Multiparametric MRI-based machine learning models for preoperatively predicting rectal adenoma with canceration-Reference-Cited by-同舟云学术

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Multiparametric MRI-based machine learning models for preoperatively predicting rectal adenoma with canceration

Published:2021-03-01 Issue:5 Volume:34 Page:707-716
ISSN:0968-5243
Container-title:Magnetic Resonance Materials in Physics, Biology and Medicine
language:en
Short-container-title:Magn Reson Mater Phy

Author:

Li Panpan,Song Gesheng,Wu Rui,Li Houying,Zhang Ran,Zuo Panli,Li Aiyin

Funder

Shandong Science and Technology Development Plan, China

Shandong Medical and Health Science and Technology Development Plan, China

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology,Biophysics

Link

https://link.springer.com/content/pdf/10.1007/s10334-021-00915-2.pdf

Reference30 articles.

1. Ferlay J, Colombet M, Soerjomataram I et al (2019) Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer 144(8):1941–1953

2. Ferlay J, Soerjomataram I, Dikshit R et al (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136(5):E359–E386

3. Pasha HF, Radwan MI, Yehia AM (2019) Circulating methylated RUNX3 and SFRP1 genes as a noninvasive panel for early detection of colorectal cancer. Eur J Gastroenterol Hepatol 31(11):1342–1349

4. Wang X, Kuang YY (2014) Advances in epigenetic biomarker research in colorectal cancer. World J Gastroenterol 20(15):4276–4287

5. Xue VW, Cheung MT, Chan PT et al (2019) Non-invasive potential circulating mRNA markers for colorectal adenoma using targeted sequencing. Sci Rep 9(1):12943

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5. Intravoxel incoherent motion DWI with different mathematical models in predicting rectal adenoma with and without canceration;European Journal of Radiology;2022-10

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