Protein Classifier for Thyroid Nodules Learned from Rapidly Acquired Proteotypes
Tony Kiat-Hon Lim,
Syed Muhammad Fahmy Syed Abdillah,
Sze Sing Lee,
Stan Z. Li,
Oi Lian Kon,
N. Gopalakrishna Iyer,
Posted 14 Apr 2020
medRxiv DOI: 10.1101/2020.04.09.20059741
Posted 14 Apr 2020
Up to 30% of thyroid nodules cannot be accurately classified as benign or malignant by cytopathology. Diagnostic accuracy can be improved by nucleic acid-based testing, yet a sizeable number of diagnostic thyroidectomies remains unavoidable. In order to develop a protein classifier for thyroid nodules, we analyzed the quantitative proteomes of 1,725 retrospective thyroid tissue samples from 578 patients using pressure-cycling technology and data-independent acquisition mass spectrometry. With artificial neural networks, a classifier of 14 proteins achieved over 93% accuracy in classifying malignant thyroid nodules. This classifier was validated in retrospective samples of 271 patients (91% accuracy), and prospective samples of 62 patients (88% accuracy) from four independent centers. These rapidly acquired proteotypes and artificial neural networks supported the establishment of an effective protein classifier for classifying thyroid nodules.
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