Transvaginal ultrasound-based deep learning radiomics for preoperative identification of non-endometrioid endometrial carcinoma: A multicenter study
- DOI
- 10.1016/j.clinimag.2026.110979
- Published
- 2027-01
- Container
- Clinical Imaging
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.clinimag.2026.110979,
title = {Transvaginal ultrasound-based deep learning radiomics for preoperative identification of non-endometrioid endometrial carcinoma: A multicenter study},
author = {Xiaoling Liu and Weihan Xiao and Yiping Jia},
year = {2027},
journal = {Clinical Imaging},
doi = {10.1016/j.clinimag.2026.110979},
url = {https://doi.org/10.1016/j.clinimag.2026.110979}
}RIS
TY - JOUR TI - Transvaginal ultrasound-based deep learning radiomics for preoperative identification of non-endometrioid endometrial carcinoma: A multicenter study AU - Xiaoling Liu AU - Weihan Xiao AU - Yiping Jia PY - 2027 JO - Clinical Imaging DO - 10.1016/j.clinimag.2026.110979 UR - https://doi.org/10.1016/j.clinimag.2026.110979 ER -
APA
Liu, X., Xiao, W., & Jia, Y. (2027). Transvaginal ultrasound-based deep learning radiomics for preoperative identification of non-endometrioid endometrial carcinoma: A multicenter study. Clinical Imaging. https://doi.org/10.1016/j.clinimag.2026.110979
Source records
- crossref · retrieved 2026-11-10T12:02:39.526Z