Radiomics in predicting mutation status for thyroid cancer: A preliminary study using radiomics features for predicting BRAFV600E mutations in papillary thyroid carcinoma

Jung Hyun Yoon, Kyunghwa Han, Eunjung Lee, Jandee Lee, Eun-Kyung Kim, Hee Jung Moon, Vivian Youngjean Park, Kee Hyun Nam and Jin Young Kwak

PLOS ONE, 2020, vol. 15, issue 2, 1-11

Abstract: Purpose: To evaluate whether if ultrasonography (US)-based radiomics enables prediction of the presence of BRAFV600E mutations among patients diagnosed as papillary thyroid carcninoma (PTC). Methods: From December 2015 to May 2017, 527 patients who had been treated surgically for PTC were included (training: 387, validation: 140). All patients had BRAFV600E mutation analysis performed on surgical specimen. Feature extraction was performed using preoperative US images of the 527 patients (mean size of PTC: 16.4mm±7.9, range, 10–85 mm). A Radiomics Score was generated by using the least absolute shrinkage and selection operator (LASSO) regression model. Univariable/multivariable logistic regression analysis was performed to evaluate the factors including Radiomics Score in predicting BRAFV600E mutation. Subgroup analysis including conventional PTC

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0228968

DOI: 10.1371/journal.pone.0228968

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