Oncology/Hematology

INHBA: a mitochondrial-related pan-cell death gene associated with the prognosis and immunity of OSCC.

Oral squamous cell carcinoma (OSCC) is a malignant tumor characterized by both clinical and molecular heterogeneity. Its rising global incidence and mortality rates pose considerable clinical challenges. Accumulating evidence suggests that dysregulation of mitochondria-associated pan-cell death, a process integrating multiple mitochondrial-related cell death modalities, is closely linked to tumor ...

Interpretable machine learning models using peripheral blood biomarkers for diagnosis and prognosis of glottic laryngeal squamous cell carcinoma.

Improving acute lymphoblastic leukemia diagnosis through CBAM-enhanced VGG19 deep learning.

Acute Lymphoblastic Leukemia (ALL) is an aggressive blood cancer that requires rapid and accurate diagnosis. Manual review of blood and bone marrow sm...

Clinical AI Applications in the Context of Cancer: Explaining Openness to Information and Consent to Use Through Risk Perceptions and Efficacy Beliefs.

The use of artificial intelligence (AI) in oncology holds immense potential for improving diagnostic accuracy and treatment outcomes. Yet, concerns ab...

Leveraging infarct topography for early warning: a robust model for predicting malignant cerebral edema after endovascular treatment in acute ischemic stroke.

BACKGROUND: The early prediction of malignant cerebral edema (MCE) following endovascular therapy for acute ischemic stroke is of paramount importance...

Development and interpretability of an XGBoost model to predict high grade cervical intraepithelial neoplasia.

TRKB-based signature identifies high-risk squamous cell carcinoma cases and TRKB blockade reprograms tumor and stromal cells toward suppressive phenotypes.

BACKGROUND: Cutaneous squamous cell carcinoma (cSCC) is a common age-related cancer, with a subset prone to recurrence and metastasis. Currently, no u...

Ultrasound-Responsive Serotonylation and Hallmark Pathway Gene Signatures Reveal Tumor Microenvironment Vulnerabilities and Prognostic Subtypes in Colorectal Cancer.

BACKGROUND: Focused ultrasound, low-intensity focused ultrasound, and microbubble-enhanced sonoporation are examples of ultrasound-based cancer therap...

A machine learning model integrating circulating Temra cell transcriptional profiles to predict immunotherapy efficacy.

BACKGROUND: While immunotherapy has transformed treatment paradigms for cancer, durable response rates remain below 30%. Currently available biomarker...

Targeting metabolic vulnerabilities with advanced delivery systems.

Metabolism modulation has emerged as a promising frontier in precision oncology. Nonetheless, the primary gap is the inability to precisely target the...

Predictive machine learning algorithms for depression and anxiety disorders in six cancer types: a comprehensive multi-center population-based study.

OBJECTIVE: Machine learning (ML) has advanced predictive modeling in medical diagnosis and risk assessment through large clinical datasets, yet applic...

[Utilizing machine learning and SHAP analysis to develop a prognostic survival prediction model for gastric cancer patients following radical gastrectomy].

Objective: To construct a prediction model and website for the overall survival (OS) of gastric cancer patients after radical gastrectomy based on SHa...

Nomogram based on the pre-treatment systemic oxidative stress score to predict the prognosis of surgically treated patients with oral squamous cell carcinoma: competing risk model analysis.

The aim of this study was to explore the prognostic predictive importance of the pre-treatment systemic oxidative stress score (SOS) in surgically tre...

Advanced computational analysis in metagenomic studies to support precision medicine.

BACKGROUND: The human microbiome has been linked to host health and is suggested to play a direct role in the onset of certain human diseases, as well...

Deep Learning-Based Diagnostic Model for Ocular Surface Neoplastic Diseases.

PURPOSE: To develop a deep learning (DL) model for diagnosing ocular surface tumors and evaluating its diagnostic performance. SETTING: Development of...

EASDnet: Empowering human-centered evidence-based medicine through an evidence and attention-based spatial disparity network for discriminative colorectal cancer histopathological screening and attribution.

PURPOSE: Accurate preoperative staging of colorectal cancer is critical to guide treatment decisions, including eligibility for R0 resection, to reduc...

Machine learning derived proliferating T cell-related signature: a novel biomarker for prognosis and treatment efficacy in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) exhibits profound molecular heterogeneity, and reliable biomarkers for predicting clinical outcome...

Emily vs. Lakisha: Quantifying socioeconomic and structural disparities in AI-generated ovarian cancer care plans.

BACKGROUND: Rapid integration of Large Language Models (LLMs) into oncology workflows mandates scrutiny of implicit biases that may exacerbate health ...

Integrative transcriptomic and machine learning analysis identifies CDH17 and HOXC13 as robust candidate prognostic biomarkers in uveal melanoma.

BACKGROUND: Uveal melanoma (UM), the most common form of ocular melanoma, represents poor prognosis, with approximately 50% of patients developing met...

An AI-based IHC quantification technique for assisting in the differentiation of MCL from CLL/SLL.

This study explores the application of artificial intelligence technology for the quantitative analysis of immunohistochemical markers to differentiat...