Oncology/Hematology

Effects of concurrent HER2-directed therapy on development of cerebral radionecrosis after stereotactic radiotherapy: a systematic review.

PURPOSE: With increasing use of human epithelial growth factor receptor two (HER2)-targeted therapies, outcomes for numerous breast cancer patients have improved. Nevertheless, patients with HER2-positive tumours face a comparatively heightened risk for developing brain metastases (BM), which are often treated with stereotactic radiosurgery (SRS). Radionecrosis represents one of the clinically mos...

Molecular biomarkers for the prognosis of breast cancer: role of amino acid metabolism genes.

The development of precise molecular biomarkers for breast cancer prognosis holds immense potential to improve treatment outcomes. This study aimed to investigate the role of amino acid metabolism genes as predictive markers for breast cancer prognosis and their association with the immune-tumour microenvironment. By employing advanced machine learning algorithms and bioinformatics analysis techni...

Use of natural language processing to identify inflammatory breast cancer cases across a healthcare system.

Early identification and referral of inflammatory breast cancer (IBC) remains challenging within large healthcare systems, limiting access to speciali...

Predicting atezolizumab response in metastatic urothelial carcinoma patients using machine learning on integrated tumour gene expression and clinical data.

Atezolizumab is a treatment for metastatic urothelial carcinoma (mUC), yet only 23% of mUC patients benefit from it. Worse yet, accurately predicting ...

Preoperative prediction model for benign and malignant gallbladder polyps on the basis of machine-learning algorithms.

BACKGROUND: This study aimed to differentiate between benign and malignant gallbladder polyps preoperatively by developing a prediction model integrat...

Thalamic neural activity and epileptic network analysis using stereoelectroencephalography: a prospective study protocol.

INTRODUCTION: Epilepsy is a prevalent chronic neurological disorder, with approximately one-third of patients experiencing intractable epilepsy, often...

Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging.

Accurate brain tumor classification is essential in neuro-oncology, as it directly informs treatment strategies and influences patient outcomes. This ...

Deep learning and inflammatory markers predict early response to immunotherapy in unresectable NSCLC: A multicenter study.

Immune checkpoint inhibitors (ICIs) demonstrate substantial interpatient variability in clinical efficacy for unresectable non-small cell lung cancer ...

Identification of DSC2 as a Key Biomarker for Induction Chemotherapy Sensitivity in Locally Advanced Laryngeal and Hypopharyngeal Squamous Cell Carcinoma.

Patients with locally advanced laryngeal and hypopharyngeal squamous cell carcinoma (LA-LHSCC) urgently need precise treatment strategies to improve t...

Integrated muti-omics data and machine learning reveal CD151 as a key biomarker inducing chemoresistance in metabolic syndrome-related early-onset left-sided colorectal cancer.

Emerging evidence has suggested a potential pathological association between early-onset left-sided colorectal cancer (EOLCC) and metabolic syndrome (...

Artificial Intelligence for Head and Neck Squamous Cell Carcinoma: From Diagnosis to Treatment.

Head and neck squamous cell carcinoma (HNSCC) remains a globally prevalent malignancy with high morbidity and mortality. Despite therapeutic advances,...

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 possib...

Multimodal Artificial Intelligence Using Endoscopic USG, CT, and MRI to Differentiate Between Serous and Mucinous Cystic Neoplasms.

Introduction Serous cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) often exhibit similar imaging features when evaluated with a single ima...

Single-cell sequencing and machine learning reveal the role of dioxin-interacting genes in HCC prognosis and immune microenvironment.

Dioxins are persistent environmental pollutants that bioaccumulate in the food chain, posing significant risks to human health. Despite their low envi...

Where Can Artificial Intelligence Assist Cancer Care?: Examining Patient-Centered Communication Dimension Effects.

OBJECTIVE: To explore how aspects of patient-centered communication (PCC) may directly or indirectly predict patients' preferences for artificial inte...

The value of intratumoral and peritumoral ultrasound radiomics model constructed using multiple machine learning algorithms for non-mass breast cancer.

To investigate the diagnostic capability of multiple machine learning algorithms combined with intratumoral and peritumoral ultrasound radiomics model...

Machine learning method based on radiomics help differentiate posterior pituitary tumors from pituitary neuroendocrine tumors and craniopharyngioma.

Posterior pituitary tumors (PPTs) are rare neoplasms, but easily misdiagnosed as pituitary neuroendocrine tumor (PitNET) and craniopharyngioma. This s...

Histopathologic deep learning model for predicting tumor response to hepatic arterial infusion chemotherapy plus TKIs and ICIs in large hepatocellular carcinoma.

BACKGROUND: While triplet therapy (HTI), which combines hepatic arterial infusion chemotherapy (HAIC) with tyrosine kinase inhibitors and immune check...

Neurodegeneration Promotes Tumorigenesis in Colorectal Cancer: Insights From Single-Cell and Spatial Multiomics.

PURPOSE: Colorectal cancer (CRC) ranks third in global incidence and second in mortality, with rates increasing among younger populations. The enteric...

Automated Brain Tumor Classification and Grading Using Multi-scale Graph Neural Network with Spatio-Temporal Transformer Attention Through MRI Scans.

The medical field uses Magnetic Resonance Imaging (MRI) as an essential diagnostic tool which provides doctors non-invasive images of brain structures...