Oncology/Hematology

A bibliometric analysis of research trends and future directions in early detection of pancreatic cancer.

BACKGROUND: Research on the early detection of pancreatic cancer has grown rapidly in recent years; however, existing bibliometric studies in this field have focused on broad research landscapes or treatment modalities, with no systematic analysis specifically mapping the knowledge structure and emerging frontiers of early detection. METHODS: Literature published between January 1, 1986, and Decem...

Mapping the research landscape of myelodysplastic syndromes: a scientometric analysis (2014-2025).

This scientometric study critically evaluates the global evolution of myelodysplastic syndromes (MDS) research from 2014 to 2025, aiming to integrate fragmented knowledge and identify emerging trends. A scientometric analysis was conducted using data retrieved from the Web of Science Core Collection on August 2, 2025. The search was restricted to "article" and "review" in English containing "Myelo...

Deep learning-driven MRI radiomics reveals biological subtypes and predicts recurrence risk in rectal cancer.

Traditional TNM staging inadequately captures the recurrence risk of rectal cancer (RC), limiting prognostic accuracy and personalized treatment decis...

Feasibility of deep learning-accelerated ultrafast T1-weighted VIBE Dixon imaging of the pelvis for screening of metastases in prostate MRI.

OBJECTIVE: This study aims to assess the image quality and perceived diagnostic confidence of research deep learning (DL)-accelerated T1-weighted "vol...

Oral squamous cell carcinoma diagnosis: an interpretable deep learning approach using multi-scale pathological images.

BACKGROUND: Diagnosis of oral squamous cell carcinoma (OSCC) currently relies heavily on the subjective experience of pathologists. This process is ti...

Integrative multi-omics and single-cell analysis identifies EGFR pathway activation and metabolic reprogramming as potential synthetic lethal vulnerabilities in resistance to the FGFR inhibitor AZD4547.

BACKGROUND: Although fibroblast growth factor receptor (FGFR) inhibitors (FGFRi) have demonstrated clinical promise, the inevitable emergence of acqui...

Multimodal deep learning-based prediction of radiation-induced temporal lobe injury via anatomical imaging and spatial dose: A multicenter study.

BACKGROUND: To develop and validate a multimodal deep learning model for pre-treatment prediction of radiation-induced temporal lobe injury (RTLI), an...

A fatty acid metabolism-based deep learning model predicts biochemical recurrence and identifies NUDT19 as a candidate metabolic factor in prostate cancer.

PURPOSE: To develop a fatty acid metabolism-based deep learning model for predicting biochemical recurrence (BCR) in prostate cancer (PCa) and to iden...

Identification of T-cell exhaustion-related biomarkers in aortic dissection via integrated multi-omics analysis and mendelian randomization.

BACKGROUND: Aortic dissection (AD) is a life-threatening cardiovascular emergency characterized by high acute mortality. While immune dysregulation is...

A perioperative multi-modal fusion and deep learning-based prognostic system for upper tract urothelial carcinoma: a multi-institutional study.

OBJECTIVES: Precise perioperative risk stratification for upper tract urothelial carcinoma (UTUC) is essential. We developed a multimodal prognostic m...

Machine learning-powered audio-omics processing method as an auxiliary diagnostic approach for advanced nasopharyngeal carcinoma.

PURPOSE: Nasopharyngeal carcinoma (NPC) is located in the nasopharyngeal mucosa and is a malignant tumour of the head and neck, and approximately 70% ...

ACOT9, a mitochondrial metabolism-related gene, promotes ROS-associated epithelial remodeling in laryngeal squamous cell carcinoma.

BACKGROUND: Laryngeal squamous cell carcinoma (LSCC) is characterized by mitochondrial metabolic reprogramming, but its prognostic significance and un...

Development and evaluation of an explainable machine learning selection pipeline for predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.

BACKGROUND: The selection of appropriate machine learning (ML) methods for clinical research remains challenging, particularly when both predictive pe...

Early risk prediction in endometrial cancer using biomarker and machine learning models: a systematic review and meta-analysis protocol.

BACKGROUND: Globally, endometrial cancer (EC) is among the most prevalent gynaecological cancers, with rising incidence driven by demographic and meta...

Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge.

Purpose To compare the performance of an artificial intelligence (AI) system with that of radiologists for estimating malignancy risk of indeterminate...

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation.

Purpose To develop and systematically evaluate an iterative training approach, termed the expert-guided annotation loop, for efficient reference stand...

Cancer-Associated Alterations In O-GalNAc Glycosylation.

Aberrant O-GalNAc glycans such as Tn, STn, and T are among the most consistent tumor-associated carbohydrate antigens, broadly expressed on carcinomas...

Cardiac substructure dosimetry in thoracic radiotherapy: moving beyond mean heart dose and toward precision cardiac risk mitigation.

BACKGROUND: Radiation-induced heart disease (RIHD) remains a clinically significant consequence of thoracic radiotherapy (RT). Historically, the mean ...

High-fidelity super-resolution CT radiomics for non-invasive EGFR mutation prediction in lung adenocarcinoma: a multi-center pooled analysis.

OBJECTIVE: To develop and validate a high-fidelity super-resolution (SR)-enhanced radiomics framework using a Residual Channel Attention Network (RCAN...

Prevalence aware feature selection improves biomarker identification in microbiome studies.

MOTIVATION: Identifying robust microbial biomarkers is crucial for disease diagnosis and prediction, elucidation of biological mechanisms, and develop...