Leukemia

Diagnosis of leukemia using microarray analysis based on Hidden Markov Model and Random Convolutional Kernel Transform.

INTRODUCTION: Leukemia is one of the most prevalent cancers worldwide, and early detection is critical for effective treatment. Microarray data is a key tool in this process, given the vast number of genes involved, which makes the analysis complex and time-consuming. Identifying relevant genes is a crucial step in disease diagnosis. MATERIAL AND METHODS: This study aims to improve the diagnostic ...

Harnessing biomarkers to guide immunotherapy in esophageal cancer: toward precision oncology.

Esophageal cancer (EC) is one of the most serious health issues around the world, ranking seventh among the most lethal types of cancer and eleventh among the most common types of cancer worldwide. Traditional therapies-such as surgery, chemotherapy, and radiation therapy-often yield limited success, especially in the advanced stages of EC, prompting the pursuit of novel and more effective treatme...

Real-Time Super-Resolution Ultrasound Imaging for Monitoring Tumor Response During Intensive Care Management of Oncologic Emergencies.

BACKGROUND: Oncologic emergencies in critically ill cancer patients frequently require rapid, real-time assessment of tumor responses to therapeutic i...

Accurately Predicting Cell Type Abundance from Spatial Histology Image Through HPCell.

Recent advancements in spatial transcriptomics (ST) have revolutionized our ability to simultaneously profile gene expression, spatial location, and t...

Residual disease in NPM1-mutated acute myeloid leukemia.

Acute myeloid leukemia (AML) represents a genetically heterogeneous malignancy, with mutations in the nucleophosmin-1 (NPM1) gene identified as the mo...

Novel risk calculator predicting long-term survival in pediatric versus adult patients diagnosed with spinal low-grade glioma: a nationwide analysis.

BACKGROUND CONTEXT: Spinal low-grade gliomas (SLGGs) are rare, slow-growing central nervous system tumors affecting both pediatric and adult populatio...

Utilizing a combined approach of machine learning and structure-based drug design principles to identify potential hits targeting SphK1.

Sphingosine kinase (SphK1) is acrucial enzyme that aids in the processing of sphingolipids by adding a phosphate group to sphingosine, converting it i...

Efficient lightweight CNN for automated classification of B-cell acute lymphoblastic leukemia.

B-cell acute lymphoblastic leukemia (B-ALL) is an aggressive hematological malignancy that primarily affects children but can also occur in adults, pr...

Automating the Referral of Bone Metastases Patients With and Without the Use of Large Language Models.

BACKGROUND AND OBJECTIVES: Bone metastases, affecting more than 4.8% of patients with cancer annually, and particularly spinal metastases require urge...

AI Prognostication in Nonsmall Cell Lung Cancer: A Systematic Review.

The systematic literature review was performed on the use of artificial intelligence (AI) algorithms in nonsmall cell lung cancer (NSCLC) prognosticat...

Crafting an innovative one health-aligned machine learning framework for neglected tropical diseases elimination.

INTRODUCTION: Strategic investment in new interventions is crucial for controlling and eliminating NTDs. However, selecting the optimal intervention c...

A Scoping Review on AI-Supported Interventions for Non-Pharmacological Management of Chronic Rheumatic Diseases.

This review summarizes AI-supported non-pharmacological interventions for adults with chronic rheumatic diseases, detailing their components, purpose,...

Machine learning modeling and analysis of prognostic hub genes in cervical adenocarcinoma: a multi target therapy for enhancement in immunosurveillance.

Endocervical adenocarcinoma (ECA) the fatal and intrusive subtype of cervical carcinoma is on rise from the last decade. Its improper detection leads ...

A Network-Driven Framework for Drug Response Precision Prediction of Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is a clonal malignancy of myeloid progenitor cells that demonstrates highly variable responses to current regimens, highl...

AI based natural inhibitor targeting RPS20 for colorectal cancer treatment using integrated computational approaches.

The increasing global incidence of cancer emphasizes the vital role of machine learning algorithms and artificial intelligence (AI) in identifying nov...

Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy.

Breast cancer continues to be a leading cause of death among women in the world. The prediction of survival outcomes based on treatment modalities, i....

Single-cell spatial transcriptomics reveals immunotherapy-driven bone marrow niche remodeling in AML.

Given the graft-versus-leukemia effect observed with allogeneic hematopoietic stem cell transplantation in refractory or relapsed acute myeloid leukem...

Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-based Pharmacokinetic Quantification of DCE MRI.

Purpose To improve the generalizability of pathologic complete response (pCR) prediction following neoadjuvant chemotherapy using deep learning (DL)-...

Machine learning for synchronous bone metastasis risk prediction in high grade lung neuroendocrine carcinoma.

Bone metastasis (BM) is common in high-grade lung neuroendocrine tumors (NETs). This study aimed to use multiple machine learning algorithms to explor...

Predicting the prognosis of radical gastrectomy for patients with locally advanced gastric cancer after neoadjuvant chemotherapy using machine learning technology: a multicenter study in China.

BACKGROUND: Neoadjuvant chemotherapy (NAC) can improve the prognosis of patients with locally advanced gastric cancer (LAGC). However, precise models ...