Oncology/Hematology

Using Large Language Models to Identify Patient-Oncologist Communication Domains: A Feasibility Study.

BACKGROUND: The American Society of Clinical Oncology (ASCO) convened a multidisciplinary panel in 2017, resulting in patient-oncologist communication guidelines. Ideally, these conversations should be documented in the medical records. However, chart review for communication topics is inefficient. Large language models (LLMs) present a computational method for identification of communication doma...

Machine learning identifies prognosticators of intracranial metastatic disease in patients with breast or lung cancer.

BACKGROUND: Intracranial metastatic disease is a severe complication of cancer that confers substantial morbidity and mortality. Patients with breast or lung cancer are at particularly elevated risk of IMD. Early identification of individuals at increased risk could enable targeted surveillance and timely intervention. METHODS: We developed interpretable machine-learning competing-risk models to e...

Comprehensive validation of machine learning models predicting chemotherapy related electrolyte disorders in a multicenter study.

BACKGROUND: Electrolyte abnormalities following chemotherapy are common and clinically significant complications in cancer patients and are often asso...

Segmentation for pelvic malignancies in radiation oncology practice: a systematic review and meta-analysis protocol.

BACKGROUND: Deep learning (DL)-based artificial intelligence (AI) models, the fourth generation in autosegmentation, have been adopted both for commer...

Using tree-based ensemble methods to produce a population-based mortality risk score in Ontario, Canada.

INTRODUCTION: Risk adjustment is critical in observational epidemiology to control for confounding of the exposure-outcome relationship. Accurate pred...

Neural network architectures and normalization techniques for automated sleep stage classification using rodent EEG and EMG signals.

Accurate sleep stage classification in animal models is crucial for translational sleep research, enabling the study of mechanistic pathways and thera...

Early detection and subtyping of gastric cancer: A cell-free DNA fragmentome-based liquid biopsy method.

Cell-free DNA (cfDNA) in plasma provides attractive opportunities for early cancer diagnosis. This study aimed to establish gastric cancer (GC) artifi...

Integration of AHRR methylation, heavy metals, and clinical characteristics for urothelial carcinoma risk stratification: An explainable artificial intelligence approach.

The short metabolic half-life of conventional tobacco biomarkers often limits their ability to reflect cumulative toxicological damage, which may comp...

Robust validation of neuroimaging and clinical models via the SAR method: A case study based on the ADNI dataset.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and substantial brain atrophy. Early and accur...

Precision nutrition in breast cancer: Towards patient- and tumour-informed dietary strategies.

Dietary strategies are increasingly recognized as important modulators of breast cancer outcomes, acting through effects on metabolic regulation, weig...

Enhancing mesenteric vascular imaging with dual-energy CTA: a comparison of DLIR and ASIR-V using low contrast agent and low radiation dose protocols.

BACKGROUND: Timely diagnosis of mesenteric vascular diseases, especially acute mesenteric ischemia (AMI) due to embolism in the superior mesenteric ar...

Predicting amyotrophic lateral sclerosis stage based on multi-parameter ultrasound: development and validation of an interpretable machine learning model.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) lacks sensitive, objective staging tools to guide clinical management and trials. Existing methods hav...

A Simulation-Free Radiation Therapy Workflow Using Synthetic Computed Tomography Generated from Diagnostic Magnetic Resonance Imaging for Personalized Hippocampal-Sparing Whole-Brain Treatment.

PURPOSE: The conventional computed tomography (CT)-based consultation to simulation process for hippocampal-sparing whole-brain radiation therapy (HS-...

Protocol for an interpretable self-attention artificial neural network framework to predict cancer risk in oral potentially malignant disorders.

Oral potentially malignant disorders (OPMDs) can directly progress to cancer, necessitating accurate risk prediction to guide clinical intervention. H...

Fusing imaging and metabolic modeling via multimodal deep learning in ovarian cancer.

Integrating genotype (e.g., transcriptomics), phenotype (e.g., imaging), and tumor microenvironment (e.g., metabolomics) is crucial to elucidating the...

Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.

Lung ground-glass nodules (GGNs) represent a critical early imaging manifestation of lung adenocarcinoma, and exploring the relationship between their...

[Digital twins in uro-oncology].

BACKGROUND: Uro-oncology is moving toward precision medicine, driven by high-dimensional longitudinal data from imaging, pathology, molecular profilin...

Monotherapy cancer drug-blind response prediction is limited to intraclass generalization.

Monotherapy cancer drug response prediction (DRP) models predict the response of a cell line to a given drug. Analyzing these models' performance incl...

Identification and molecular typing of disulfidptosis-related biomarkers in anaplastic thyroid carcinoma.

Increasing evidence suggests that disulfidptosis plays a crucial role in tumorigenesis and progression. This study aimed to identify biomarkers closel...

A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.

Certain characteristics, such as high heterogeneity, a complex tumor microenvironment, metastatic potential, and drug resistance, render Lung cancer (...