Oncology/Hematology

Deep learning-based malignancy probability estimation of pulmonary nodules in PET/CT imaging.

OBJECTIVE: The current BTS guidelines recommend evaluation of suspicious pulmonary nodules using [18F]FDG-PET/CT imaging, followed by Herder model risk stratification. However, it is based on limited imaging features, which may limit diagnostic accuracy. This study aims to develop a PET/CT-based deep learning (DL) model for malignancy probability estimation (AITO-PETCT-MP) and compare its performa...

Mapping oral-systemic health relationships: a data-driven analysis of co-occurrence patterns in biomedical literature.

OBJECTIVE: To systematically map co-occurrence patterns between systemic medical conditions and oral diseases in the biomedical literature. MATERIALS AND METHODS: We conducted a large-scale bibliometric analysis of PubMed-indexed literature from 1995 to 2025. Using automated Application Programming Interface queries, we retrieved publications jointly referencing 256 systemic conditions (defined us...

Periodic surveillance of intraductal papillary mucinous neoplasms: gaps in evidence and future directions.

Intraductal papillary mucinous neoplasm (IPMN) is a well-established precursor to pancreatic cancer; however, its generally indolent biological behavi...

Topological imaging of single-cell CAF heterogeneity for predicting prognosis and adjuvant chemotherapy benefit in pancreatic cancer.

PURPOSE: Cancer-associated fibroblasts (CAFs) are central drivers of PDAC progression and therapeutic resistance, yet their preoperative clinical util...

Comparison of radiomics handcrafted features and deep features in 18F-FDG PET/CT for differentiating benign and malignant persistent pulmonary ground-glass nodules.

BACKGROUND: The differentiation between benign and malignant persistent pulmonary ground-glass nodules (GGNs) remains challenging, and the relative va...

Artificial intelligence-based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: a pilot study.

BACKGROUND: Aortic valve calcium scoring by computed tomography (CT) is an established method for assessing aortic stenosis severity but is limited by...

RadiolGAN: Multicenter Feasibility Study of Synthetic CT From 3D Ultra-Short Echo Time MRI for Enhanced Pulmonary Radiologic Sign Visualization.

BACKGROUND: Chest CT requires breath-holding and ionizing radiation. 3D ultrashort echo time (UTE) MRI allows radiation-free imaging, but the image qu...

Glycosylation Signatures of Thyroglobulin in Papillary Thyroid Carcinoma.

Thyroglobulin (Tg) is a clinically established biomarker for thyroid cancer; however, its diagnostic specificity remains limited due to confounding be...

Combining pathology artificial intelligence and genomic biomarkers to refine long-term postprostatectomy outcome prediction.

BACKGROUND: A multimodal AI (MMAI) model has been validated in prostate biopsy specimens to guide treatment intensification in men receiving radiation...

Atomic force microscopy in lung cancer: from mechanobiology to clinical translation.

Atomic force microscopy (AFM) enables the nanoscale investigation of cellular mechanics, morphology, and molecular interactions under conditions that ...

Adaptive graph learning of microbial phylogeny enables accurate and interpretable microbiome-based host phenotype prediction.

The human microbiome is inherently structured by phylogeny, yet most predictive models treat microbial taxa as independent features, thereby underusin...

Machine learning prediction of local control after Gamma Knife radiosurgery to post-resection cavities from brain metastases: a proof-of-concept study.

BACKGROUND: Large symptomatic brain metastases require initial surgical resection. However, local control (LC) after Gamma Knife radiosurgery (GKRS) t...

Establishing a Multimodal Model for Predicting Lymphovascular Invasion in Breast Cancer Using Deep Transfer Learning Based on Ultrasound and Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

OBJECTIVE: Develop a multimodal model based on ultrasound (US) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data for predicting ...

A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing.

Cell-free DNA (cfDNA) in body fluids enables noninvasive cancer detection. Multifeature artificial intelligence (AI) can improve sensitivity by integr...

Applications of Large Language Models in Ovarian Cancer Management: Protocol for a Systematic Review and Meta-Analysis.

BACKGROUND: Ovarian cancer (OC) is a highly fatal gynecologic malignancy with complex management challenges and limited long-term survival for advance...

A Machine Learning-Assisted Highly Sensitive Photoelectrochemical Biosensor Based on Dual-Signal CuO Nanoparticles for the Combined Detection of Dual LncRNAs.

As an emerging biomarker of lung cancer, developing a highly sensitive method for the combined detection of dual lncRNAs holds significant value for t...

Response to the Letter to the Editor Regarding: "Predicting Postoperative Neurological Outcomes in Metastatic Spinal Tumor Surgery Using Machine Learning".

Antibody-drug conjugates in selected solid tumours: a position statement update based on findings from the third workshop held by the ETOP IBCSG Partners Foundation.

The European Thoracic Oncology Platform (ETOP) International Breast Cancer Study Group (IBCSG) Partners Foundation initiated a series of workshops for...

Center-specific Federated Learning for Radiation Pneumonitis:A Cross-Center Adaptive Alternating Framework.

BACKGROUND AND PURPOSE: Accurate prediction of symptomatic radiation pneumonitis (RP) is critical for radiotherapy, yet the generalization of deep lea...

Pulmonary nodule prediction in the multi-omics era: integrating radiomics, AI, liquid biopsy, and airway classifiers.

Low-dose CT (LDCT) lung cancer screening significantly reduces mortality but has dramatically increased the detection of pulmonary nodules. Most of th...