Publications by authors named "Sarah Yuqi Zhu"

Grading of oral epithelial dysplasia (OED) can be challenging with considerable intraobserver and interobserver variability. Abnormal immunohistochemical staining patterns of the tumor suppressor protein, p53, have been recently shown to be potentially associated with progression in OED. We retrospectively identified 214 oral biopsies from 203 patients recruited in a longitudinal study between 2001 and 2008 with a diagnosis of reactive, nondysplastic lesions, low-grade lesions (mild OED and moderate OED) and high-grade lesions (HGLs; severe OED/carcinoma in situ).

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Background: Early-stage oral squamous cell carcinoma (OSCC) patients have a one-in-four risk of regional metastasis (LN+), which is also the most significant prognostic factor for survival. As there are no validated biomarkers for predicting LN+ in early-stage OSCC, elective neck dissection often leads to over-treatment and under-treatment. We present a machine-learning-based model using the quantitative nuclear phenotype of cancer cells from the primary tumor to predict the risk of nodal disease.

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Neck lymph node metastasis (LN+) is one of the most significant prognostic factors affecting 1-in-2 patients diagnosed with oral squamous cell carcinoma (OSCC). The different LN outcomes between clinico-pathologically similar primary tumors suggest underlying molecular signatures that could be associated with the risk of nodal disease development. MicroRNAs (miRNAs)are short non-coding molecules that regulate the expression of their target genes to maintain the balance of cellular processes.

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