Oncology/Hematology

Development and validation of machine learning models for distant metastasis of primary hepatic carcinoma: a population-based study.

BACKGROUND: Primary liver cancer is the sixth most common cancer globally and ranks third in cancer-related mortality. Patients with distant metastasis (PLCDM) have particularly low survival rates and are more difficult to treat. This study aims to identify risk factors associated with distant metastasis and overall survival (OS) in primary liver cancer and to determine the optimal predictive mode...

Automated feature learning and survival prognostication in grade 4 glioma using supervised machine learning models.

OBJECTIVE: WHO grade 4 glioma is the most common primary malignant brain tumor, with a median survival of only 14.6 months. Predicting survival outcomes remains challenging due to the tumor's heterogeneity and the influence of multiple clinical factors. Machine learning (ML) techniques have demonstrated superior predictive performance compared to traditional statistical models. Embedded feature-se...

Interpretable deep fuzzy network-aided detection of central lymph node metastasis status in papillary thyroid carcinoma.

PURPOSE: The non-invasive assessment of central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) plays a crucial role i...

Early detection and staging of retinitis pigmentosa using multifocal electroretinogram parameters and machine learning algorithms.

Retinitis pigmentosa is an inherited retinal disease caused by damage to photoreceptor cells. Diagnosis and staging of this disease are crucial for ea...

Computational theranostics strategy for pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge in modern medicine, characterized by its insidious progression, early systemic metas...

Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma.

Accurate assessment of lymph node metastasis (LNM) in T1/2-stage esophageal squamous cell carcinoma (ESCC) is critical for treatment planning but rema...

Next-generation cancer therapeutics: unveiling the potential of liposome-based nanoparticles through bioinformatics.

Cancer remains one of the most deadly diseases in the world, requiring constant growth and improvements in therapeutic strategies. Traditional cancer ...

Can we identify individuals at risk to develop multiple myeloma? A machine learning-based predictive model.

Multiple myeloma evolves unnoticed over years, and when diagnosed, organ damage is common. Electronic health records (EHR) can help in developing pred...

Artificial Intelligence in Predicting Efficacy and Toxicity of Immunotherapy: Applications, Challenges, and Future Directions.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, becoming a standard approach for various tumor types. Consequently, accurate...

The melanoma MEGA-study: Integrating proteogenomics, digital pathology, and AI-analytics for precision oncology.

Melanoma remains the most aggressive form of skin cancer, characterized by high metastatic potential, genetic heterogeneity, and resistance to convent...

Three-dimensional multimodal imaging for predicting early recurrence of hepatocellular carcinoma after surgical resection.

BACKGROUND: High tumor recurrence after surgery remains a significant challenge in managing hepatocellular carcinoma (HCC). We aimed to construct a mu...

Application Value of Deep Learning-Based AI Model in the Classification of Breast Nodules.

Breast nodules are highly prevalent among women, and ultrasound is a widely used screening tool. However, single ultrasound examinations often result...

The role of tumor microenvironment and immune cell crosstalk in triple-negative breast cancer (TNBC): Emerging therapeutic opportunities.

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by its lack of estrogen, progesterone, and HER2 receptors, leading to limi...

Advances in molecular pathology and therapy of non-small cell lung cancer.

Over the past two decades, non-small cell lung cancer (NSCLC) has witnessed encouraging advancements in basic and clinical research. However, substant...

Multi-omics analysis unveils dysregulation of the tumor immune microenvironment and development of a machine learning-based multi-gene classifier for predicting lateral lymph node metastasis in papillary thyroid carcinoma.

PURPOSE: Lateral lymph node metastasis (LNM) critically influences surgical decision-making in papillary thyroid carcinoma (PTC). However, the sensiti...

Signature gene expression model for quantitative evaluation of MASH-like liver injury in mice.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a spectrum of chronic pathologic conditions strongly associated with metabolic syn...

Multi-class transformer-based segmentation of pancreatic ductal adenocarcinoma and surrounding structures in CT imaging: a multi-center evaluation.

OBJECTIVE: Accurate segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding anatomical structures is critical for diagnosis, treatment...

Machine learning-based models for outcome prediction in skull base and spinal chordomas: a systematic review and meta-analysis.

BACKGROUND: Chordomas are primary bone lesions originating from embryonic notochord remnants, most commonly developing along the skull base and spine....

Comprehensive statistical and machine learning framework for identification of metabolomic biomarkers in breast cancer.

INTRODUCTION: Breast cancer is the most common cancer among women, with its burden increasing over the past decades. Early diagnosis significantly imp...

Comparative analysis of pre-transcatheter aortic valve implantation CTA protocols: Optimizing radiation dose and contrast volume.

BACKGROUND: To establish the most effective and safe pre-transcatheter aortic valve implantation (TAVI) CT angiography (CTA) protocol by comparing two...