Latest AI and machine learning research in other cancers for healthcare professionals.
Accurate, early-stage staging of Alzheimer's disease (AD) is critical for therapeutic intervention but is hampered by data privacy regulations, multimodal data heterogeneity, and the "black-box" nature of complex Artificial Intelligence (AI) models. To address these interconnected challenges, we introduce a novel, privacy-preserving federated learning framework for robust and interpretable AD stag...
Early identification of malignant ovarian tumors is critical for informing treatment decisions and enhancing patients' quality of life. As the third most prevalent gynecologic cancer globally, ovarian cancer remains challenging to diagnose due to the high cost, limited accessibility, and radiation exposure associated with current screening techniques. This study integrates surface-enhanced Raman s...
Artificial intelligence (AI) is transforming health care and has implications for palliative care (PC) and serious illness communication (SIC). This a...
BACKGROUND: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide...
PURPOSE: The purpose of this study was to evaluate the contribution of radiomics features extracted from various pancreatic structures on computed tom...
Exosomes, nano-sized extracellular vesicles secreted by almost all cell types, have emerged as biologically compatible vehicles for targeted drug deli...
BACKGROUND AND PURPOSE: The choroid of the eye is a rare site for metastatic tumor spread, and as small lesions on the periphery of brain MRI studies,...
BACKGROUND: Early diagnosis and accurate prediction of treatment response in esophageal squamous cell carcinoma (ESCC) remain major clinical challenge...
BACKGROUND: Accurate and noninvasive breast cancer grading and therapy monitoring remain critical challenges in oncology. Traditional methods often re...
Chimeric antigen receptor (CAR)-T cell immunotherapy shows significant success in hematologic malignancies. However, it faces critical challenges in s...
Cancer immunotherapy is increasingly moving toward personalized, precision-based strategies, with cancer vaccines emerging as a promising approach to ...
BACKGROUND: Approximately one-fourth of patients with clinical stage I testicular cancer relapse. For decades, risk stratification has been based on d...
BACKGROUND: Tumor heterogeneity mediated drug resistance was a core clinical challenge to improve the prognosis of patients with advanced hepatocellul...
PURPOSE: To explore the role of MRI-based habitat radiomics in assessing the metastatic status of renal cell carcinoma (RCC). METHODS: This study retr...
OBJECTIVE: This study aimed to evaluate the coherence between data heterogeneity and model complexity by comparing seven convolutional neural network ...
INTRODUCTION: Depression frequently co-occurs with psychosis and is associated with poor outcomes. Early identification of patients at risk of persist...
BACKGROUND: Traditional statistical models often fail to capture the complex dynamics influencing survival outcomes in patients with bladder cancer af...
BACKGROUND: Leptomeningeal metastasis (LM) is a fatal complication of advanced cancer with limited therapeutic options and poor prognosis. Immune chec...
Liver cancer ranks sixth in incidence and third in mortality worldwide, with hepatocellular carcinoma (HCC) accounting for 90% of cases. Kinesin famil...
Esophageal squamous cell carcinoma (ESCC) lacks a standardized classification system, resulting in inconsistent clinical management and a suboptimal p...