Brain Cancer

Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.

The high-resolution three-dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possibilities for early disease diagnosis. Early detection is especially important as the incidence of breast cancer increases. However, DBT also presents challenges in terms of poorer results for dense breasts, increased false positive rates, slightly hig...

Machine learning and multi-omics analysis reveal key regulators of proneural-mesenchymal transition in glioblastoma.

Glioblastoma (GBM) is classified into subtypes according to the molecular expression profile; the proneural subtype has a relatively good prognosis, and the mesenchymal type is the most aggressive form with the worst prognosis. GBM undergoes proneural-mesenchymal transition (PMT) during its evolution or in response to changes in the microenvironment or therapeutic interventions. PMT is accompanied...

Screening of glioma susceptibility SNPs and construction of risk models based on machine learning algorithms.

BACKGROUND: Glioma is a common primary malignant brain tumor. This study aimed to develop a predictive model for glioma risk by these screened key SNP...

Predicting therapeutic clinical trial enrollment for adult patients with low- and high-grade glioma using supervised machine learning.

Therapeutic clinical trial enrollment does not match glioma incidence across demographics. Traditional statistical methods have identified independent...

Construction and validation of a prognostic nomogram model integrating machine learning-pathomics and clinical features in IDH-wildtype glioblastoma.

BACKGROUND: Novel diagnostic criteria for glioblastoma (GBM) in the 2021 WHO classification emphasize the importance of integrating pathological and m...

Current trends in glioma tumor segmentation: A survey of deep learning modules.

BACKGROUND: Multiparametric Magnetic Resonance Imaging (mpMRI) is the gold standard for diagnosing brain tumors, especially gliomas, which are difficu...

Current AI technologies in cancer diagnostics and treatment.

Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, an...

Monte Carlo and Machine Learning-Based Evaluation of Fe-Enriched Al Alloys for Nuclear Radiation Shielding Applications.

This study presents a hybrid computational investigation into the radiation shielding behavior of Fe-enriched Al-based alloys (Al-Fe-Mo-Si-Zr) for pot...

The Integration of Artificial Intelligence Into Precision Medicine for Neuro-Oncology: Ethical, Clinical, and Nursing Implications in Immunotherapy Care.

This paper explores how artificial intelligence (AI) is being woven into precision medicine for neuro-oncology, highlighting its ethical, clinical, an...

Immunological biomarkers and gene signatures predictive of radiotherapy resistance in non-small cell lung cancer.

INTRODUCTION: A significant challenge in treating non-small cell lung cancer (NSCLC) is its inherent resistance to radiation therapy, leading to poor ...

Comparative evaluation of deep learning-based and conventional reconstruction techniques for image quality enhancement in low-dose chest computed tomography.

BACKGROUND: With the increasing use of computed tomography (CT) in clinical practice, greater attention is being paid to the radiation dose. There are...

Rapid diagnosis of TERT promoter mutation using Terahertz absorption spectroscopy in glioblastoma.

Glioblastoma (GBM) is a highly aggressive brain tumor with poor outcomes and limited treatment options. The telomerase reverse transcriptase (TERT) pr...

Deep Learning Auto-segmentation of Diffuse Midline Glioma on Multimodal Magnetic Resonance Images.

Diffuse midline glioma (DMG) H3 K27M-altered is a rare pediatric brainstem cancer with poor prognosis. To advance the development of predictive models...

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

PURPOSE: To develop and validate interpretable machine learning models for differentiating glioblastoma (GB) from solitary brain metastasis (SBM) usin...

Regression and machine learning approaches identify potential risk factors for glioblastoma multiforme.

Glioblastoma multiforme is a lethal disease, with a 5-year survival rate of <10%. The identification of risk factors for glioblastoma multiforme is es...

Validity and Reliability of an Artificial Intelligence-Based Posture Estimation Software for Measuring Cervical and Lower-Limb Alignment Versus Radiographic Imaging.

: Accurate postural assessment is essential for managing musculoskeletal disorders; however, routine screening is often limited by radiation exposure,...

Explainable machine learning and feature interpretation to predict survival outcomes in the treatment of lung cancer.

The treatment outcomes of lung cancer are highly variable, and machine learning (ML) models provide valuable insights into how clinical and biochemica...

Multimodal Explainable Artificial Intelligence for Prognostic Stratification of Glioblastoma Patients.

Glioblastoma is the most common and aggressive malignant adult tumor of the central nervous system, with a grim prognosis and heterogeneous morphologi...

Integrative machine learning and bioinformatics analysis unveil key genes for precise glioma classification and prognosis evaluation.

Gliomas exhibit significant heterogeneity and diverse molecular subtypes, and there are marked differences in treatment strategies and prognoses for g...

Deep learning and iterative image reconstruction for head CT: Impact on image quality and radiation dose reduction-Comparative study.

This study focuses on an objective evaluation of a novel reconstruction algorithm-Deep Learning Image Reconstruction (DLIR)-ability to improve image ...