Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Telomere-related genes (TRGs) are vital in diverse tumor types. Nevertheless, there is a notable lack of in-depth research concerning their significance in head and neck squamous cell carcinoma (HNSCC). In this context, the present study aims to assess the predictive value of TRGs in HNSCC. Gene expression data and clinical data for HNSCC were sourced from The Cancer Genome Atlas and the Gene Expr...
Reliable tumor segmentation in thoracic computed tomography (CT) remains challenging due to boundary ambiguity, class imbalance, and anatomical variability. We propose an uncertainty-guided, coarse-to-fine segmentation framework that combines full-volume tumor localization with refined region-of-interest (ROI) segmentation, enhanced by anatomically aware post-processing. The first-stage model ge...
Background: Spatial transcriptomics have emerged as a powerful tool in biomedical research because of its ability to capture both the spatial contex...
Purpose: Accurately classifying tissue margins during cancer surgeries is crucial for ensuring complete tumor removal. Rapid Evaporative Ionization ...
Cancer patients are increasingly turning to large language models (LLMs) as a new form of internet search for medical information, making it critica...
Speed-of-sound (SoS) is a biomechanical characteristic of tissue, and its imaging can provide a promising biomarker for diagnosis. Reconstructing So...
Cancer remains one of the leading causes of mortality worldwide, and among its many forms, brain tumors are particularly notorious due to their aggr...
PURPOSE: A multimodal artificial intelligence (MMAI) biomarker was developed using clinical trial data from North American men with localized prostate...
Artificial intelligence (AI) is increasingly being utilized in cancer research as a computational strategy for analyzing multiomics datasets. Advances...
BACKGROUND: Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer. Many molecular genetic changes are associated wit...
Overdiagnosis in cancer care remains a significant concern, often resulting in unnecessary physical, emotional, and financial burdens on patients. Art...
Transformer-based models have achieved remarkable success in natural language and vision tasks, but their application to gene expression analysis re...
Artificial intelligence (AI) and the introduction of Large Language Model (LLM) chatbots have become a common source of patient inquiry in healthcare....
Efficiently acquired and precisely reconstructed imaging are crucial to the success of modern radiation therapy (RT). Computed tomography (CT) and m...
Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths and the third most prevalent malignant tumour worldwide. Early de...
The histopathological images contain a huge amount of information, which can make diagnosis an extremely timeconsuming and tedious task. In this stu...
Accurate and efficient cell detection is crucial in many biomedical image analysis tasks. We evaluate the performance of several Deep Learning (DL) ...
In the U.S., lung cancer is the second major cause of death. Early detection of suspicious lung nodules is crucial for patient treatment planning, m...
Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with early and accurate diagnosis playing a pivotal role in imp...
Computed tomography (CT) examination poses radiation injury to patient. A consensus performing CT imaging is to make the radiation dose as low as re...