...

[Reirradiation: A new therapeutic paradigm in oncology].

Reirradiation (reRT) has become an essential therapeutic option for selected patients with locoregional recurrences, when surgery or systemic therapie...

Deep survival analysis in multimodal medical data: a parametric and probabilistic approach with competing risks.

Accurate survival prediction is critical in oncology for prognosis and treatment planning. Traditional approaches often rely on a single data modality...

Inferring translational efficiency from transcriptomes improves noncanonical neoantigen prioritization and cancer patient stratification.

Accurate assessment of protein translation is crucial for understanding disease variant functions, but mRNA-protein discrepancy limits transcriptomics...

Machine learning algorithms develop a tumor-educated platelets-related gene signature to predict colorectal cancer prognosis and therapy response.

Tumor-educated platelets (TEPs) have recently emerged as an important component of liquid biopsy, yet the clinical relevance in colorectal cancer (CRC...

A transformer-based multimodal deep learning model for preoperative prediction of Ki-67 expression level in glioma.

PURPOSE: The aim of this study is to develop a deep learning model using preoperative multimodal MR data to predict the Ki-67 expression level of glio...

A functionally guided fusion Vision Transformer for predicting IDH status in gliomas: a multicenter study with external validation and incomplete multimodal evaluation.

BACKGROUND: Accurate preoperative prediction of isocitrate dehydrogenase (IDH) genotype in gliomas is crucial for treatment planning and prognostic ev...

Deep learning based time series analysis for breathing phase prediction in phase-gated proton therapy.

OBJECTIVE: Phase gating is a critical technique to mitigate tumor motion during radiotherapy, particularly in spot-scanned particle therapy (SSPT) whe...

EDRNet-based adolescent idiopathic scoliosis screening method using bare-back images.

BACKGROUND: Adolescent idiopathic scoliosis (AIS) affects 2-3% of adolescents. Current screening relies on X-rays, which limits large-scale applicatio...

Ensembling Unets for Rare Chromosomal Aberration Detection in Metaphase Images, Uncertainty Quantification, and Ionizing Radiation Dose Estimation.

In biological dosimetry a radiation dose is estimated using the average number of chromosomal aberrations per peripheral blood lymphocytes. This analy...

Abdominal Dual-Energy CT with Deep Learning Reconstruction: Preserving Image Quality and Hepatic Lesion Conspicuity with Reduced Radiation and Contrast.

The objective was to evaluate the image quality and hepatic lesion conspicuity in a dual-low-dose (radiation and contrast volume) upper abdominal dual...

Causal multi-fidelity surrogate forward and inverse models for ICF implosions.

Continued progress in inertial confinement fusion (ICF) requires solving inverse problems relating experimental observations to simulation input param...

Integrating machine learning and retrospective cohort to explore the effects of AEDs on the prognosis of LGG and identify related molecular targets.

Antiepileptic drugs (AEDs) were frequently employed in glioma patients, especially those with low-grade glioma (LGG), in which epilepsy manifested in ...

A myeloid immunosuppressive phenotype defines primary refractoriness to atezolizumab Plus bevacizumab in hepatocellular caarcinoma.

BACKGROUND: Despite improved outcomes with atezolizumab plus bevacizumab (A+B) in hepatocellular carcinoma (HCC), primary refractoriness (PRef), chara...

A Review of the Australian MRI Linac Program: From Pie in the Sky to Research Milestone.

The Australian Magnetic Resonance Imaging (MRI) Linear Accelerator program (MRI linac) was a major research project that aimed to build and test a uni...

Spatial-temporal recurrence patterns of grade 4 glioma using deep learning integrated multiparametric MRI and molecular pathology.

Grade 4 glioma is inherently lethal due to inevitable recurrence. Current radiotherapy guidelines recommend uniform target volume margins, disregardin...

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lu...

Novel method for risk stratification of radiation-induced breast fibrosis: subgroup hypothesis verified by machine learning.

Breast fibrosis (BF) after radiotherapy remains one of the most dreaded late toxicities in breast cancer care, yet multiple additive predictors strugg...