AIMC Topic: Kidney Neoplasms

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Using Latent Dirichlet Allocation Topic Modeling to Uncover Latent Research Topics and Trends in Renal Cell Carcinoma: Bibliometric Review.

JMIR cancer
BACKGROUND: Renal cell carcinoma (RCC) is a common, often lethal kidney cancer that originates in the renal cortex. Its incidence is rising, and major factors include smoking, obesity, and hypertension, though its etiology is uncertain. While surgery...

LAC-TME classifier: machine learning-driven model predicts survival and prioritizes targeted therapy in clear cell renal cell carcinoma.

Journal of cancer research and clinical oncology
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a major type of kidney cancer, making up about 80% of cases, with advanced stages showing low survival rates. Current treatments face challenges like toxicity and drug resistance. Studies indicat...

Development and validation of a plasma-urine metabolism diagnostic model for renal cell carcinoma using machine learning.

World journal of urology
BACKGROUND: Renal cell carcinoma (RCC), which accounts for 70-90% of kidney malignancies, remains difficult to diagnose early due to its asymptomatic onset and the lack of reliable biomarkers. This study aimed to develop a robust diagnostic model by ...

SNMMI/EANM/ACNM Procedure Standard/Procedure Guideline on the Use of Molecular Imaging for Renal Mass Characterization.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Anatomic imaging of renal masses provides limited information on the histology or likely aggressiveness of the tumor, leading to the use of invasive procedures such as renal mass biopsy or empiric partial or radical nephrectomy. Molecular imaging can...

A SWI/SNF complex-related genes signature predicts prognosis and immune infiltration in ccRCC with KCNK5 as a novel biomarker.

Scientific reports
Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC). Although we have made many achievements in the therapy of RCC with the progress of medicine, the clinical management of metastatic RCC remains a dauntin...

Multimodal deep learning framework integrating multiphase CT and histopathological whole slide imaging for predicting recurrence in ccRCC.

Scientific reports
ccRCC is an aggressive, heterogeneous tumor with a poor prognosis. Prognostic assessments need multi-modal data. Radiological images have limits, while pathological images offer micro-level details. Integrating these for ccRCC outcome prediction is i...

Predicting distant metastasis in early-onset kidney cancer using machine learning: a SEER database study with external validation.

Clinical and experimental medicine
Patients with early-onset kidney cancer (EOKC) face a marked decline in prognosis after distant metastasis, yet the accuracy of current predictive methods remains limited. This study aims to develop a predictive model using multiple machine learning ...

Machine learning prediction of overall survival in patients with cT1b renal cell carcinoma after surgical resection using the SEER database.

Scientific reports
Accurate survival prediction is essential for guiding follow-up strategies in patients with cT1b renal cell carcinoma (RCC). Traditional AJCC TNM staging systems provide limited prognostic accuracy. Data from the SEER database were used, which includ...

Machine learning-driven classification and prognostic prediction of kidney renal clear cell carcinoma using APOBEC family expression signatures.

Scientific reports
Apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like (APOBEC) cytidine deaminases are a highly evolutionarily conserved protein family. Their members are closely associated with DNA damage repair and involved in the genesis and progressio...

Development and validation of a predictive model for adherent perirenal fat based on CT radiomics and deep learning.

World journal of urology
PURPOSE: The study aimed to develop and validate a predictive model for preoperative APF using computed tomography (CT) radiomics combined with deep learning, and validating the performance of the model in an independent cohort.