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Editor's e-Note
With the annual ASTRO meeting running from September 15 to 18 this month, we’re serving up some news from the world of oncology. There are many interesting developments to report, and we’re highlighting a few that caught our attention. Please let us know on Twitter and/or Facebook if there are particular stories that you’d like us to cover.

— Dave Yeager, editor
e-News Exclusive
Oncology News

Machine Learning Predicts Thyroid Nodules

According to an ahead-of-print article to be published in the December 2019 issue of the American Journal of Roentgenology, researchers have validated a first-of-its-kind machine learning-based model to evaluate immunohistochemical (IHC) characteristics in patients with suspected thyroid nodules, achieving “excellent performance” for individualized noninvasive prediction of the presence of cytokeratin 19, galectin 3, and thyroperoxidase, based on CT images.

“When IHC information is hidden on CT images,” principal investigator Jiabing Gu explains, “it may be possible to discern the relation between this information and radiomics by use of texture analysis.”

To assess whether texture analysis could be utilized to predict IHC characteristics of suspected thyroid nodules, Gu and colleagues from China’s University of Jinan enrolled 103 patients—with a training cohort-to-validation cohort ratio of approximately 3:1—with suspected thyroid nodules who had undergone thyroidectomy and IHC analysis from January 2013 to January 2016. All 103 patients—28 men and 75 women with a median age of 58 years and a range of 33 to 70 years—underwent CT before surgery, and 3D Slicer v 4.8.1 was used to analyze images of the surgical specimen.

To facilitate test-retest methods, 20 patients were imaged in two sets of CT series within 10 to 15 minutes, using the same scanner and protocols, without contrast administration. These images were used only to select reproducible and nonredundant features, not to establish or verify the radiomic model.

Full story »
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