Sun Y - Search Results

1

Extramural depth of rectal cancer tumor invasion at thin-section MRI: predicting treatment response to neoadjuvant chemoradiation.

Tong T, Sun Y, Cai S, Zhang Z, Gu Y. Tong T, et al. Among authors: sun y. Oncotarget. 2015 Oct 6;6(30):30277-86. doi: 10.18632/oncotarget.4623. Oncotarget. 2015. PMID: 26309163 Free PMC article.

2

[Prediction of efficacy of neoadjuvant radiochemotherapy based on depth of invasion in T3 rectal cancer].

Shen L, Tong T, Zhang H, Sun Y, Zhu J, Cai G, Li G, Liang L, Cai X, Fan M, Huang J, Yang L, Sun W, Cai S, Zhang Z. Shen L, et al. Among authors: sun w, sun y. Zhonghua Wei Chang Wai Ke Za Zhi. 2014 Jun;17(6):551-5. Zhonghua Wei Chang Wai Ke Za Zhi. 2014. PMID: 24953360 Chinese.

3

Apparent Diffusion Coefficient (ADC) value: a potential imaging biomarker that reflects the biological features of rectal cancer.

Sun Y, Tong T, Cai S, Bi R, Xin C, Gu Y. Sun Y, et al. PLoS One. 2014 Oct 10;9(10):e109371. doi: 10.1371/journal.pone.0109371. eCollection 2014. PLoS One. 2014. PMID: 25303288 Free PMC article.

4

Dynamic contrast-enhanced MRI: Use in predicting pathological complete response to neoadjuvant chemoradiation in locally advanced rectal cancer.

Tong T, Sun Y, Gollub MJ, Peng W, Cai S, Zhang Z, Gu Y. Tong T, et al. Among authors: sun y. J Magn Reson Imaging. 2015 Sep;42(3):673-80. doi: 10.1002/jmri.24835. Epub 2015 Feb 5. J Magn Reson Imaging. 2015. PMID: 25652254

5

T3 subclassification using the EMD/mesorectum ratio predicts neoadjuvant chemoradiation outcome in T3 rectal cancer patients.

Shen L, Sun Y, Zhang H, Zhang J, Deng W, Wang Y, Yao Y, Yang L, Zhu J, Tong T, Liang L, Zhang Z. Shen L, et al. Among authors: sun y. Br J Radiol. 2018 Jan;91(1081):20170617. doi: 10.1259/bjr.20170617. Epub 2017 Nov 21. Br J Radiol. 2018. PMID: 29072488 Free PMC article.

6

The value of diffusion kurtosis imaging in assessing pathological complete response to neoadjuvant chemoradiation therapy in rectal cancer: a comparison with conventional diffusion-weighted imaging.

Hu F, Tang W, Sun Y, Wan D, Cai S, Zhang Z, Grimm R, Yan X, Fu C, Tong T, Peng W. Hu F, et al. Among authors: sun y. Oncotarget. 2017 Apr 27;8(43):75597-75606. doi: 10.18632/oncotarget.17491. eCollection 2017 Sep 26. Oncotarget. 2017. PMID: 29088894 Free PMC article.

7

Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings.

Sun Y, Hu P, Wang J, Shen L, Xia F, Qing G, Hu W, Zhang Z, Xin C, Peng W, Tong T, Gu Y. Sun Y, et al. J Magn Reson Imaging. 2018 Feb 13. doi: 10.1002/jmri.25969. Online ahead of print. J Magn Reson Imaging. 2018. PMID: 29437279

8

Technical Note: A deep learning-based autosegmentation of rectal tumors in MR images.

Wang J, Lu J, Qin G, Shen L, Sun Y, Ying H, Zhang Z, Hu W. Wang J, et al. Among authors: sun y. Med Phys. 2018 Jun;45(6):2560-2564. doi: 10.1002/mp.12918. Epub 2018 May 3. Med Phys. 2018. PMID: 29663417

9

Predictive value of MRI-detected extramural vascular invasion in stage T3 rectal cancer patients before neoadjuvant chemoradiation.

Sun Y, Li J, Shen L, Wang X, Tong T, Gu Y. Sun Y, et al. Diagn Interv Radiol. 2018 May-Jun;24(3):128-134. doi: 10.5152/dir.2018.17286. Diagn Interv Radiol. 2018. PMID: 29770764 Free PMC article.

10

Diffusion kurtosis imaging in the characterisation of rectal cancer: utilizing the most repeatable region-of-interest strategy for diffusion parameters on a 3T scanner.

Sun Y, Xiao Q, Hu F, Fu C, Jia H, Yan X, Xin C, Cai S, Peng W, Wang X, Tong T, Gu Y. Sun Y, et al. Eur Radiol. 2018 Dec;28(12):5211-5220. doi: 10.1007/s00330-018-5495-y. Epub 2018 May 24. Eur Radiol. 2018. PMID: 29797056

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