文章名稱(chēng)引用次數(shù)

• Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma49
• Clinical indications for musculoskeletal ultrasound updated in 2017 by European Society of Musculoskeletal Radiology (ESSR) consensus35
• Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI31
• Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade27
• Prostate artery embolisation for benign prostatic hyperplasia: a systematic review and meta-analysis26
• Glioma imaging in Europe: A survey of 220 centres and recommendations for best clinical practice26
• Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI24
• Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging23
• Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling21
• The performance of 3D ABUS versus HHUS in the visualisation and BI-RADS characterisation of breast lesions in a large cohort of 886 women20