1. Pediatric Urinary System Neoplasms
    Michael George et al, 2017, Radiologic Clinics of North America CrossRef
  2. Renal Angiomyolipoma Based on New Classification: How to Differentiate It From Renal Cell Carcinoma
    Byung Kwan Park, 2019, American Journal of Roentgenology CrossRef
  3. Diagnostic Performance of CT for Diagnosis of Fat-Poor Angiomyolipoma in Patients With Renal Masses: A Systematic Review and Meta-Analysis
    Sungmin Woo et al, 2017, American Journal of Roentgenology CrossRef
  4. Angiomyolipoma of the Kidneys: Current Perspectives and Challenges in Diagnostic Imaging and Image-Guided Therapy
    Abdul Razik et al, 2019, Current Problems in Diagnostic Radiology CrossRef
  5. Quantitative Analysis of Multiphase Contrast-Enhanced CT Images: A Pilot Study of Preoperative Prediction of Fat-Poor Angiomyolipoma and Renal Cell Carcinoma
    Zhenchao Tang et al, 2020, American Journal of Roentgenology CrossRef
  6. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification
    Han Sang Lee et al, 2017, Medical Physics CrossRef
  7. Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools
    Xu Wang et al, 2021, Cancer Imaging CrossRef
  8. Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast‐enhanced CT images with texture image patches and hand‐crafted feature concatenation
    Hansang Lee et al, 2018, Medical Physics CrossRef
  9. Tumoral vascular pattern in renal cell carcinoma and fat-poor renal angiomyolipoma as a novel helpful differentiating factor on contrast-enhanced CT scan
    Seyed Morteza Bagheri et al, 2017, Tumor Biology CrossRef
  10. 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
    Ruimeng Yang et al, 2020, European Radiology CrossRef
  11. Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features
    En-Ming Cui et al, 2019, Acta Radiologica CrossRef
  12. A New Computer-Aided Diagnostic (Cad) System For Precise Identification Of Renal Tumors
    Mohamed Shehata et al, 2021, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) CrossRef
  13. Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma
    Zhichao Feng et al, 2018, European Radiology CrossRef
  14. Renal Angiomyolipoma: Radiologic Classification and Imaging Features According to the Amount of Fat
    Byung Kwan Park, 2017, American Journal of Roentgenology CrossRef
  15. A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors
    Mohamed Shehata et al, 2021, Sensors CrossRef
  16. Differential diagnosis of hypervascular ultra-small renal cell carcinoma and renal angiomyolipoma with minimal fat in early stage by using thin-section multidetector computed tomography
    Xu Wang et al, 2020, Abdominal Radiology CrossRef
  17. Diagnostic value of contrast-enhanced CT in clear cell renal cell carcinoma: a systematic review and meta-analysis
    Jiacheng Shen et al, 2024, BMC Urology CrossRef
  18. Treatment of fat-poor renal angiomyolipoma with ectopic blood supply by fluorescent laparoscopy: A case report and review of literature
    Jian-Er Tang et al, 2024, World Journal of Clinical Oncology CrossRef