OBJECTIVE: To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for discriminating low and high nuclear grade clear cell renal cell carcinomas (cc-RCCs).
BACKGROUND: In exploring the time course of a disease to support or generate biological hypotheses, the shape of the hazard function provides relevant information. For long follow-ups the shape of hazard function may be complex, with the presence of ...
Papillary Renal Cell Carcinoma (PRCC) is a heterogeneous disease with variations in disease progression and clinical outcomes. The advent of next generation sequencing techniques (NGS) has generated data from patients that can be analysed to develop ...
PURPOSE: To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomograph...
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Feb 13, 2018
PURPOSE: To evaluate the surgical and functional outcomes of a matched-paired series of on-clamp vs off-clamp endoscopic robot-assisted simple enucleation (ERASE) and standardized renorraphy in a tertiary referral institution, to search for predictor...
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