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Kidney Neoplasms

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The expanding role of artificial intelligence in the histopathological diagnosis in urological oncology: a literature review.

Folia medica
The ongoing growth of artificial intelligence (AI) involves virtually every aspect of oncologic care in medicine. Although AI is in its infancy, it has shown great promise in the diagnosis of oncologic urological conditions. This paper aims to explor...

Renal Cell Carcinoma Discrimination through Attenuated Total Reflection Fourier Transform Infrared Spectroscopy of Dried Human Urine and Machine Learning Techniques.

International journal of molecular sciences
Renal cell carcinoma (RCC) is the sixth most common cancer in men and is often asymptomatic, leading to incidental detection in advanced disease stages that are associated with aggressive histology and poorer outcomes. Various cancer biomarkers are f...

An Application of Machine-Learning-Oriented Radiomics Model in Clear Cell Renal Cell Carcinoma (ccRCC) Early Diagnosis.

British journal of hospital medicine (London, England : 2005)
Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of kidney cancer, where early diagnosis is crucial for improving prognosis and treatment outcomes. Radiomics, which utilizes machine learning techniques, presents a promising ap...

Impact of different nephrectomy types on M0 renal cell carcinoma outcomes in a propensity score matching and deep learning study.

Scientific reports
There are few analyses comparing complete nephrectomy with resection of the renal parenchyma only (CN) or radical nephrectomy that includes simultaneous resection of the parenchyma, affected perirenal fascia, perirenal fat, and ureter (RN) relative t...

A machine learning-based analysis for the definition of an optimal renal biopsy for kidney cancer.

Urologic oncology
OBJECTIVE: Renal Tumor biopsy (RTB) can assist clinicians in determining the most suitable approach for treatment of renal cancer. However, RTB's limitations in accurately determining histology and grading have hindered its broader adoption and data ...

Artificial intelligence algorithms enhance urine cytology reporting confidence in postoperative follow-up for upper urinary tract urothelial carcinoma.

International urology and nephrology
PURPOSE: In Taiwan, the incidence of urothelial carcinoma of the upper urinary tract (UTUC) is high and intravesical recurrence is approximately 22%-47%. Thus, postoperative cystoscopy and urine cytology follow-up, which require experienced cytologis...

Construction of a Wilms tumor risk model based on machine learning and identification of cuproptosis-related clusters.

BMC medical informatics and decision making
BACKGROUND: Cuproptosis, a recently identified type of programmed cell death triggered by copper, has mechanisms in Wilms tumor (WT) that are not yet fully understood. This research focuses on examining the link between WT and Cuproptosis-related gen...

Triple and quadruple optimization for feature selection in cancer biomarker discovery.

Journal of biomedical informatics
The proliferation of omics data has advanced cancer biomarker discovery but often falls short in external validation, mainly due to a narrow focus on prediction accuracy that neglects clinical utility and validation feasibility. We introduce three- a...

Aggressiveness classification of clear cell renal cell carcinoma using registration-independent radiology-pathology correlation learning.

Medical physics
BACKGROUND: Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Clear cell RCC (ccRCC) is the most common RCC subtype, with both aggressive and indolent manifestations. Indolent ccRCC is often low-grade without necrosis an...