Latest AI and machine learning research in oncology/hematology for healthcare professionals.
INTRODUCTION: Acute myeloid leukemia (AML) is a heterogenous hematologic malignancy that maintains h...
Design of T cell receptors by artificial intelligence is poised to accelerate cancer immunotherapy.
Many deep learning methods have been proposed for brain tumor segmentation from multi-modal Magnetic...
OBJECTIVE: To compare the predictive value of minimal ablative margin (MAM) quantification using tum...
Limited patient data availability presents a challenge for efficient machine learning (ML) model dev...
BACKGROUND: Single-photon emission computed tomography (SPECT) plays a crucial role in detecting bon...
BACKGROUND: Thyroid incidentalomas (TIs) are incidental thyroid lesions detected on fluorodeoxy-d-gl...
BACKGROUND AND PURPOSE: Single-session, multiparametric [¹⁸F]FET PET/MRI is used to detect tumor rec...
. Radiotherapy treatment planning is a time-consuming and potentially subjective process that requir...
BACKGROUND: Atezolizumab combined with bevacizumab has become the standard first-line systemic thera...
BACKGROUND: Noninvasive and precise tools for treatment response estimation in patients with high-ri...
The study investigates the correlation between CD3 T-cell expression levels and cervical cancer (CC)...
BACKGROUND/INTRODUCTION: T cell engagers (TCEs) are engineered immunotherapeutic molecules designed ...
Sleep staging is a crucial method for the evaluation of sleep quality and the diagnosis of sleep dis...
BACKGROUND: Chatbots driven by large language model artificial intelligence (AI) have emerged as pot...
A global technological race is underway to develop increasingly powerful and precise quantum compute...
OBJECTIVE: Predicting esophago-gastric and esophagojejunal anastomotic leakage (AL) is inherently ch...
Breast cancer is the most common cancers among women worldwide. Early diagnosis and personalized med...