AIMC Topic: Adenocarcinoma, Follicular
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Application of a machine learning algorithm to predict malignancy in thyroid cytopathology.
Cancer cytopathology
Feb 3, 2020
BACKGROUND: The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) comprises 6 categories used for the diagnosis of thyroid fine-needle aspiration biopsy (FNAB). Each category has an associated risk of malignancy, which is important in the ...
High-Resolution Raman Microscopic Detection of Follicular Thyroid Cancer Cells with Unsupervised Machine Learning.
The journal of physical chemistry. B
May 8, 2019
We use Raman microscopic images with high spatial and spectral resolution to investigate differences between human follicular thyroid (Nthy-ori 3-1) and follicular thyroid carcinoma (FTC-133) cells, a well-differentiated thyroid cancer. Through compa...
Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid.
Diagnostic cytopathology
Dec 20, 2017
BACKGROUND: To distinguish follicular adenoma (FA) and follicular carcinoma (FC) of thyroid in fine needle aspiration cytology (FNAC) is a challenging problem.
Thyroglobulin levels before radioactive iodine therapy and dynamic risk stratification after 1 year in patients with differentiated thyroid cancer.
Archives of endocrinology and metabolism
Dec 1, 2017
OBJECTIVES: We sought to assess the relationship between stimulated thyroglobulin (sTg) before radioactive iodine therapy (RIT), and the dynamic risk stratification 1 year after treatment, and to establish the utility of the sTg as a predictor of res...
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