Other Cancers

Classifying Kidney Tumor via Adaptive SE-ResNeXt With Mutli-Novel Loss Function and Renal Mass Segmentation Using Pyramidal Attention-Based R2Unet++ Model.

Kidney tumors are one of the prevalent types of tumor globally and it has become a significant health concern. It is the most frequent type of urological tumor. While kidney tumors can present in various forms, the majority of the tumors are malignant. Hence, early detection is needed to reduce the mortality rates, develop preventive measures to minimize their impact and provide effective treatmen...

Clinical Significance of Biomarkers in Oropharyngeal Squamous Cell Carcinoma: Recurrence Prediction and Treatment Response.

BACKGROUND: Oropharyngeal squamous cell carcinoma (OPSCC) accounts for a substantial proportion of head and neck cancers, with a rising incidence largely driven by human papillomavirus (HPV) infection. Despite advances in multimodal treatment, disease recurrence remains common and limits long-term survival, highlighting the need for reliable biomarkers to guide prognosis and treatment. METHODS: Th...

Deep learning for EEG-based sleep stage classification: a review.

Deep learning architectures are now widely applied in sleep electroencephalogram (EEG) analysis. These developments have significantly advanced EEG-ba...

Transcriptome-based Deep Learning Model for Predicting Gemcitabine and Cisplatin Chemotherapy Response in Urothelial Carcinoma: Development and External Validation.

BACKGROUND/AIM: Chemotherapy with gemcitabine and cisplatin remains the cornerstone of treatment for advanced urothelial carcinoma (UC), yet response ...

Current and Emerging Therapies Targeting the IL-23/IL-17 Axis in Psoriasis.

Over the past two decades, experimental and clinical evidence has established the interleukin-23 (IL-23)/interleukin-17 (IL-17) axis as a key mediator...

CBFI: A multi-layer gene network key node identification algorithm integrating structural-biological features and dynamic regulatory weights.

OBJECTIVE: Identifying key nodes within multi-layer GRNs is crucial for uncovering potential biomarkers and therapeutic targets. Key nodes exhibit bot...

Machine learning-enhanced plasma proteomics discriminates pancreatic cancer-associated diabetes from type 2 diabetes mellitus.

Pancreatic ductal adenocarcinoma (PDAC) is frequently preceded by new-onset diabetes mellitus (NODM), yet differentiating PDAC-associated DM from type...

Revolutionizing lung cancer screening: the rise of artificial intelligence integrating circulating tumor markers.

Lung cancer persists as the predominant oncological cause of mortality globally, underscoring an imperative public health issue that demands effective...

Mutational signatures in blood-brain barrier: mechanisms, computational insights, and clinical applications in precision oncology.

The blood - brain barrier (BBB) plays a central role in maintaining central nervous system (CNS) homeostasis, and its disruption is a defining feature...

Malignant Transformation Risk in Patients With Oral Leukoplakia and Type 2 Diabetes Mellitus.

INTRODUCTION AND AIMS: Oral leukoplakia (OLK) is the most common oral potentially malignant disorder, with a malignant transformation rate of up to 9....

A prospective multimethod investigation of cancer-related pain integrating clinical data and machine learning: results from the RUGGI Study.

BACKGROUND: Cancer-related pain is a multidimensional phenomenon, and key determinants of pain intensity remain unclear. The aim of this study was to ...

Early ctDNA stratifies survival in locally advanced and oligometastatic lung cancer treated with radiotherapy.

PURPOSE: Circulating tumor fraction estimate (ctFE) is a machine learning-derived composite metric of circulating tumor DNA (ctDNA) burden. We hypothe...

An integrative machine learning and structure-driven drug repositioning strategy for human IRAK4-targeted cancer therapy.

The upregulation of interleukin-1 receptor-associated kinase 4 (IRAK4) drives pro-tumorigenic signaling across various malignancies. Currently availab...

Predicting early response to ablative radiotherapy in oligometastatic disease: a scoping review of radiomics-based machine learning and deep learning models.

OBJECTIVES: Oligometastatic disease represents an intermediate stage of cancer, often treated with surgery or ablative radiotherapy (ART). This scopin...

Identification of tolerogenic dendritic cells-related prognostic biomarkers in Wilms tumor via machine learning integration.

Wilms tumor (WT) is the most common pediatric kidney cancer. Tolerogenic dendritic cells (TolDCs) promote tumor immune evasion in the tumor microenvir...

Improving Long-Term Adherence to Endocrine Therapy Among Breast Cancer Survivors: Development of a Multiscale Modeling and Intervention System.

BACKGROUND: Breast cancer is a significant public health burden. Despite its critical role in preventing the recurrence of breast cancer, rates of lon...

Machine learning outperforms large language models for survival prediction in advanced hepatocellular carcinoma: a multicenter study.

Accurate prognostic prediction remains a critical unmet need in advanced hepatocellular carcinoma (HCC). While machine learning (ML) models have demon...

A quantitative analysis of global AI medical studies: gaps in randomized controlled trials.

Medical artificial intelligence (AI) has advanced rapidly, yet a comprehensive quantitative overview of its clinical evaluation landscape remains lack...

Peripheral blood mononuclear cell DNA methylation signatures guide surgical decision-making in indeterminate pulmonary nodules.

BACKGROUND: Distinguishing malignant from benign pulmonary nodules remained a significant clinical challenge. Given the involvement of DNA methylation...

Imaging of ewing sarcoma: an updated analysis including presenting features, prognostic imaging biomarkers, and treatment response assessment.

Ewing sarcoma is a highly aggressive small round cell sarcoma primarily affecting children and adolescents. Imaging plays a central role from diagnosi...