OBJECTIVE: To investigate the discriminative capabilities of different machine learning-based classification models on the differentiation of small (< 4 cm) renal angiomyolipoma without visible fat (AMLwvf) and renal cell carcinoma (RCC).
OBJECTIVES: To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images.
OBJECTIVE: Establish a workflow that utilizes convolutional neural nets (CNN) to classify solid, lipid-poor, contrast enhancing renal masses using multiphase contrast enhanced CT (CECT) images and to assess the performance of the resulting network.
The clinical data of 22 patients with giant renal hamartoma in Zhejiang Provincial People's Hospital who underwent robot-assisted laparoscopic nephron-sparing surgery from October 2014 to January 2020 were retrospectively analyzed. All the patients s...
BACKGROUND: Prediction of complications and surgical outcomes is of outmost importance even in patients with benign renal masses. The aim of our study is to test the PADUA, SPARE and R.E.N.A.L. scores to predict nephron sparing surgery (NSS) outcomes...
To compare the safety and effectiveness of robot-assisted partial nephrectomy (RAPN) laparoscopic partial nephrectomy (LPN) in the treatment of central renal angiomyolipomas (AMLs). We retrospectively analyzed the clinical data of 103 patients who...
Journal of cancer research and clinical oncology
37672075
PURPOSE: There are undetectable levels of fat in fat-poor angiomyolipoma. Thus, it is often misdiagnosed as renal cell carcinoma. We aimed to develop and evaluate a multichannel deep learning model for differentiating fat-poor angiomyolipoma (fp-AML)...
BACKGROUND: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
BACKGROUND: This study employed a convolutional neural network (CNN) to analyze computed tomography (CT) scans with the aim of differentiating among renal tumors according to histologic sub-type.