Zhu H - Search Results

1

Prediction of pathological nodal stage of locally advanced rectal cancer by collective features of multiple lymph nodes in magnetic resonance images before and after neoadjuvant chemoradiotherapy.

Zhu H, Zhang X, Li X, Shi Y, Zhu H, Sun Y. Zhu H, et al. Chin J Cancer Res. 2019 Dec;31(6):984-992. doi: 10.21147/j.issn.1000-9604.2019.06.14. Chin J Cancer Res. 2019. PMID: 31949400 Free PMC article.

2

Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

Liu Z, Zhang XY, Shi YJ, Wang L, Zhu HT, Tang Z, Wang S, Li XT, Tian J, Sun YS. Liu Z, et al. Clin Cancer Res. 2017 Dec 1;23(23):7253-7262. doi: 10.1158/1078-0432.CCR-17-1038. Epub 2017 Sep 22. Clin Cancer Res. 2017. PMID: 28939744

3

Whole lesion histogram analysis of apparent diffusion coefficients on MRI predicts disease-free survival in locally advanced squamous cell cervical cancer after radical chemo-radiotherapy.

Zhao B, Cao K, Li XT, Zhu HT, Sun YS. Zhao B, et al. BMC Cancer. 2019 Nov 15;19(1):1115. doi: 10.1186/s12885-019-6344-3. BMC Cancer. 2019. PMID: 31729974 Free PMC article.

4

Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method.

Qu YH, Zhu HT, Cao K, Li XT, Ye M, Sun YS. Qu YH, et al. Thorac Cancer. 2020 Mar;11(3):651-658. doi: 10.1111/1759-7714.13309. Epub 2020 Jan 16. Thorac Cancer. 2020. PMID: 31944571 Free PMC article.

5

Predicting Rectal Cancer Response to Neoadjuvant Chemoradiotherapy Using Deep Learning of Diffusion Kurtosis MRI.

Zhang XY, Wang L, Zhu HT, Li ZW, Ye M, Li XT, Shi YJ, Zhu HC, Sun YS. Zhang XY, et al. Radiology. 2020 Jul;296(1):56-64. doi: 10.1148/radiol.2020190936. Epub 2020 Apr 21. Radiology. 2020. PMID: 32315264

6

Radiomics Analysis Based on Diffusion Kurtosis Imaging and T2 Weighted Imaging for Differentiation of Pancreatic Neuroendocrine Tumors From Solid Pseudopapillary Tumors.

Shi YJ, Zhu HT, Liu YL, Wei YY, Qin XB, Zhang XY, Li XT, Sun YS. Shi YJ, et al. Front Oncol. 2020 Aug 21;10:1624. doi: 10.3389/fonc.2020.01624. eCollection 2020. Front Oncol. 2020. PMID: 32974201 Free PMC article.

7

Pilot Study of CT-Based Radiomics Model for Early Evaluation of Response to Immunotherapy in Patients With Metastatic Melanoma.

Wang ZL, Mao LL, Zhou ZG, Si L, Zhu HT, Chen X, Zhou MJ, Sun YS, Guo J. Wang ZL, et al. Front Oncol. 2020 Aug 25;10:1524. doi: 10.3389/fonc.2020.01524. eCollection 2020. Front Oncol. 2020. PMID: 32984000 Free PMC article.

8

Use of Radiomics to Predict Response to Immunotherapy of Malignant Tumors of the Digestive System.

Ji Z, Cui Y, Peng Z, Gong J, Zhu HT, Zhang X, Li J, Lu M, Lu Z, Shen L, Sun YS. Ji Z, et al. Med Sci Monit. 2020 Oct 20;26:e924671. doi: 10.12659/MSM.924671. Med Sci Monit. 2020. PMID: 33077705 Free PMC article.

9

A Deep Learning Model to Predict the Response to Neoadjuvant Chemoradiotherapy by the Pretreatment Apparent Diffusion Coefficient Images of Locally Advanced Rectal Cancer.

Zhu HT, Zhang XY, Shi YJ, Li XT, Sun YS. Zhu HT, et al. Front Oncol. 2020 Oct 29;10:574337. doi: 10.3389/fonc.2020.574337. eCollection 2020. Front Oncol. 2020. PMID: 33194680 Free PMC article.

10

Predicting Lymph Node Metastasis Using Computed Tomography Radiomics Analysis in Patients With Resectable Esophageal Squamous Cell Carcinoma.

Zhao B, Zhu HT, Li XT, Shi YJ, Cao K, Sun YS. Zhao B, et al. J Comput Assist Tomogr. 2021 Mar-Apr 01;45(2):323-329. doi: 10.1097/RCT.0000000000001125. J Comput Assist Tomogr. 2021. PMID: 33512851

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