PURPOSE: A deep learning model integrating CT radiomics and clinical features was developed to predict perioperative complications and risk grade in patients undergoing partial nephrectomy, and was compared to traditional anatomical classification mo...
Cancer is a life-threatening disease that affects several human lives all over the world. The classification of cancer severities utilizing histopathological images is vital for effective and timely diagnosis. This always creates a demandable require...
BACKGROUND: Accurate preoperative T and TNM staging of clear cell renal cell carcinoma (ccRCC) is crucial for diagnosis and treatment, but these assessments often depend on subjective radiologist judgment, leading to interobserver variability. This s...
Renal cell carcinomas (RCCs) are the seventh most widespread histological cancer. Around 40% of patients die in RCC due to the disease development. Thus, this tumour is the most lethal malignant urological tumour. The histopathologic classification o...
Renal cell carcinoma (RCC) is one of the typical categories of kidney cancer and is a varied group of malignancies arising from epithelial cells of the kidney parenchyma. RCC has more than ten subtypes. Classification of RCC sub-types is mainly accor...
Renal cancer is a key reason for cancer-related deaths among males worldwide. Earlier diagnosis of renal cancer is critical since it can considerably increase the chance of survivability. However evaluating the histopathological renal tissue is a ted...
Clear cell renal cell carcinoma (ccRCC) is a highly heterogeneous tumor that lacks reliable biological markers for diagnosis and prognostic monitoring. Currently, the differentially expressed genes between paired adjacent normal tissues and ccRCC tum...
Kidney lesion subtype identification is essential for precise diagnosis and personalized treatment planning. However, achieving reliable classification remains challenging due to factors such as inter-patient anatomical variability, incomplete multi-...
BACKGROUND: Annotating new classes in MRI images is time-consuming. Refining presegmented structures can accelerate this process. Many target classes lacking in MRI are supported by computed tomography (CT) models, but translating MRI to synthetic CT...
BACKGROUND: Tumor evolution, driven by the emergence of genetically and epigenetically distinct subclones, enables cancers to adapt to selective pressures and become more aggressive, posing a major challenge in oncology. Multi-regional sequencing has...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.