Oncology/Hematology

Dual-Phase Computed Tomography-Based Deep Learning Architecture for Three-Year Survival Prediction in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a major global health burden, ranking as the sixth most common cancer and the third leading cause of cancer-related mortality. Computed tomography (CT) is widely used for HCC evaluation because of the high spatial resolution and the availability of multiphase acquisition. However, interpretation requires expertise and may be subject to inter-observer variability. ...

TOPOS: target organ prediction on scout views for automated CT scan planning.

BACKGROUND: Overscanning is a common issue in CT planning, leading to unnecessary radiation exposure. PURPOSE: To develop a deep learning model to segment anatomical structures in scout views to optimize scan ranges and reduce radiation. MATERIALS AND METHODS: In this single-center retrospective study, 1146 patients undergoing CT between 2022 and 2025 were included. The model was trained on segmen...

Single-cell and machine learning reveal ROS-associated heterogeneity in hepatocellular carcinoma for precision medicine.

BACKGROUND: Reactive oxygen species (ROS) are critical regulators of tumor development and progression, yet their clinical significance and cellular m...

Deep learning-based CT radiomics for ALK rearrangement status prediction in lung adenocarcinoma.

BACKGROUND: Current clinical guidelines mandate routine evaluation of anaplastic lymphoma kinase (ALK) rearrangement in lung adenocarcinoma prior to A...

Multi-omics integration identifies ribosome biogenesis-active macrophage subpopulation and its key gene GNL2 in driving liver hepatocellular carcinoma progression and mechanisms.

BACKGROUND: Liver hepatocellular carcinoma (LIHC) is a common malignancy, yet the core genes driving its progression and potential therapeutic targets...

An intelligent lung nodule classification model using 3D Trans-DenseUnet++-based lung nodule segmentation.

Lung nodule detectionis necessary for lung cancer treatment, which is crucial for treating the patients. However, the current dataset comprises only a...

Metaheuristic optimization of deep CNNs for multi-class diagnosis of cervical cancer and lymphoma.

Accurate and early diagnosis of cancer is critical for determining effective treatment strategies and improving patient survival rates. However, autom...

Multiplex networks-based directed graph neural network for cancer driver gene identification.

Identifying cancer driver genes is crucial in precision oncology. Most existing methods rely on a single interaction network to capture gene relations...

Application of artificial intelligence based on contrast-enhanced CT imaging for predicting peritoneal metastasis in patients with T3/T4 stage gastric cancer.

Gastric cancer, prevalent in East Asia, often presents with peritoneal metastasis at diagnosis, limiting surgical options and reducing survival rates....

Magnetic medical microrobots with memory-capable genetic circuits.

Autonomous microrobots can reach hard-to-access regions in the human body for minimally invasive therapy. However, their microscale size limits the in...

Adrenal gland dysfunction in males with cluster headache.

BACKGROUND: Cluster headache is associated with compensated hypogonadism in males, suggesting impaired testicular steroidogenesis. It is unknown if ad...

Convolutional neural network analysis of cervical CT images: classification of Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis.

BACKGROUND: Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis are common diseases affecting the head and neck. The causes and treatment metho...

Tetrahedral framework nucleic acids as a promising vehicle for small molecule drugs of traditional Chinese medicine: application to disease prevention and treatment.

The modernization of Traditional Chinese Medicine (TCM) faces considerable challenges, primarily due to the poor water solubility, low chemical stabil...

Recent innovative approaches for drug delivery systems targeting cancer and CNS disorders.

Advancements in drug delivery systems (DDSs) have revolutionized pharmaceutical development by enhancing therapeutic precision, minimizing off-target ...

Droplet microfluidic profiling of NK cell cytotoxicity with machine learning-enabled target-cell death analysis.

Predicting the clinical efficacy of Natural Killer (NK) cell immunotherapies remains challenging due to functional heterogeneity within effector popul...

Non-Invasive Screening of AI-Assisted DNA Aneuploidy Cytology by Image Cytometry for the Early Diagnosis of Malignant Transformation of OPMDs.

INTRODUCTION AND AIMS: Oral exfoliative DNA aneuploidy cytology by image cytometry (DNA-ICM) has been introduced for the early diagnosis of malignant ...

Bispecific antibodies for cancer therapy: evolution of structural formats and co-targeting strategies from wet-lab to AI-driven in silico modeling.

Bispecific antibodies (BsAbs), designed to recognize two different antigens or two different epitopes on the same antigen, are generated in laboratori...

Deep learning for detection and automatic visualization of radiation-induced temporal lobe injury in nasopharyngeal carcinoma across endemic and non-endemic areas in China.

PURPOSE: Detection of radiation-induced temporal lobe injury (RTLI) at the earliest radiologically detectable stage is important for timely interventi...

Classification of Serum Protein and Immunofixation Electrophoresis Images by Computer Vision-Based Deep Learning Models: A Single- and Dual-Modality Study.

Objectives: The objective of this study was to develop deep learning models for the automated classification of serum protein electrophoresis (SPE) an...

The Combination of Machine Learning and Metabolomics for the Clinical Diagnosis of Hepatocellular Carcinoma.

The early diagnosis of hepatocellular carcinoma (HCC) lacks biomarkers with a high sensitivity and specificity. This study was to find sensitive and s...