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Xiaonan Liu | OMICS International
ISSN:2167-7964

OMICS Journal of Radiology
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Modeling of Acute Rectal Toxicity to Compare Two Patient Positioning Methods for Prostate Cancer Radiotherapy: Can Toxicity Modeling be Used for Quality Assurance?
Potential Application of Radiomics for Differentiating Solitary Pulmonary Nodules

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OMICS Journal of Radiology received 551 citations as per Google Scholar report

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Xiaonan Liu

School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe Arizona, USA

Biography

Experienced in machine learning and data mining algorithm, especially solving the application problems in healthcare and manufacturing. Collaboration with other people with different backgrounds. Excellent skill in statistics, machine learning algorithm using statistical software. Willing and passionate to solve unknown problems with teammates.
Publications

Patient Specific Characteristics Are an Important Factor That Determines the Risk of Acute Grade ≥ 2 Rectal Toxicity in Patients Treated for Prostate Cancer with IMRT and Daily Image Guidance Based on Implanted Gold Markers

Aim: To model acute rectal toxicity in Intensity Modulated Radiation Therapy (IMRT) for prostate cancer using dosimetry and patient specific characteristics. Methods: A database of 79 prostate cancer patients treated with image guided IMRT was used to fit parameters of Lyman-Kutcher-Burman (LKB) and logistic regression Normal Tissue Complications ... Read More»

Xiaonan Liu, Jing Li, Teresa Wu, Steven E Schild, Michael H Schild, William Wong, Sujay Vora and Mirek Fatyga

Research Article: OMICS J Radiol 2016, 5: 225

DOI: 10.4172/2167-7964.1000225

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