9 Papers
Ran Luo is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Medicine & Breast cancer. The author has an hindex of 3, co-authored 4 publications.
Chat about Author
Papers
A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening
Huanhuan Liu,Yanhong Chen,Yuzhen Zhang,Lijun Wang,Ran Luo,Haoting Wu,Chenqing Wu,Huiling Zhang,Weixiong Tan,Hongkun Yin,Dengbin Wang +10 more
TL;DR: In this article, the authors investigated the value of full-field digital mammography-based deep learning (DL) in predicting malignancy of Breast Imaging Reporting and Data System (BI-RADS) 4 microcalcifications.
50
Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer
TL;DR: In this article , a radiomic signature constructed from deep learning features and a nomogram for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients was developed.
An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions
Lijun Wang,Lufan Chang,Ran Luo,Xue'e Cui,Huanhuan Liu,Haoting Wu,Yanhong Chen,Yuzhen Zhang,Chenqing Wu,Fang-Liang Li,Hao Liu,Wenbin Guan,Dengbin Wang +12 more
TL;DR: An artificial intelligence system to classify benign and malignant non-mass enhancement (NME) lesions using maximum intensity projection (MIP) of early post-contrast subtracted breast MR images yielded good applicability in classifying NME lesions in breast MRI and can assist the junior radiologist achieve better performance.
19
Correlation between conventional MR imaging combined with diffusion-weighted imaging and histopathologic findings in eyes primarily enucleated for advanced retinoblastoma: a retrospective study.
Yanfen Cui,Yanfen Cui,Ran Luo,Ruifen Wang,Huanhuan Liu,Caiyuan Zhang,Zhongyang Zhang,Dengbin Wang +7 more
TL;DR: Conventional MRI has some limitations in reliably predicting microscopic infiltration, with the diagnostic efficiency showing room for improvement, whereas ADC values correlated well with certain high-risk prognostic parameters for retinoblastoma.
15
A deep learning model based on dynamic contrast-enhanced magnetic resonance imaging enables accurate prediction of benign and malignant breast lessons
TL;DR: The CNN model based on DCE-MRI demonstrated high accuracy for predicting malignancy among the breast lesions and should be validated in a larger and independent cohort.