Predicting pathological complete response to breast cancer neoadjuvant therapy using multi-combination machine learning models based on vision transformer features
L
Linyong Wu
S
Songhua Li
F
Feng Chen
C
Chaojun Wu
Y
Yan Lin
(Department of Medical Oncology Guangxi Medical University Cancer Hospital Nanning China)
S
Shaofeng Wu
D
Dayou Wei
X
Xiaohong Xu
(Research Institute of Materials Science of Shanxi Normal University & Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education)
Article Details
Journal
Scientific Reports
Volume / Issue
Vol. 16, Issue 1
Published
December 31, 2025
ISSN
2045-2322
Publisher
Nature Portfolio
Authors (8)
L
Linyong Wu
S
Songhua Li
F
Feng Chen
C
Chaojun Wu
Y
Yan Lin
Department of Medical Oncology Guangxi Medical University Cancer Hospital Nanning China
S
Shaofeng Wu
D
Dayou Wei
X
Xiaohong Xu
Research Institute of Materials Science of Shanxi Normal University & Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education
Related Articles from this Journal
AI models of unstable flow exhibit hallucination
Ramdhan Wibawa, Birendra Jha
Aug 2026
10.1038/s41598-026-64023-8
The MCAA-YOLO + XPBI integrated model provides a hybrid intelligent measurement method for predicting the body weight of Hu sheep
Hang Zhang, Yuang Cheng et al.
Aug 2026
10.1038/s41598-026-64075-w
Application of iron nano-biofertilizers as a sustainable approach to enhance growth and micronutrient uptake in soybean
Ava Mohrazi, Reza Ghasemi-Fasaei
Aug 2026
10.1038/s41598-026-66011-4
Adolescents’ status-dependent public and private conformity to prosocial and antisocial decisions in a public goods game
Niloofar Saeedi, Khatereh Borhani et al.
Aug 2026
10.1038/s41598-026-64831-y
An explainable hierarchical capsule network framework for supporting expert sensory evaluation of herbal medicines with natural language generation
Hyein Lee, Dae-Hyun Jung
Aug 2026
10.1038/s41598-026-64156-w