Enhancing BRAF V600E mutation prediction in thyroid cancer through interpretable deep learning models combining clinical and ultrasound-based radiomics features.
- DOI
- 10.21037/qims-2026-1-0299
- Published
- 2026 Jul 1
- Container
- Quantitative imaging in medicine and surgery
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.21037/qims-2026-1-0299,
title = {Enhancing BRAF V600E mutation prediction in thyroid cancer through interpretable deep learning models combining clinical and ultrasound-based radiomics features.},
author = {Zhang L and Huang C and Chen Z and Ying Y and Jiang N and Zhong X and Chen F and Guo Y and Luo S},
year = {2026},
journal = {Quantitative imaging in medicine and surgery},
doi = {10.21037/qims-2026-1-0299},
url = {https://doi.org/10.21037/qims-2026-1-0299}
}RIS
TY - JOUR TI - Enhancing BRAF V600E mutation prediction in thyroid cancer through interpretable deep learning models combining clinical and ultrasound-based radiomics features. AU - Zhang L AU - Huang C AU - Chen Z AU - Ying Y AU - Jiang N AU - Zhong X AU - Chen F AU - Guo Y AU - Luo S PY - 2026 JO - Quantitative imaging in medicine and surgery DO - 10.21037/qims-2026-1-0299 UR - https://doi.org/10.21037/qims-2026-1-0299 ER -
APA
L, Z., C, H., Z, C., Y, Y., N, J., X, Z., F, C., Y, G., & S, L. (2026). Enhancing BRAF V600E mutation prediction in thyroid cancer through interpretable deep learning models combining clinical and ultrasound-based radiomics features.. Quantitative imaging in medicine and surgery. https://doi.org/10.21037/qims-2026-1-0299
Source records
- pubmed · retrieved 2026-09-27T14:16:35.612Z