Oncology/Hematology

Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma: a step towards virtual biopsy.

OBJECTIVES: Upper tract urothelial carcinoma (UTUC) is a rare, aggressive lesion, with early detection a key to its management. This study aimed to utilise computed tomographic urogram data to develop machine learning models for predicting tumour grading and staging in upper urothelial tract carcinoma patients and to compare these predictions with histopathological diagnosis used as reference stan...

Construction of the prediction model for multiple myeloma based on machine learning.

INTRODUCTION: The global burden of multiple myeloma (MM) is increasing every year. Here, we have developed machine learning models to provide a reference for the early detection of MM.

Machine-Learning-Assisted Rational Design of Si─Rhodamine as Cathepsin-pH-Activated Probe for Accurate Fluorescence Navigation.

High-performance fluorescent probes stand as indispensable tools in fluorescence-guided imaging, and are crucial for precise delineation of focal tiss...

Development of a diagnostic support system for the fibrosis of nonalcoholic fatty liver disease using artificial intelligence and deep learning.

Liver fibrosis is a pathological condition characterized by the abnormal proliferation of liver tissue, subsequently able to progress to cirrhosis or ...

Development and Validation of a Machine Learning Model for Bone Metastasis in Prostate Cancer: Based on Inflammatory and Nutritional Indicators.

OBJECTIVE: To establish a predictive model for prostate cancer bone metastasis utilizing multiple machine learning algorithms.

Establishment of a risk prediction model for olfactory disorders in patients with transnasal pituitary tumors by machine learning.

To construct a prediction model of olfactory dysfunction after transnasal sellar pituitary tumor resection based on machine learning algorithms. A cro...

A model for skin cancer using combination of ensemble learning and deep learning.

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United S...

DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images.

Breast cancer remains a critical global concern, underscoring the urgent need for early detection and accurate diagnosis to improve survival rates amo...

Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy.

To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on...

Use of artificial intelligence chatbots in clinical management of immune-related adverse events.

BACKGROUND: Artificial intelligence (AI) chatbots have become a major source of general and medical information, though their accuracy and completenes...

Automated machine learning for predicting liver metastasis in patients with gastrointestinal stromal tumor: a SEER-based analysis.

Gastrointestinal stromal tumors (GISTs) are a rare type of tumor that can develop liver metastasis (LIM), significantly impacting the patient's progno...

Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture.

The most common and aggressive tumor is brain malignancy, which has a short life span in the fourth grade of the disease. As a result, the medical pla...

Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis.

OBJECTIVE: This study seeks to construct a machine learning model that merges clinical characteristics with ultrasound radiomic analysis-encompassing ...

Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.

PURPOSE: This study aims to evaluate the clinical performance of a deep learning (DL)-enhanced two-fold accelerated PET imaging method in patients wit...

Rapid and Precise Diagnosis of Retroperitoneal Liposarcoma with Deep-Learned Label-Free Molecular Microscopy.

The retroperitoneal liposarcoma (RLPS) is a rare malignancy whose only curative therapy is surgical resection. However, well-differentiated liposarcom...

Machine learning-derived immunosenescence index for predicting outcome and drug sensitivity in patients with skin cutaneous melanoma.

The functions of immunosenescence are closely related to skin cutaneous melanoma (SKCM). The aim of this study is to uncover the characteristics of im...

Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning.

Tumor heterogeneity is a primary factor that contributes to treatment failure. Predictive tools, capable of classifying cancer cells based on their fu...

Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology.

This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression a...

Coronary Artery Calcification on Low-Dose Lung Cancer Screening CT in South Korea: Visual and Artificial Intelligence-Based Assessment and Association With Cardiovascular Events.

Coronary artery calcification (CAC) on lung cancer screening low-dose chest CT (LDCT) is a cardiovascular risk marker. South Korea was the first Asia...

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized tre...