Breast Cancer

Uncovering Clinically Relevant Breast Cancer Subtypes Biomarkers Using Integrative Bioinformatics and Machine Learning Approaches.

A precise diagnosis and customized treatment become more difficult by the genomic heterogeneity of breast cancer (BRCA). In order to examine gene expression data from two separate Gene Expression Omnibus (GEO) microarray datasets, we used a integrative approach in this study that combined bioinformatics and machine learning. We were able to distinguish between universal and subtype-specific transc...

Machine Learning Models for Individualized Osteoradionecrosis Risk Prediction in Head and Neck Cancer.

To develop and validate predictive models for osteoradionecrosis (ORN) after head and neck radiation therapy (RT) using time-to-event data with death as the competing risk, and to quantify the degree of risk overestimation when the competing risk is ignored. In this prognostic study of patients who underwent curative RT between 2011 and 2018, with ongoing follow-up, sociodemographic, clinical, and...

Artificial intelligence-based quantification of breast arterial calcifications to predict cardiovascular morbidity and mortality.

BACKGROUND AND AIMS: Women are underdiagnosed and undertreated for cardiovascular disease (CVD). Automatic quantification of breast arterial calcifica...

Integrative network pharmacology and machine learning identify potential targets of indole-3-lactic acid in colorectal cancer.

The treatment of colorectal cancer (CRC) remains challenging due to chemotherapy resistance and genetic heterogeneity. Indole-3-lactic acid (ILA), a t...

Deep Learning Reconstruction on Quantitative Analysis in Brain Tumors With Diffusion-Weighted Imaging and Dynamic Susceptibility Contrast Imaging.

BACKGROUND: Although deep learning reconstruction (DLR) has been shown to improve image quality in MRI, its impact on quantitative physiologic paramet...

Multimodal Deep Learning-Based Screening of Degenerative Temporomandibular Joint Disease Using 2D Radiography: A Cost-Effective and Low-Radiation Approach.

OBJECTIVES: This study aimed to develop and preliminarily validate a multimodal deep learning model based on two-dimensional maxillofacial imaging for...

Enhancing adverse drug event extraction and summarization for cancer drugs through large language models.

PURPOSE: Cancer treatments such as chemotherapy, targeted therapy, and immunotherapy can effectively combat malignant cells but frequently cause serio...

The dual role of the crosstalk between autophagy and ferroptosis in lung cancer treatment: Advances in mechanisms and therapeutic strategies (Review).

The interaction between autophagy and ferroptosis has resulted in the identification of novel approaches for the treatment of lung cancer (LC). The tw...

Integrated analysis of therapeutic strategies and prognostic factors in advanced lung adenocarcinoma: Retrospective study with emphasis on gene assays, multimodality treatment approaches and predictive machine learning models.

Patients with advanced lung adenocarcinoma have a range of treatment options, including targeted therapy and gene assay-guided chemotherapy. The aim o...

Artificial Intelligence for T classification of TNM breast cancer in MRI imaging: Enabling Precision in Treatment Decisions.

The integration of artificial intelligence (AI) into breast cancer management presents transformative potential for both diagnosis and treatment plann...

Prediction of Ki-67 expression in invasive breast cancer with dual-modality radiomics.

Ki-67 expression, a critical biomarker for tumor aggressiveness and proliferation in invasive breast cancer, is traditionally assessed via invasive bi...

Automatic Hepatic Steatosis Quantification using Low-Dose CT with deep learning-based noise reduction and CT Fat Fraction Analysis Software.

OBJECTIVES: To evaluate the accuracy of CT-derived fat fraction (CDFF) software for quantifying hepatic steatosis at various radiation doses, using MR...

Intelligent model for stratification of acute radiation syndrome severity in humans.

PURPOSE: The aim of this study was to develop and compare two intelligent model for stratifying the severity of acute radiation syndrome (ARS) in huma...

Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.

Glioblastoma (GB), the most aggressive primary brain tumor, is characterized by profound inter- and intratumoral heterogeneity and a highly immunosupp...

Early electrocardiographic repolarization changes are associated with subclinical cancer therapy-related cardiac dysfunction in lymphoma patients: A machine learning-assisted longitudinal study.

UNLABELLED: Anthracycline-induced cardiotoxicity remains a significant clinical challenge. We evaluated longitudinal electrocardiographic (ECG) repola...

Neoadjuvant Systemic Therapy in Kidney and Bladder Cancer: Current Evidence and Emerging Paradigms.

Neoadjuvant systemic therapy has emerged as a strategy to improve outcomes in high-risk localized genitourinary malignancies. In bladder cancer, neoad...

A scoping review of radiation dose and image quality in paediatric CT: Towards safe imaging protocols and diagnostic reference levels in Jordan.

BACKGROUND: Computed tomography (CT) is an essential diagnostic tool, but its associated radiation exposure raises significant concerns, especially fo...

Minimum Clinically Achievable Dose for Detecting Liver Lesions Using Deep Learning Image Reconstruction: A Phantom and Patient Study.

RATIONALE AND OBJECTIVES: To investigate the performance of deep learning image reconstruction (DLIR) at an ultra-low dose of approximately 4.5 mGy fo...

AI-based BRAIx risk score for the intermediate-term prediction of breast cancer: a population cohort study.

BACKGROUND: Artificial intelligence (AI)-based algorithms are being implemented in breast screening to detect breast cancers on mammographic images. W...

Contactless and Wireless Wound Monitoring Using Nitrogen-Doped Graphene Antenna Sensor.

Quantitative remote wound monitoring has the potential to shorten patient recovery time and alleviate the workload of healthcare professionals. In thi...