Oncology/Hematology

Machine learning assessment of pathologic response in lung cancer resections after neoadjuvant therapy - IASLC MPR Project.

INTRODUCTION: Machine learning algorithms may improve efficiency and accuracy of pathologic response (PR) assessment in surgically resected lung cancers following neoadjuvant therapy. The aim of this study was to develop digital models for quantifying tumor bed (TB) area and residual viable tumor (VT), and to compare these results to previously published assessments of PR by pathologists from the ...

Ferroptosis in cancer: integrating multi-omics, AI-driven biomarkers, and translational challenges.

Ferroptosis, an iron-dependent regulated cell death driven by lipid peroxidation, has emerged as a potential target in cancers resistant to apoptosis and other programmed cell death pathways. This review integrates classical mechanistic insights with multi-omics and artificial intelligence (AI) approaches to examine ferroptosis regulation, biomarker identification, and therapeutic potential across...

Automated vs. manual segmentation for small renal mass imaging.

INTRODUCTION: Automated segmentation using artificial intelligence (AI) has the potential to rapidly perform three-dimensional (3D) segmentation of sm...

Radiomics-Based Machine Learning versus the MAP Score for Predicting Haematocrit Decline after Laparoscopic Partial Nephrectomy.

OBJECTIVE: To evaluate and compare the ability of the Mayo Adhesive Probability (MAP) score and radiomics-based machine learning approaches to predict...

Integrated Network Pharmacology and Machine Learning to Reveal the Mechanisms of Schisandrin A Against Triple-Negative Breast Cancer.

This study aimed to elucidate the mechanism of action of Schisandrin A in the intervention of triple-negative breast cancer (TNBC). Through the applic...

Organ-Specific Cancer-Associated Fibroblast Subtypes Across the Digestive System Linked to Cancer Hallmarks.

Cancer-associated fibroblasts (CAFs) are major stromal components of the tumor microenvironment (TME) and play diverse roles in gastrointestinal (GI) ...

Expert Validation of an Artificial Intelligence-Enabled Trial Matching Solution Using Real-World Data From Patients With Cancer.

PURPOSE: To validate the performance of an AI system (TRIAGE) for cancer trial eligibility screening using real-world longitudinal electronic health r...

Artificial intelligence-assisted urine cytology based on the Paris System for Reporting Urothelial Carcinoma.

BACKGROUND: Urine cytology is a noninvasive and valuable tool for detecting urothelial carcinoma but suffers from variable sensitivity and observer de...

In-Depth Plasma Proteomics Identifies SNCA as a Discriminating Biomarker of PDAC.

Pancreatic ductal adenocarcinoma (PDAC) carries a poor prognosis largely due to lack of efficient diagnostic means. We applied mass spectrometry-based...

The role of machine learning, deep learning, and MRI findings in the classification of pediatric posterior fossa tumors.

PURPOSE: Pediatric posterior fossa tumors represent a major subset of childhood central nervous system neoplasms; however, overlapping MRI features of...

Predicting Metastatic Potential from Histopathological Whole Slide Images of Primary Tumors in Head and Neck Squamous Cell Carcinoma Using Attention-Based Deep Learning and Signaling Pathway Alterations.

BACKGROUND: Head and Neck Squamous Cell Carcinoma (HNSCC) ranks as the 6th most prevalent cancer worldwide, imposing a significant burden on global he...

Exploring the potential targets and mechanisms of artemisinin in the treatment of diabetic kidney disease using network pharmacology and molecular docking.

Diabetic kidney disease (DKD) is a secondary glomerular disease caused by diabetes, and its incidence is increasing annually. Artemisinin is an organi...

Integrative multi-omics analysis identifies a circadian rhythm-associated gene signature for prognosis and therapeutic stratification in lung adenocarcinoma.

BACKGROUND/OBJECTIVES: Circadian rhythm disruption is increasingly implicated in tumor progression and therapy resistance. However, its prognostic val...

Interpretable machine learning model for predicting operative difficulty in robotic total mesorectal excision for mid-low rectal cancer.

Precise preoperative prediction of surgical complexity in robot-assisted total mesorectal excision (R-TME) is essential for optimizing surgical strate...

A risk stratification framework based on ammonia-induced cell death signatures for prognostic assessment and therapeutic insights in colorectal cancer.

BACKGROUND: Ammonia, which was traditionally regarded as a metabolic by-product, has more recently emerged as a crucial regulator of tumor metabolism ...

Evaluation of super-resolution deep learning reconstruction on three-dimensional constructive interference in steady state for enhanced visualization of vestibular schwannomas.

We evaluated whether, compared with conventional deep learning reconstruction (DLR) and zero-filling interpolation (ZIP), super-resolution DLR (SR-DLR...

Unveiling the Impact of Copper Metabolism on Epithelial-Mesenchymal Transition of Triple-Negative Breast Cancer: Identification of Therapeutic Targets.

Triple-negative breast cancer (TNBC) is a biologically aggressive subtype of breast cancer marked by high heterogeneity and poor prognosis. Copper met...

Supervised deep learning with gene functional annotation for cell classification.

Gene-by-gene differential expression analysis is a widely used supervised approach for interpreting single-cell RNA-sequencing (scRNA-seq) data. Howev...

Population-Level Raman Biochemical Staging of Malaria in Human Red Blood Cells Using Interpretable Machine Learning.

Accurate malaria staging is vital for treatment decisions and monitoring of transmission. Because mature Plasmodium falciparum parasites sequester in ...

Feasibility and Preliminary Effectiveness of the ChulaCancer Mobile Chatbot for Supportive Care of Patients With Breast or Colorectal Cancer Receiving Chemotherapy: Pilot Randomized Controlled Trial.

BACKGROUND: Chemotherapy-related toxicities often lead to unscheduled health care use and diminished quality of life. Digital health interventions, su...