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Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study.
You J, Huang Y, Ouyang L, Zhang X, Chen P, Wu X, Jin Z, Shen H, Zhang L, Chen Q, Pei S, Zhang B, Zhang S. You J, et al. Among authors: shen h. EClinicalMedicine. 2024 Feb 27;69:102499. doi: 10.1016/j.eclinm.2024.102499. eCollection 2024 Mar. EClinicalMedicine. 2024. PMID: 38440400 Free PMC article.
An interpretable machine learning model based on contrast-enhanced CT parameters for predicting treatment response to conventional transarterial chemoembolization in patients with hepatocellular carcinoma.
Zhang L, Jin Z, Li C, He Z, Zhang B, Chen Q, You J, Ma X, Shen H, Wang F, Wu L, Ma C, Zhang S. Zhang L, et al. Among authors: shen h. Radiol Med. 2024 Mar;129(3):353-367. doi: 10.1007/s11547-024-01785-z. Epub 2024 Feb 14. Radiol Med. 2024. PMID: 38353864
Quantitative Parameters of Contrast-Enhanced Ultrasound Predicting the Response to Combined Immune Checkpoint Inhibitor and Anti-angiogenesis Therapies for Unresectable Hepatocellular Carcinoma.
Zhang Y, Zheng R, Liu M, Zhang X, Sun Y, Shen H, Chen S, Cai H, Guo W, Xie X, Liu B, Huang G. Zhang Y, et al. Among authors: shen h. Ultrasound Med Biol. 2024 Mar;50(3):352-357. doi: 10.1016/j.ultrasmedbio.2023.11.005. Epub 2023 Dec 10. Ultrasound Med Biol. 2024. PMID: 38072718
Correction: A deep learning model with data integration of ultrasound contrast-enhanced micro-flow cines, B-mode images, and clinical parameters for diagnosing significant liver fibrosis in patients with chronic hepatitis B.
Liu Z, Li W, Zhu Z, Wen H, Li MD, Hou C, Shen H, Huang B, Luo Y, Wang W, Chen X. Liu Z, et al. Among authors: shen h. Eur Radiol. 2023 Aug;33(8):5908. doi: 10.1007/s00330-023-09817-4. Eur Radiol. 2023. PMID: 37355507 No abstract available.
14,357 results
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