Oncology/Hematology

Enhancing Exploration and Exploitation in Tumor Treatment Through Action-Guided Deep Reinforcement Learning.

Inverse treatment planning is pivotal in tumor treatment planning. It enables the multi-objective optimization of radiation dose delivery, ensuring precise tumor targeting while sparing surrounding healthy tissues. This process often requires frequent parameter adjustments to achieve the desired balance between objectives, making it both labor-intensive and time-consuming. Deep reinforcement learn...

Evaluating eight smoking metrics for modelling survival in non-small cell lung cancer.

INTRODUCTION: Smoking is a strong modifiable prognostic factor for lung cancer survival. We compared eight smoking metrics to determine which metric best models the relationship between smoking exposure with overall survival (OS) and lung cancer-specific survival (LCSS). These metrics included cigarettes-per-day, smoking duration, pack-years, square-root pack-years, logcig-years, the comprehensive...

Optimizing pediatric chest CT: superior image quality and lower radiation dose with deep learning reconstruction.

OBJECTIVES: To compare image quality and radiation dose between deep learning reconstruction (DLIR) and hybrid iterative reconstruction (HIR) algorith...

The impact of emerging technologies on education, roles and career progression of radiation therapists: a delphi consensus based on the ESTRO RTT workshop.

Rapid technological advances in radiation oncology, including artificial intelligence (AI), online adaptive radiotherapy, and advanced imaging, are tr...

Diagnostic Accuracy of Artificial Intelligence for Predicting MGMT Promoter Methylation in Glioblastoma Using MR Imaging: A Systematic Review.

PURPOSE: Glioblastoma (GBM) is an aggressive brain tumor with poor prognosis. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation is a ...

Machine learning-augmented finite element modeling for transient hemodynamics in human arteries.

This work introduces a machine learning enhanced finite element model for the study of blood flow in a stretching artery with the presence of sulfonat...

Endothelial cell senescence shapes T cell activity in late-stage of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a leading cause of death with few effective therapies. While clinical staging distinguishes mild to ve...

Statistical models to characterize colon tumor stiffness heterogeneity through representative atomic force microscopy maps.

The study of the impact of physical forces and on cells has emerged as a fertile field of investigation. Applications in oncology are especially trans...

Development and validation of machine learning models for predicting cancer-specific survival in colorectal signet ring cell carcinoma.

Deciphering lung adenocarcinoma heterogeneity: a multi-omics approach reveals nuclear division fibroblasts as prognosticators and therapeutic targets.

BACKGROUND: Lung adenocarcinoma (LUAD) is a predominant contributor to cancer‑related mortality globally. Lung‑associated fibroblasts (LAFs) are intri...

AI-assisted tumor board decision-making in pancreatic oncology.

BACKGROUND: Pancreatic cancer requires nuanced, multidisciplinary treatment planning typically conducted within tumor boards. While Large Language Mod...

Novel genomic risk stratification model for primary high-grade malignant peripheral nerve sheath tumor (MPNST).

Risk stratification across the three main clinical subsets of malignant peripheral nerve sheath tumor (MPNST), neurofibromatosis type I (NF1-related),...

International testing and refinement of AI algorithms predicting acute leukemia subtypes from routine laboratory data.

Despite advances for patients with acute leukemia health disparities limit access to diagnosis and treatment. Artificial Intelligence (AI) approaches ...

Development and validation of the ultrasound-based radiomics and deep learning prognostic models for diffuse large B-cell lymphoma.

Diagnostic accuracy of an AI-based pathologic response assessment in locally advanced non-small cell lung cancer after neoadjuvant chemo-immunotherapy.

Contrasting roles of PD-L1 expression in tumor and stroma: Biological and prognostic implications in esophageal squamous cell carcinoma.

Programmed death-ligand 1 (PD-L1) plays a central role in immune regulation in esophageal squamous cell carcinoma (ESCC) and has been widely used as a...

Stereotactic Body Radiotherapy for Spinal Metastases: Outcomes and Development of a Risk Assessment Score to Predict Local Tumor Progression.

BACKGROUND AND OBJECTIVES: Stereotactic body radiotherapy (SBRT) has emerged as an effective treatment modality for spinal metastases. However, high-p...

MRI-based AI framework for predicting lymph node metastasis and prognosis in rectal cancer after neoadjuvant chemoradiotherapy: a multicenter study.

BACKGROUND: Early prediction of lymph node metastasis (LNM) after neoadjuvant chemoradiotherapy (nCRT) is crucial for improving treatment planning and...

Macrophages restrict tumor immune infiltration by controlling collagen topography.

During tumorigenesis, the extracellular matrix is extensively remodeled. Whereas the impact of such remodeling on tumor growth and invasion is well de...

Multi-representation machine learning approaches for screening aurora kinases A & B inhibitors and insights from structural alerts.

The Aurora kinase (AURK) family enzymes are almost identical to the serine or threonine kinase (STK), and they are essential for various cell function...