Brain Cancer

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) remains unclear. Here, we employed 10 machine learning algorithms to develop a stable, accurate TEP-related gene signature (TEPGS) to explore its links to tumor-associated macrophages (TAMs) and spatial platelet abundance. TEPGS correlated strongly...

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 glioma and externally validate the predictive performance of the model. METHODS: This study retrospectively collected the clinical and imaging data from 421 patients with grade 2-4 gliomas who underwent surgical resection or biopsy and were pathologicall...

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...

Unimodal vs. multimodal deep learning for non-invasive MGMT promoter methylation prediction in glioblastoma: A systematic evaluation on the BraTS 2021 dataset.

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor in adults, with a median survival of 14.6 months under standard radiotherapy ...

Deep learning applications in cancer treatment Prediction: Comprehensive research foundation for systematic review and Meta-Analysis.

Deep learning has rapidly emerged as a transformative technology in oncology, offering new capabilities in treatment response prediction and personali...

Deep learning-based automatic segmentation of rectal tumors in endoscopic images.

PURPOSE: Endoscopy is critical in the identification of rectal tumors, but is prone to observer errors. The aim of this study was to assess the inter-...

T2Pdecoder enables protein-centric analyses from transcriptomic data.

Protein quantification is not as extensive as RNA quantification, especially for isocitrate dehydrogenase (IDH) mutant gliomas. Predicting protein abu...

Pre-operative MRI-Based Radiomics for Predicting Telomerase Reverse Transcriptase Promoter Mutation Status in Glioma Patients: A Systematic Review and Meta-analysis.

TERT promoter (TERTp) mutations shape glioma prognosis and therapy, yet tissue testing can be limited by sampling error and surgical inaccessibility. ...

Simulation-Enhanced Learning in Nuclear Medicine: Theory, Modalities, and Applications Across the Training Continuum.

Simulation-based education has evolved into a foundational component of nuclear medicine technologist training, driven by increasing procedural comple...