Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses

Y Ying Xiong L Linpeng Yao J Jinglai Lin J Jiaxi Yao Q Qi Bai (Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry) Y Yuan Huang X Xue Zhang R Risheng Huang R Run Wang (School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, China) K Kang Wang Y Yu Qi P Pingyi Zhu H Haoran Wang (New Cornerstone Science Laboratory, State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center of Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering) L Li Liu J Jianjun Zhou J Jianming Guo F Feng Chen C Chenchen Dai S Shuo Wang

Article Details

Volume / Issue Vol. 16, Issue 1
Published February 07, 2025
ISSN 2041-1723
Publisher Nature Portfolio

Journal Info

Nature Communications

Nature Portfolio

ISSN: 2041-1723 Open Access Life Sciences

Authors (19)

Y

Ying Xiong

L

Linpeng Yao

J

Jinglai Lin

J

Jiaxi Yao

Q

Qi Bai

Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry

Y

Yuan Huang

X

Xue Zhang

R

Risheng Huang

R

Run Wang

School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, China

K

Kang Wang

Y

Yu Qi

P

Pingyi Zhu

H

Haoran Wang

New Cornerstone Science Laboratory, State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center of Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering

L

Li Liu

J

Jianjun Zhou

J

Jianming Guo

F

Feng Chen

C

Chenchen Dai

S

Shuo Wang