AIMC Topic: Carcinoma, Renal Cell

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Computer vision assisted deep transfer learning model for accurate grading of renal cell carcinoma from kidney histopathology images.

Scientific reports
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...

Advanced transformer with attention-based neural network framework for precise renal cell carcinoma detection using histological kidney images.

Scientific reports
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...

Exploiting deep transfer learning based precise classification and grading of renal cell carcinoma using histopathological images.

Scientific reports
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...

MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphology.

Genome medicine
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...

Construction and multi-omics analysis of ccRCC mitochondrial related gene machine learning model and validate of key gene FKBP10.

International immunopharmacology
BACKGROUND: Clear cell renal cell carcinoma represents the most prevalent histological subtype of renal malignancy Emerging evidence underscores the critical involvement of mitochondrial dysfunction in oncogenesis and tumor progression. In this study...

KPNA2 expression as a biomarker for immunosuppressive microenvironment predicting response to TKI and immunotherapy in metastatic renal cell carcinoma.

European journal of pharmacology
BACKGROUND: Immunotherapy (IO) combined with tyrosine kinase inhibitors (TKI) are now first-line therapy for advanced renal cell carcinoma (RCC), though reliable predictive biomarkers remain elusive. Recent evidence demonstrates that karyopherin α2 s...

Utilisation of artificial intelligence to enhance the detection rates of renal cancer on cross-sectional imaging: protocol for a systematic review and meta-analysis.

BMJ open
INTRODUCTION: The incidence of renal cell carcinoma has steadily been on the increase due to the increased use of imaging to identify incidental masses. Although survival has also improved because of early detection, overdiagnosis and overtreatment o...

Machine learning-assisted radiogenomic analysis for miR-15a expression prediction in renal cell carcinoma.

BMC cancer
BACKGROUND: Renal cell carcinoma (RCC) is a prevalent malignancy with highly variable outcomes. MicroRNA-15a (miR-15a) has emerged as a promising prognostic biomarker in RCC, linked to angiogenesis, apoptosis, and proliferation. Radiogenomics integra...

Anoikis-related genes predicts prognosis and therapeutic response in renal cell carcinoma.

Annals of medicine
BACKGROUND: Metastasis represents the primary cause of cancer-related mortality, with a high incidence observed in renal cell carcinoma (RCC). Anoikis, a specialized form of apoptosis, plays a crucial role in preventing displaced cells from adhering ...