Other Cancers

Construction and validation of a nomogram prediction model for the catheter-related thrombosis risk of central venous access devices in patients with cancer: a prospective machine learning study.

Central venous access devices (CVADs) are integral to cancer treatment. However, catheter-related thrombosis (CRT) poses a considerable risk to patient safety. It interrupts treatment; delays therapy; prolongs hospitalisation; and increases the physical, psychological and financial burden of patients. Our study aims to construct and validate a predictive model for CRT risk in patients with cancer....

Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.

Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intelligence (AI) in interventional oncology has garnered significant attention. This narrative review describes the current state of AI applications in int...

De Novo Natural Language Processing Algorithm Accurately Identifies Myxofibrosarcoma From Pathology Reports.

BACKGROUND: Available codes in the ICD-10 do not accurately reflect soft tissue sarcoma diagnoses, and this can result in an underrepresentation of so...

AutoLNMNet: Automated Network for Estimating Lymph-Node Metastasis in EGC Using a Pyramid Vision Transformer and Data Derived From Multiphoton Microscopy.

Lymph-node status is important in decision-making during early gastric cancer (EGC) treatment. Currently, endoscopic submucosal dissection is the main...

Multi-instance learning for identifying high-risk subregions associated with synchronous distant metastasis in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is one of the most common histological subtypes of renal tumors.

Machine learning models based on CT radiomics features for distinguishing benign and malignant vertebral compression fractures in patients with malignant tumors.

BACKGROUND: Radiomics has become an important tool for distinguishing benign and malignant vertebral compression fractures (VCFs). It is more clinical...

Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review.

Brain tumor is a type of disease caused by uncontrolled cell proliferation in the brain leading to serious health issues such as memory loss and motor...

Prediction of Lesion-Based Treatment Response after Two Cycles of Lu-177 Prostate Specific Membrane Antigen Treatment in Metastatic Castration-Resistant Prostate Cancer Using Machine Learning.

INTRODUCTION: Lutetium-177 (Lu-177) prostate-specific membrane antigen (PSMA) therapy is a radionuclide treatment that prolongs overall survival in me...

Retrospective Analysis of Radiofrequency Ablation in Patients with Small Solitary Hepatocellular Carcinoma: Survival Outcomes and Development of a Machine Learning Prognostic Model.

BACKGROUND AND OBJECTIVE: The effectiveness of radiofrequency ablation (RFA) in improving long-term survival outcomes for patients with a solitary hep...

Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.

BACKGROUND: Lung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. H...

Exploring the potential of structure-based deep learning approaches for T cell receptor design.

Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modelin...

Integrating machine learning to advance epitope mapping.

Identifying epitopes, or the segments of a protein that bind to antibodies, is critical for the development of a variety of immunotherapeutics and dia...

A vision transformer-based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma.

BACKGROUND: Lymph node metastasis (LNM) plays a crucial role in the management of lung cancer; however, the ability of chest computed tomography (CT) ...

Artificial intelligence-based pathological application to predict regional lymph node metastasis in Papillary Thyroid Cancer.

In this study, a model for predicting lymph node metastasis in papillary thyroid cancer was trained using pathology images from the TCGA(The Cancer Ge...

Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas.

Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and inf...

Comparative study of machine learning and statistical survival models for enhancing cervical cancer prognosis and risk factor assessment using SEER data.

Cervical cancer is a common malignant tumor of the female reproductive system and the leading cause of death among women worldwide. The survival predi...

High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma.

Oral cancer is a global health challenge with a difficult histopathological diagnosis. The accurate histopathological interpretation of oral cancer ti...

Machine learning- a new paradigm in nanoparticle-mediated drug delivery to cancerous tissues through the human cardiovascular system enhanced by magnetic field.

Nanoparticle-mediated drug delivery offers a promising approach to targeted cancer therapy, leveraging the ability of nanoparticles to deliver therape...

Machine learning predicts cuproptosis-related lncRNAs and survival in glioma patients.

Gliomas are the most common tumor in the central nervous system in adults, with glioblastoma (GBM) representing the most malignant form, while low-gra...

Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis.

Deep learning-assisted digital pathology has demonstrated the potential to profoundly impact clinical practice, even surpassing human pathologists in ...