Oncology/Hematology

Complex-valued neural networks to speed-up MR thermometry during hyperthermia using Fourier PD and PDUNet.

Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tumour tissue is exogenously heated to temperatures between 39 and 43 °C for 60 min. Temperature monitoring can be performed non-invasively using dynamic magnetic resonance imaging (MRI). However, the slow nature of MRI leads to motion artefacts in the...

Recent Advances in Molecular Targeted Therapy for Inflammatory Bowel Disease.

Progress in the development of molecular-targeted therapies in the field of inflammatory bowel disease has been remarkable. With the advent of molecular-targeted agents, treatment strategies have changed, and higher-level treatment goals have been set. In terms of the long-term prognosis of inflammatory bowel disease, there has been a clear decline in the rate of surgery since 2000 when anti-tumor...

Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer.

BACKGROUND: Lysyl oxidases (LOX/LOXL1-4) are crucial for cancer progression, yet their transcriptional regulation, potential therapeutic targeting, pr...

Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithms.

Early detection of pancreatic cancer (PC) remains challenging largely due to the low population incidence and few known risk factors. However, screeni...

Large-scale bulk and single-cell RNA sequencing combined with machine learning reveals glioblastoma-associated neutrophil heterogeneity and establishes a VEGFA neutrophil prognostic model.

BACKGROUND: Neutrophils play a key role in the tumor microenvironment (TME); however, their functions in glioblastoma (GBM) are overlooked and insuffi...

Gap-App: A sex-distinct AI-based predictor for pancreatic ductal adenocarcinoma survival as a web application open to patients and physicians.

In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between ma...

MedScale-Former: Self-guided multiscale transformer for medical image segmentation.

Accurate medical image segmentation is crucial for enabling automated clinical decision procedures. However, existing supervised deep learning methods...

Repurposing of the Syk inhibitor fostamatinib using a machine learning algorithm.

TAM (TYRO3, AXL, MERTK) receptor tyrosine kinases (RTKs) have intrinsic roles in tumor cell proliferation, migration, chemoresistance, and suppression...

MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis.

Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provides a powerful approach for in-depth T cell immune ...

101 Machine Learning Algorithms for Mining Esophageal Squamous Cell Carcinoma Neoantigen Prognostic Models in Single-Cell Data.

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in the digestive tract, characterized by a high recurrence ra...

LKAN: LLM-Based Knowledge-Aware Attention Network for Clinical Staging of Liver Cancer.

Clinical staging of liver cancer (CSoLC), an important indicator for evaluating primary liver cancer (PLC), is key in the diagnosis, treatment, and re...

Self-Supervised Multi-Scale Multi-Modal Graph Pool Transformer for Sellar Region Tumor Diagnosis.

The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the central nervous system. The early diagnosis of the se...

Multi-Scale Dynamic Sparse Token Multi-Instance Learning for Pathology Image Classification.

In many challenging breast cancer pathology images, the proportion of truly informative tumor regions is extremely limited. The disparity between the ...

Using Real-World Data for Machine-Learning Algorithms to Predict the Treatment Response in Advanced Melanoma: A Pilot Study for Personalizing Cancer Care.

PURPOSE: The use of real-world data (RWD) in oncology is becoming increasingly important for clinical decision making and tailoring treatment. Despite...

Leveraging Artificial Intelligence to Uncover Symptom Burden in Palliative Care: Analysis of Nonscheduled Visits Using a Phi-3 Small Language Model.

PURPOSE: This study aimed to differentiate nonscheduled visits (NSVs) in an outpatient palliative care setting that are driven by or accompanied by un...

Brain tumor segmentation and detection in MRI using convolutional neural networks and VGG16.

BackgroundIn this research, we explore the application of Convolutional Neural Networks (CNNs) for the development of an automated cancer detection sy...

Early detection of esophageal cancer: Evaluating AI algorithms with multi-institutional narrowband and white-light imaging data.

Esophageal cancer is one of the most common cancers worldwide, especially esophageal squamous cell carcinoma, which is often diagnosed at a late stage...

Identification of Crohn's Disease-Related Biomarkers and Pan-Cancer Analysis Based on Machine Learning.

: In recent years, the incidence of Crohn's disease (CD) has shown a significant global increase, with numerous studies demonstrating its correlation ...

Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and experiments.

INTRODUCTION: Early diagnosis of Ewing sarcoma (ES) is critical for improving patient prognosis. However, the accurate diagnosis of ES remains challen...

5-5-5 ABRT (Dose of 5 Gy per Fraction for up to 5 Fractions Over 5 Weeks Adaptive Bridging Radiation Therapy)-Artificial Intelligence Enters the CAR (-T) (Chimeric Antigen Receptor-T) in Relapsed/Refractory Large B Cell Lymphoma.

PURPOSE: Bridging radiation therapy (BRT) is effective for local control in patients with relapsed or refractory large B cell lymphoma who are undergo...