Oncology/Hematology

Identifying Key Genes in Cancer Networks Using Persistent Homology

Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the high-dimensional interactions that cancer genes have within cancer networks. This study presents a novel m...

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology.

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomi...

Machine learning-based individualized survival prediction model for prognosis in osteosarcoma: Data from the SEER database.

Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This study aimed to compare the performance of multiple...

Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs. Clinical Teams

In-basket message interactions play a crucial role in physician-patient communication, occurring during all phases (pre-, during, and post) of a pat...

Digital Twin Ecosystem for Oncology Clinical Operations

Artificial Intelligence (AI) and Large Language Models (LLMs) hold significant promise in revolutionizing healthcare, especially in clinical applica...

MRI Radiomics for IDH Genotype Prediction in Glioblastoma Diagnosis

Radiomics is a relatively new field which utilises automatically identified features from radiological scans. It has found a widespread application,...

[Applications of artificial intelligence for imaging-driven diagnosis and treatment of bone and soft tissue tumors].

Bone and soft tissue tumors occur in the musculoskeletal system, and malignant bone tumors of bone and soft tissue account for 0.2% of all human malig...

Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks.

The discovery of diagnostic and therapeutic biomarkers for complex diseases, especially cancer, has always been a central and long-term challenge in m...

Deep contrastive learning for predicting cancer prognosis using gene expression values.

Recent advancements in image classification have demonstrated that contrastive learning (CL) can aid in further learning tasks by acquiring good featu...

Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity.

Stratification of patients diagnosed with cancer has become a major goal in personalized oncology. One important aspect is the accurate prediction of ...

A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers.

Identifying potential cancer biomarkers is a key task in biomedical research, providing a promising avenue for the diagnosis and treatment of human tu...

[A deep learning model based on magnetic resonance imaging and clinical feature fusion for predicting preoperative cytokeratin 19 status in hepatocellular carcinoma].

OBJECTIVE: To establish a deep learning model for testing the feasibility of combining magnetic resonance imaging (MRI) deep learning features with cl...

Artificial intelligence strengthens cervical cancer screening - present and future.

Cervical cancer is a severe threat to women's health. The majority of cervical cancer cases occur in developing countries. The WHO has proposed screen...

Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data

Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient pro...

Multivariate Analysis of Gut Microbiota Composition and Prevalence of Gastric Cancer

The global surge in the cases of gastric cancer has prompted an investigation into the potential of gut microbiota as a predictive marker for the di...

Automating proton PBS treatment planning for head and neck cancers using policy gradient-based deep reinforcement learning

Proton pencil beam scanning (PBS) treatment planning for head and neck (H&N) cancers is a time-consuming and experience-demanding task where a large...

Clinical Validation of a Real-Time Machine Learning-based System for the Detection of Acute Myeloid Leukemia by Flow Cytometry

Machine-learning (ML) models in flow cytometry have the potential to reduce error rates, increase reproducibility, and boost the efficiency of clini...

Tailoring nonsurgical therapy for elderly patients with head and neck squamous cell carcinoma: A deep learning-based approach.

To assess deep learning models for personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy for e...

Impact of Stain Variation and Color Normalization for Prognostic Predictions in Pathology

In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expe...

DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images

Cancer is a complex disease characterized by uncontrolled cell growth. T cell receptors (TCRs), crucial proteins in the immune system, play a key ro...