Li JP - Search Results

1

Add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on MRI: A multicentre, validation study.

Cao X, Chen X, Lin ZC, Liang CX, Huang YY, Cai ZC, Li JP, Gao MY, Mai HQ, Li CF, Guo X, Lyu X. Cao X, et al. Among authors: li cf, li jp. iScience. 2022 Aug 3;25(9):104841. doi: 10.1016/j.isci.2022.104841. eCollection 2022 Sep 16. iScience. 2022. PMID: 36034225 Free PMC article.

2

MRI-detected residual retropharyngeal lymph node after intensity-modulated radiotherapy in nasopharyngeal carcinoma: Prognostic value and a nomogram for the pretherapy prediction of it.

Li WZ, Liu GY, Lin LF, Lv SH, Qiang MY, Lv X, Wu YS, Liang H, Ke LR, Wang DL, Yu YH, Qiu WZ, Liu KY, Guo X, Li JP, Zou YJ, Xiang YQ, Xia WX. Li WZ, et al. Among authors: li jp. Radiother Oncol. 2020 Apr;145:101-108. doi: 10.1016/j.radonc.2019.12.018. Epub 2020 Jan 10. Radiother Oncol. 2020. PMID: 31931288

3

Prognostic value and the potential role of treatment options for cervical lymph node necrosis in nasopharyngeal carcinoma.

Liu K, Lin S, Ke L, Xia W, Zhang C, Li J, Gao M, Qiang M, Chen X, Liu J, Xie C, Guo X, Lv X. Liu K, et al. Oral Oncol. 2020 Jun 27;109:104864. doi: 10.1016/j.oraloncology.2020.104864. Online ahead of print. Oral Oncol. 2020. PMID: 32604061 Free article.

4

Prognostic and Treatment Guiding Significance of MRI-Based Tumor Burden Features and Nodal Necrosis in Nasopharyngeal Carcinoma.

Chen X, Cao X, Jing B, Xia W, Ke L, Xiang Y, Liu K, Qiang M, Liang C, Li J, Gao M, Li W, Miao J, Liu G, Cai Z, Lv S, Guo X, Li C, Lv X. Chen X, et al. Front Oncol. 2020 Sep 11;10:537318. doi: 10.3389/fonc.2020.537318. eCollection 2020. Front Oncol. 2020. PMID: 33042831 Free PMC article.

5

An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features.

Chen X, Li Y, Li X, Cao X, Xiang Y, Xia W, Li J, Gao M, Sun Y, Liu K, Qiang M, Liang C, Miao J, Cai Z, Guo X, Li C, Xie G, Lv X. Chen X, et al. Oral Oncol. 2021 Jul;118:105335. doi: 10.1016/j.oraloncology.2021.105335. Epub 2021 May 21. Oral Oncol. 2021. PMID: 34023742

6

Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: a multi-center multi-reader evaluation study.

Yin S, Luo X, Yang Y, Shao Y, Ma L, Lin C, Yang Q, Wang D, Luo Y, Mai Z, Fan W, Zheng D, Li J, Cheng F, Zhang Y, Zhong X, Shen F, Shao G, Wu J, Sun Y, Luo H, Li C, Gao Y, Shen D, Zhang R, Xie C. Yin S, et al. Neuro Oncol. 2022 Sep 1;24(9):1559-1570. doi: 10.1093/neuonc/noac025. Neuro Oncol. 2022. PMID: 35100427 Free PMC article.

7

AI-based fingerprint index of visceral adipose tissue for the prediction of bowel damage in patients with Crohn's disease.

Li X, Hu C, Wang H, Lin Y, Li J, Cui E, Zhuang X, Li J, Lu J, Zhang R, Wang Y, Peng Z, Sun C, Li Z, Chen M, Shi L, Mao R, Huang B, Feng ST. Li X, et al. iScience. 2024 Sep 28;27(10):111022. doi: 10.1016/j.isci.2024.111022. eCollection 2024 Oct 18. iScience. 2024. PMID: 39635135 Free PMC article.

8

Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging-reporting and data system.

Li J, Cao K, Lin H, Deng L, Yang S, Gao Y, Liang M, Lin C, Zhang W, Xie C, Zhang K, Luo J, Pan Z, Yue P, Zou Y, Huang B. Li J, et al. Eur Radiol. 2023 Apr;33(4):2699-2709. doi: 10.1007/s00330-022-09272-7. Epub 2022 Nov 25. Eur Radiol. 2023. PMID: 36434397

9

CT-based radiomics signature of visceral adipose tissue for prediction of disease progression in patients with Crohn's disease: A multicentre cohort study.

Li X, Zhang N, Hu C, Lin Y, Li J, Li Z, Cui E, Shi L, Zhuang X, Li J, Lu J, Wang Y, Liu R, Yuan C, Lin H, He J, Ke D, Tang S, Zou Y, He B, Sun C, Chen M, Huang B, Mao R, Feng ST. Li X, et al. EClinicalMedicine. 2022 Dec 30;56:101805. doi: 10.1016/j.eclinm.2022.101805. eCollection 2023 Feb. EClinicalMedicine. 2022. PMID: 36618894 Free PMC article.

10

Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning.

Li J, Qiu Z, Cao K, Deng L, Zhang W, Xie C, Yang S, Yue P, Zhong J, Lyu J, Huang X, Zhang K, Zou Y, Huang B. Li J, et al. Comput Methods Programs Biomed. 2023 May;233:107466. doi: 10.1016/j.cmpb.2023.107466. Epub 2023 Mar 5. Comput Methods Programs Biomed. 2023. PMID: 36907040

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