Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses
Abstract Treatment decisions for an incidental renal mass are mostly made with pathologic uncertainty. Improving the diagnosis of benign renal masses and distinguishing aggressive cancers from indolent ones is key to better treatment selection. We analyze 13261 pre-operative computed computed tomogr...
Saved in:
Main Authors: | Ying Xiong, Linpeng Yao, Jinglai Lin, Jiaxi Yao, Qi Bai, Yuan Huang, Xue Zhang, Risheng Huang, Run Wang, Kang Wang, Yu Qi, Pingyi Zhu, Haoran Wang, Li Liu, Jianjun Zhou, Jianming Guo, Feng Chen, Chenchen Dai, Shuo Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-02-01
|
Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-025-56784-z |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
RenalSegNet: automated segmentation of renal tumor, veins, and arteries in contrast-enhanced CT scans
by: Rashid Khan, et al.
Published: (2025-01-01) -
Identification of Houge type of X-linked syndromic mental retardation caused by CNKSR2 truncated variants
by: Si-Hua Chang, et al.
Published: (2025-02-01) -
Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values
by: Jiaxin Li, et al.
Published: (2025-02-01) -
MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images
by: Tianhu Zhao, et al.
Published: (2025-02-01) -
Causal links between obesity, lipids, adipokines, and cognition: a bidirectional Mendelian-randomization analysis
by: Meng Gong, et al.
Published: (2025-02-01)