Oncology/Hematology

Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.

The extensive heterogeneity of cancer across biological scales necessitates a holistic approach beyond single-analyte methods. Integrating multi-omics data - from genomics to proteomics - with multimodal information, such as clinical records and medical imaging, offers a comprehensive, systems-level view of tumorigenesis. Artificial intelligence (AI) has emerged as the essential technology to deci...

Enhanced multicancer screening assay through whole-genome methylation sequencing-based multimodal cell-free DNA analysis.

The rapid and accurate detection of multiple cancers presents considerable challenges, especially for stage I disease, due to the low concentration and heterogeneous nature of circulating tumor DNA. Here we introduce an enhanced multicancer screening assay that integrates whole-genome methylation sequencing with an innovative multimodal analytical framework for cell-free DNA. The ensemble machine ...

Automated detection of basement-membrane breaches in oral squamous cell carcinoma : A comparative study of convolutional neural network architectures.

BACKGROUND: Oral squamous cell carcinoma (OSCC) remains a leading cause of cancer-related morbidity and mortality worldwide. The ability to detect ear...

Online adaptive radiotherapy: Clinical utility, challenges and perspectives.

Online adaptive radiotherapy (oART) represents a major evolution in radiation oncology, enabling daily plan adaptation to account for anatomical varia...

Machine learning for land use change analysis in environmental protection areas.

Anthropogenic land use and land cover change (LULCC), combined with ongoing climate variability, poses significant challenges to environmental protect...

In vivo CAR-cell therapy: current challenges and emerging therapeutic advances.

In vivo chimeric antigen receptor (CAR) cell therapy is undergoing a transformative shift from conventional ex vivo manufacturing toward in situ cellu...

Perceptions of Health Care Professionals on the Integration and Use of AI in Clinical Cancer Care: Interview Study.

BACKGROUND: Artificial intelligence (AI) is increasingly recognized for its potential to transform cancer care. However, much of the existing evidence...

AI-Driven Pathology and Blood-Based Biomarkers: A Golden Opportunity to Democratize Precision Oncology.

Advancing global health equity requires a paradigm shift in precision oncology, which currently remains inaccessible to most patients worldwide becaus...

Neuromodulation-induced normalization of cortical metastable dynamics signatures in Parkinson's disease.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) alleviates motor symptoms in Parkinson's disease (PD), but how it modulates whole-brain ...

Hybrid supervised deep learning for lung adenocarcinoma diagnosis to optimize surgical strategies.

Intraoperative frozen section pathological diagnosis of lung adenocarcinoma serves as the gold standard for determining the extent of surgical resecti...

AI-based synthetic CT attenuation correction enables reliable quantitative SPECT in unilateral condylar hyperplasia.

PURPOSE: Unilateral condylar hyperplasia (UCH) is a rare mandibular growth disorder in which accurate assessment of condylar metabolic activity is ess...

Development of machine learning and nomogram models to predict lung metastasis and prognosticate survival in breast cancer.

PURPOSE: Lung metastasis in breast cancer (BCLM) is a critical determinant of poor prognosis, occurring in approximately 30-50% of advanced cases and ...

Quantitative Analysis of Multiple Serum Tumor Biomarkers by an Interpretable Stacked Ensemble Model.

Surface-enhanced Raman spectroscopy (SERS) offers exceptional sensitivity and specificity for biomolecular detection, particularly in analyzing serum ...

Cauchy Lotus Optimization-based feature selection and ResNet 101 based XceptionNet architecture for radiation pneumonitis prediction in lung cancer patients.

Thoracic radiotherapy for lung cancer patients followed by radiation pneumonitis (RP) has significant clinical side effects. Risk-adaptive treatment p...

Machine learning models for predicting microvascular invasion in hepatocellular carcinoma with three-dimensional whole-lesion 18F-FDG PET radiomics.

OBJECTIVE: To estimate the performance of machine learning models based on preoperative three-dimensional whole-lesion radiomics features for predicti...

Combining cutting edge computational and experimental methods for targeting kras mutations in non-small cell lung cancer.

INTRODUCTION: Historically, KRAS mutations have been notoriously difficult to target despite their status as the most commonly mutated oncogene in the...

A Transformer-ResNet-Integrated Deep Learning Model for Digital Pathology-Based Grading and Risk Stratification of MASLD-Related Liver Fibrosis.

INTRODUCTION: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects up to 30% of the global population and drives progression to fi...

Optimal treatment selection for hepatocellular carcinoma in the era of immunotherapy.

Immunotherapy has revolutionized hepatocellular carcinoma (HCC) management, necessitating personalized strategies in current guidelines. Despite curat...

Clinician-deployable deep hypergraph model integrating clinical and CT radiomics predicts immunotherapy outcomes in NSCLC.

Current image-based deep learning models that predict the benefits of immunotherapy in non-small cell lung cancer (NSCLC) require high-performance har...

Cuproptosis-related circulating non-coding RNAs as diagnostic and prognostic biomarkers in oncology.

Cuproptosis is a recently described copper-dependent form of regulated cell death linked to mitochondrial metabolic stress and is emerging as a biolog...