AIMC Topic: Angiomyolipoma
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Using deep learning to differentiate among histology renal tumor types in computed tomography scans.
BMC medical imaging
Feb 26, 2025
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.
Fully automated segmentation and classification of renal tumors on CT scans via machine learning.
BMC cancer
Jan 29, 2025
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.
Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study.
Journal of cancer research and clinical oncology
Sep 6, 2023
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)...
Comparisons of the Safety and Effectiveness of Robot-Assisted Laparoscopic Partial Nephrectomy for Central Renal Angiomyolipomas: A Propensity Score-Matched Analysis Study.
Journal of endourology
Aug 14, 2023
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...
Are nephrometry scores accurate for the prediction of outcomes in patients with renal angiomyolipoma treated with robot-assisted partial nephrectomy? A multi-institutional analysis.
Minerva urology and nephrology
May 27, 2022
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...
Deep learning based classification of solid lipid-poor contrast enhancing renal masses using contrast enhanced CT.
The British journal of radiology
May 11, 2020
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.
Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.
European radiology
Apr 29, 2020
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.
Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat.
European radiology
Aug 29, 2019
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).
Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma.
Ultrasound in medicine & biology
May 25, 2019
The question of whether ultrasound point shear wave elastography can differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML) is controversial. This study prospectively enrolled 51 patients with 52 renal tumors (42 RCCs, 10 AMLs). We obtain...
Elasticity Imaging Techniques
Aged, 80 and over
Angiomyolipoma
Young Adult
Adult
Sensitivity and Specificity
Prospective Studies
Humans
Diagnosis, Differential
Female
Machine Learning
Reproducibility of Results
Image Interpretation, Computer-Assisted
Male
Aged
Kidney Neoplasms
Middle Aged
Carcinoma, Renal Cell
Adolescent
Kidney
Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features.
Acta radiologica (Stockholm, Sweden : 1987)
Feb 24, 2019
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